| title: “Data_Products” |
| author: “aid004” |
| date: “2022-11-19” |
| output: html_document |
| toc: true |
| toc_float:true |
Let’s look at some boxplots
data("ToothGrowth")
Let’s change the dose to a factor and look at the top of the dataframe
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
head(ToothGrowth, 4)
## len supp dose
## 1 4.2 VC 0.5
## 2 11.5 VC 0.5
## 3 7.3 VC 0.5
## 4 5.8 VC 0.5
Let’s load ggplot
library(ggplot2)
Let’s set the theme for our plots to classic
theme_set(
theme_bw() +
theme(legend.position = "top")
)
Let’s start with a very basic boxplot with dose vs. length
tg <- ggplot(ToothGrowth, aes(x = dose, y = len))
tg + geom_boxplot()
Now let’s look at a boxplot with points for the mean
tg + geom_boxplot(notch = TRUE, fill = "lightgrey") +
stat_summary(fun = mean, geom = "point", shape = 9, size = 2.5, color = "indianred")
We can also change the scale number of variables included, and their order
tg + geom_boxplot() +
scale_x_discrete(limits = c("0.5", "2"))
## Warning: Removed 20 rows containing missing values (stat_boxplot).
Let’s put our x axis in descending order
tg +geom_boxplot() +
scale_x_discrete(limits = c("2", "1", "0.5"))
We can also change boxplot colors by groups
tg + geom_boxplot(aes(color = dose)) +
scale_color_manual(values = c("indianred", "blue1", "green2"))
What if we want to display our data subset by oj vs vitamin c?
tg2 <- tg + geom_boxplot(aes(fill = supp), position = position_dodge(0.9)) +
scale_fill_manual(values = c("#999999", "#E69F00"))
tg2
We can also arrange this as two plots with facet_wrap
tg2 + facet_wrap(~supp)
Now let’s take a look at some barplots.
We’ll start with making a dataframe based on the tooth data.
df <- data.frame(dose = c("D0.5", "D1", "D2"),
len = c(4.2, 10, 29.5))
df
## dose len
## 1 D0.5 4.2
## 2 D1 10.0
## 3 D2 29.5
And now let’s make a second dataframe
df2 <- data.frame(supp=rep(c("VC", "OJ"), each = 3),
dose = rep(c("D0.5", "D1", "D2"), 2),
len = c(6.8, 15, 33, 4.2, 10, 29.5))
df2
## supp dose len
## 1 VC D0.5 6.8
## 2 VC D1 15.0
## 3 VC D2 33.0
## 4 OJ D0.5 4.2
## 5 OJ D1 10.0
## 6 OJ D2 29.5
Let’s load up ggplot2
library(ggplot2)
Let’s set our parameters for ggplot
theme_set(
theme_classic() +
theme(legend.position = "top")
)
Let’s start with some basic barplots using the tooth data
f <-ggplot(df, aes(x = dose, y = len))
f + geom_col()
Now let’s change the fill, and add labels to the top
f + geom_col(fill = "darkblue") +
geom_text(aes(label = len), vjust = -0.3)
Now let’s add the labels inside the bars
f + geom_col(fill = "darkblue") +
geom_text(aes(label = len), vjust = 1.6, color = "white")
Now let’s change the barplot colors by group
f + geom_col(aes(color = dose), fill = "white") +
scale_color_manual(values = c("blue", "gold", "red"))
This is kinda hard to see, so let’s change the fill.
f + geom_col(aes(fill = dose)) +
scale_fill_manual(values = c("blue", "gold", "red"))
Ok how do we do this with multiple groups
ggplot(df2, aes(x = dose, y =len)) +
geom_col(aes(color = supp, fill = supp), position = position_stack()) +
scale_color_manual(values =c("blue", "gold")) +
scale_fill_manual(values = c("blue", "gold"))
p <- ggplot(df2, aes(x = dose, y = len)) +
geom_col(aes(color = supp, fill = supp), position = position_dodge(0.8), width = 0.7) +
scale_color_manual(values = c("blue", "gold")) +
scale_fill_manual(values = c("blue", "gold"))
p
Now let’s add those labels to the dodged barplot
p + geom_text(
aes(label = len, group = supp),
position = position_dodge(0.8),
vjust = -0.3, size = 3.5
)
Now what if we want to add labels to our stacked barplots? For this we need dplyr
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
df2 <- df2 %>%
group_by(dose) %>%
arrange(dose, desc(supp)) %>%
dplyr::mutate(lab_ypos = cumsum(len) - 0.5 * len)
df2
## # A tibble: 6 × 4
## # Groups: dose [3]
## supp dose len lab_ypos
## <chr> <chr> <dbl> <dbl>
## 1 VC D0.5 6.8 3.4
## 2 OJ D0.5 4.2 8.9
## 3 VC D1 15 7.5
## 4 OJ D1 10 20
## 5 VC D2 33 16.5
## 6 OJ D2 29.5 47.8
Now let’s recreate our stacked graphs
ggplot(df2, aes(x=dose, y = len)) +
geom_col(aes(fill = supp), width = 0.7) +
geom_text(aes(y = lab_ypos, label = len, group = supp), color = "white") +
scale_color_manual(values = c("blue", "gold")) +
scale_fill_manual(values = c("blue", "gold"))
Let’s take a look at histograms. First, let’s load our data.
set.seed(1234)
wdata = data.frame(
sex = factor(rep(c("F", "M"), each = 200)),
weight = c(rnorm(200, 50), rnorm(200, 58))
)
head(wdata, 4)
## sex weight
## 1 F 48.79293
## 2 F 50.27743
## 3 F 51.08444
## 4 F 47.65430
Now let’s load dplyr
library(dplyr)
mu <- wdata %>%
group_by(sex) %>%
summarise(grp.mean = mean(weight))
Now let’s load the plotting package
library(ggplot2)
theme_set(
theme_classic() +
theme(legend.position = "bottom")
)
Now let’s create a ggplot object
a <- ggplot(wdata, aes(x = weight))
a + geom_histogram(bins = 30, color = "black", fill = "grey") +
geom_vline(aes(xintercept = mean(weight)),
linetype = "dashed", size = 0.6)
Now let’s change the color by group
a + geom_histogram(aes(color = sex), fill = "white", position = "identity") +
scale_color_manual(values = c("#00AFBB", "#E7B800"))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
a + geom_histogram(aes(color = sex, fill = sex), position = "identity") +
scale_color_manual(values = c("#00AFBB", "#E7B800")) +
scale_fill_manual(values = c("indianred", "lightblue1"))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
What if we want to combine density plots and histograms?
a + geom_histogram(aes(y = stat(density)),
color = "black", fill = "white") +
geom_density(alpha = 0.2, fill = "#FF6666")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
a + geom_histogram(aes(y = stat(density), color = sex),
fill = "white", position = "identity") +
geom_density(aes(color = sex), size = 1) +
scale_color_manual(values = c("indianred", "lightblue1"))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
First let’s load the required packages
library(ggplot2)
Let’s set our theme
theme_set(
theme_dark() +
theme(legend.position = "top")
)
First let’s initiate a ggplot object called TG
data("ToothGrowth")
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
tg <- ggplot(ToothGrowth, aes(x=dose, y = len))
Let’s create a dotplot with a summary statistic
tg + geom_dotplot(binaxis = "y", stackdir = "center", fill = "white") +
stat_summary(fun = mean, fun.args = list(mult=1))
## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.
## Warning: Removed 3 rows containing missing values (geom_segment).
Let’s add a boxplt and dotplot together
tg + geom_boxplot(width = 0.5) +
geom_dotplot(binaxis = "y", stackdir = "center", fill = "white")
## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.
tg + geom_violin(trim = FALSE) +
geom_dotplot(binaxis = "y", stackdir = "center", fill = "#999999") +
stat_summary(fun = mean, fun.args = list(mult=1))
## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.
## Warning: Removed 3 rows containing missing values (geom_segment).
Let’s create a dotplot with multiple groups
tg + geom_boxplot(width = 0.5) +
geom_dotplot(aes(fill = supp), binaxis = "y", stackdir = "center") +
scale_fill_manual(values = c("indianred", "lightblue1"))
## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.
tg + geom_boxplot(aes(color = supp), width = 0.5, position = position_dodge(0.8)) +
geom_dotplot(aes(fill = supp, color = supp), binaxis = 'y', stackdir = 'center',
dotsize = 0.8, position = position_dodge(0.8)) +
scale_fill_manual(values = c("#00AFBB", "#E7B800")) +
scale_color_manual(values = c("#00AFBB", "#E7B800"))
## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.
Now let’s change it up and look at some line plots
We’ll start by making a custom dataframe kinda like the tooth dataset. This way we can see the lines and stuff that we’re modifying
df <- data.frame(dose = c("D0.5", "D1", "D2"),
len = c(4.2, 10, 29.55))
Now let’s create a second dataframe for plotting by groups
df2 <- data.frame(supp = rep(c("VC", "OJ"), each = 3),
dose = rep(c("D0.5", "D1", "D2"), 2),
len = c(6.8, 15, 33, 4.2, 10, 29.5))
df2
## supp dose len
## 1 VC D0.5 6.8
## 2 VC D1 15.0
## 3 VC D2 33.0
## 4 OJ D0.5 4.2
## 5 OJ D1 10.0
## 6 OJ D2 29.5
Now let’s again load ggplot2 and set a theme
library(ggplot2)
theme_set(
theme_gray() +
theme(legend.position = "right")
)
Now let’s do some basic line plots. First, we will build a function to display all the different line types
generateRLineTypes <- function(){
oldPar <- par ()
par(font = 2, mar = c(0, 0, 0, 0))
plot(1, pch = "", ylim = c(0, 6), xlim = c(0, 0.7), axes = FALSE, xlab = "", ylab = "")
for(i in 0:6) lines(c(0.3, 0.7), c(i, i), lty = i, lwd = 3)
text(rep(0.1, 6), 0:6, labels = c("0. 'Blank'", "1. 'solid'", "2. 'dashed'", "3. 'dotted'",
"4. 'dotdash'", "5. 'longdash'", "6. 'twodash'"))
par(mar = oldPar$mar, font = oldPar$font)
}
generateRLineTypes()
Now let’s build a basic line plot
p <- ggplot(data = df, aes(x = dose, y = len, group = 1))
p + geom_line() + geom_point()
Now let’s modify the line type and color
p + geom_line(linetype = "dashed", color = "steelblue") +
geom_point(color = "steelblue")
Now let’s try a step graph, which indicates a threshold type progression
p +geom_step() + geom_point()
Now let’s move on to making multiple groups. First we’ll create our ggplot object
p <- ggplot(df2, aes(x=dose, y=len, group = supp))
Now let’s change line types and point shapes by group
p + geom_line(aes(linetype = supp, color = supp)) +
geom_point(aes(shape = supp, color = supp)) +
scale_color_manual(values = c("red", "blue"))
Now let’s look at line plots with a numeric x axis
df3 <- data.frame(supp = rep(c("VC", "OJ"), each = 3),
dose = rep(c("0.5", "1", "2"), 2),
len=c(6.8, 15, 33, 4.2, 10, 29.5))
df3
## supp dose len
## 1 VC 0.5 6.8
## 2 VC 1 15.0
## 3 VC 2 33.0
## 4 OJ 0.5 4.2
## 5 OJ 1 10.0
## 6 OJ 2 29.5
Now let’s plot where both axes are treated as continuous labels
df3$dose <- as.numeric(as.vector(df3$dose))
ggplot(data = df3, aes(x=dose, y=len, group = supp, color = supp)) +
geom_line() + geom_point()
Now let’s look at a line graph with having the x axis as dates. We’ll use the built in aconomics time for this example.
head(economics)
## # A tibble: 6 × 6
## date pce pop psavert uempmed unemploy
## <date> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1967-07-01 507. 198712 12.6 4.5 2944
## 2 1967-08-01 510. 198911 12.6 4.7 2945
## 3 1967-09-01 516. 199113 11.9 4.6 2958
## 4 1967-10-01 512. 199311 12.9 4.9 3143
## 5 1967-11-01 517. 199498 12.8 4.7 3066
## 6 1967-12-01 525. 199657 11.8 4.8 3018
ggplot(data = economics, aes(x = date, y = pop)) +
geom_line()
Now let’s subset the data
ss <- subset(economics, date > as.Date("2006-1-1"))
ggplot(data = ss, aes(x = date, y = pop)) + geom_line()
We can also change the line size, for instance by another variable like unemployment
ggplot(data = economics, aes(x=date, y = pop)) +
geom_line(aes(size = unemploy/pop))
We can also plot multiple time-series data
ggplot(economics, aes(x = date)) +
geom_line(aes(y=psavert), color = "darkred") +
geom_line(aes(y = uempmed), color = "steelblue", linetype = "twodash")
Lastly, let’s make this into an area plot
ggplot(economics, aes(x=date)) +
geom_area(aes(y = psavert), fill = "#999999",
color = "#999999", alpha = 0.5) +
geom_area(aes(y = uempmed), fill = "#E69F00",
color = "#E69F00", alpha = 0.5)
First, let’s load the required packages
library(ggplot2)
library(ggridges)
#BiocManager::install("ggridges")
Now, let’s load some sample data
?airquality
air <- ggplot(airquality) + aes(Temp, Month, group = Month) + geom_density_ridges()
air
## Picking joint bandwidth of 2.65
Now, let’s add some pazzazz to our graph
library(viridis)
## Loading required package: viridisLite
library(viridisLite)
ggplot(airquality) + aes(Temp, Month, group = Month, fill = ..x..) +
geom_density_ridges_gradient() +
scale_fill_viridis(option = "C", name = "Temp")
## Picking joint bandwidth of 2.65
Lat thing we will do is create a facet plot for all our data.
library(tidyr)
airquality %>%
gather(key = "Measurement", value = "Value", Ozone, Solar.R, Wind, Temp) %>%
ggplot() + aes(Value, Month, group = Month) +
geom_density_ridges() +
facet_wrap(~ Measurement, scales = "free")
## Picking joint bandwidth of 11
## Picking joint bandwidth of 40.1
## Picking joint bandwidth of 2.65
## Picking joint bandwidth of 1.44
## Warning: Removed 44 rows containing non-finite values (stat_density_ridges).
A density plot is a nice alternative to a histogram
set.seed(1234)
wdata = data.frame(
sex = factor(rep(c("F", "M"), each = 200)),
weight = c(rnorm(200, 55), rnorm(200, 58))
)
Adding the library and group data
library(dplyr)
mu <- wdata %>%
group_by(sex) %>%
dplyr::summarize(grp.mean = mean(weight))
Next, we load the graphing packages
library(ggplot2)
theme_set(
theme_classic() +
theme(legend.position = "right")
)
Now, let’s do the basic plot function. First, we will create a ggplot object.
d <- ggplot(wdata, aes(x = weight))
Now, let’s do a basic density plot
d + geom_density() +
geom_vline(aes(xintercept = mean(weight)), linetype = "dashed")
Now, let’s change the y axis to count instead of density
d + geom_density(aes(y = stat(count)), fill = "lightgray") +
geom_vline(aes(xintercept = mean(weight)), linetype = "dashed")
Let’s make it easier to read by adding color.
d + geom_density(aes(color = sex)) +
scale_color_manual(values = c("darkgray", "gold"))
Lastly, let’s fill the density plots
d + geom_density(aes(fill = sex), alpha = 0.4) +
geom_vline(aes(xintercept = grp.mean, color = sex), data = mu, linetype = "dashed") +
scale_color_manual(values = c("grey", "gold")) +
scale_fill_manual(values = c("grey", "gold"))
First, let’s load our required package
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
Let’s start with a scatter plot of the Orange dataset
Orange <- as.data.frame(Orange)
plot_ly(data = Orange, x = ~age, y = ~circumference)
## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter
## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode
Now, let’s add some more info
plot_ly(data = Orange, x = ~age, y = ~circumference,
color = ~Tree, size = ~age,
text = ~paste("Tree ID:", Tree, "<br>Age:", age, "Circ:", circumference)
)
## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter
## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
Now, let’s create a random distribution and add it to our dataframe
trace_1 <- rnorm(35, mean= 120, sd = 10)
new_data <- data.frame(Orange, trace_1)
We’ll use the random numbers as lines on the graph
plot_ly(data = new_data, x = ~age, y = ~circumference, color = ~Tree, size = ~age,
text = ~paste("Tree ID:", Tree, "<br>Age:", age, "<br>Circ:", circumference)) %>%
add_trace(y = ~trace_1, mode = 'lines') %>%
add_trace(y = ~circumference, mode = 'markers')
## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter
## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
Now, let’s create a graph with the option of showing as a scatter or line, and add labels.
plot_ly(data = Orange, x = ~age, y = ~circumference,
color = ~Tree, size = ~circumference,
text = ~paste("Tree ID:", Tree, "<br>Age:", age, "Circ:", circumference)) %>%
add_trace(y = ~circumference, mode = 'markers') %>%
layout(
title = "Plot of Orange data with switchable trace",
updatemenus= list(
list(
type = 'downdrop',
y = 0.8,
buttons = list(
list(method = 'restyle',
args = list('mode', 'markers'),
label = "Marker"
),
list(method = "restyle",
args = list("mode", 'lines'),
label = "Lines"
)
)
)
)
)
## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
First, let’s load our required packages
library(plotly)
Now, let’s create a random 3D matrix
d <- data.frame(
x <- seq(1,10, by = 0.5),
y <- seq(1,10, by = 0.5)
)
z <- matrix(rnorm(length(d$x) * length(d$y)), nrow = length(d$x), ncol = length(d$y))
Now, let’s plot our 3D data
plot_ly(d, x = ~x, y = ~y, z = ~z) %>%
add_surface()
Let’s add some more aspects to it, such as topography
plot_ly(d, x = ~x, y = ~y, z = ~z) %>%
add_surface(
contours = list(
z = list(
show = TRUE,
usecolormap = TRUE,
highlightcolor = "FF00000",
project = list(z = TRUE)
)
)
)
Now, let’s look at a 3D scatter plot
plot_ly(longley, x = ~GNP, y = ~Population, z = ~Employed, marker = list(color = ~GNP)) %>%
add_markers()
First, let’s load our required libraries
library(ggplot2)
library(dplyr)
BiocManager::install("plotrix")
## 'getOption("repos")' replaces Bioconductor standard repositories, see
## '?repositories' for details
##
## replacement repositories:
## CRAN: https://cloud.r-project.org
## Bioconductor version 3.15 (BiocManager 1.30.18), R 4.2.1 (2022-06-23)
## Warning: package(s) not installed when version(s) same as current; use `force = TRUE` to
## re-install: 'plotrix'
## Installation paths not writeable, unable to update packages
## path: /usr/lib/R/library
## packages:
## foreign, MASS, Matrix, mgcv, nlme, nnet, rpart, spatial
## Old packages: 'BiocManager', 'bit', 'brew', 'bslib', 'callr', 'car', 'cli',
## 'commonmark', 'cpp11', 'crayon', 'curl', 'data.table', 'devtools', 'digest',
## 'DT', 'evaluate', 'fontawesome', 'gert', 'ggforce', 'ggplot2', 'ggpubr',
## 'ggraph', 'ggrepel', 'ggridges', 'ggsignif', 'ggstance', 'graphlayouts',
## 'igraph', 'isoband', 'jsonlite', 'knitr', 'labelled', 'lifecycle', 'limma',
## 'lme4', 'lubridate', 'maps', 'maptools', 'markdown', 'MatrixModels',
## 'matrixStats', 'minqa', 'modelr', 'mosaic', 'mosaicCore', 'openssl',
## 'pkgload', 'plotly', 'plyr', 'polyclip', 'processx', 'ps', 'purrr', 'ragg',
## 'raster', 'RcppArmadillo', 'RcppEigen', 'RCurl', 'readr', 'rlang',
## 'rmarkdown', 'RMySQL', 'roxygen2', 'rsconnect', 'RSQLite', 'rstatix', 'sass',
## 'shiny', 'sp', 'sys', 'terra', 'testthat', 'tidyselect', 'tinytex', 'tm',
## 'tokenizers', 'vctrs', 'vroom', 'widyr', 'xfun', 'XML', 'xts', 'yaml', 'zip',
## 'zoo'
library(plotrix)
theme_set(
theme_classic()+
theme(legend.position = "top")
)
Let’s again use the tooth data for this exercise
df <- ToothGrowth
df$dose <- as.factor(df$dose)
Now, let’s use dplyr for manipulation purposes
df.summary <- df %>%
group_by(dose) %>%
dplyr::summarize(
sd = sd(len, na.rm = TRUE),
stderr = std.error(len, na.rm = TRUE),
len = mean(len),
)
df.summary
## # A tibble: 3 × 4
## dose sd stderr len
## <fct> <dbl> <dbl> <dbl>
## 1 0.5 4.50 1.01 10.6
## 2 1 4.42 0.987 19.7
## 3 2 3.77 0.844 26.1
Let’s now look at some key functions
Let’s start by creating a ggplot object
tg <- ggplot(
df.summary,
aes(x = dose, y = len, ymin = len - sd, ymax = len + sd)
)
Now, let’s look at the most basic error bars
tg + geom_pointrange()
tg + geom_errorbar(width = 0.2) +
geom_point(size = 1.5)
Now, let’s create horizontal error bars by manipulating our graph
ggplot(df.summary, aes( x = len, y = dose, xmin = len - sd, xmax = len + sd)) +
geom_point() +
geom_errorbarh(height = 0.2)
This just gives you an idea of error bars on the horizontal axis
Now, let’s look at adding jitter points (actual measurements) to our data.
ggplot(df, aes(dose, len)) +
geom_jitter(psoition = position_jitter(0.2), color = "darkgray") +
geom_pointrange(aes(ymin = len - sd, ymax = len + sd), data = df.summary)
## Warning: Ignoring unknown parameters: psoition
Now, let’s try error bars on a violin plot
ggplot(df, aes(dose, len)) +
geom_violin(color = "darkgrey", trim = FALSE) +
geom_pointrange(aes(ymin = len - sd, ymax = len+sd), data = df.summary)
Now, how about with a line graph?
ggplot(df.summary, aes(dose, len)) +
geom_line(aes(group = 1)) + #always specify this when you have 1 line)
geom_errorbar(aes(ymin = len - stderr, ymax = len + stderr), width = 0.2) +
geom_point(size = 2)
Now, let’s make a bar graph with the halve error bars
ggplot(df.summary, aes(dose, len)) +
geom_col(fill = "lightgray", color = "black") +
geom_errorbar(aes(ymin = len, ymax = len+stderr), width = 0.2)
You can see that by not specifying wmin = len-stderr, we have in essence cut our error bar in half.
How about we add jitter points to the line plots? We need to use the original dataframe for the jitter plot, and the summary df for the geom layers.
ggplot(df, aes(dose, len)) +
geom_jitter(position = position_jitter(0.2), color = "darkgrey") +
geom_line(aes(group = 1), data = df.summary) +
geom_errorbar(
aes(ymin = len - stderr, ymax = len + stderr),
data = df.summary, width = 0.2) +
geom_point(data = df.summary, size = 0.2)
What about adding jitterpoints to a barplot?
ggplot(df, aes(dose, len)) +
geom_col(data = df.summary, fill = NA, color = "black") +
geom_jitter(position = position_jitter(0.3), color = "blue") +
geom_errorbar(aes(ymin = len - stderr, ymax = len + stderr),
data = df.summary, width = 0.2)
What if we wanted to have our error bars per group? (OJ vs VC)
df.summary2 <- df %>%
group_by(dose, supp) %>%
dplyr::summarize(
sd = sd(len),
stderr = std.error(len),
len = mean(len)
)
## `summarise()` has grouped output by 'dose'. You can override using the
## `.groups` argument.
df.summary2
## # A tibble: 6 × 5
## # Groups: dose [3]
## dose supp sd stderr len
## <fct> <fct> <dbl> <dbl> <dbl>
## 1 0.5 OJ 4.46 1.41 13.2
## 2 0.5 VC 2.75 0.869 7.98
## 3 1 OJ 3.91 1.24 22.7
## 4 1 VC 2.52 0.795 16.8
## 5 2 OJ 2.66 0.840 26.1
## 6 2 VC 4.80 1.52 26.1
Now, you can see we have mean and error for each dose and supp
ggplot(df.summary2, aes(dose, len)) +
geom_pointrange(
aes(ymin = len - stderr, ymax = len + stderr, color = supp),
position = position_dodge(0.3)) +
scale_color_manual(values = c("indianred", "lightblue"))
How about line plots with multiple error bars?
ggplot(df.summary2, aes(dose, len)) +
geom_line(aes(linetype = supp, group = supp)) +
geom_point() +
geom_errorbar(aes(ymin = len - stderr, ymax = len + stderr, group = supp), width = 0.2)
And the same with a bar plot
ggplot(df.summary2, aes(dose, len)) +
geom_col(aes(fill = supp), position = position_dodge(0.8), width = 0.7) +
geom_errorbar(
aes(ymin = len - sd, ymax = len + sd, group = supp),
width = 0.2, position = position_dodge(0.8)) +
scale_fill_manual(values = c("indianred", "lightblue"))
Now, let’s add some jitterpoints
ggplot(df, aes(dose, len, color = supp)) +
geom_jitter(position = position_dodge(0.2)) +
geom_line(aes(group = supp), data = df.summary2) +
geom_point() +
geom_errorbar(aes(ymin = len - stderr, ymax = len + stderr, group = supp), data = df.summary2, width = 0.2)
ggplot(df, aes(dose, len, color = supp)) +
geom_col(data = df.summary2, position = position_dodge(0.8), width = 0.7, fill = "white") +
geom_jitter(
position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.8)) +
geom_errorbar(
aes(ymin = len - stderr, ymax = len + stderr), data = df.summary2,
width = 0.2, position = position_dodge(0.8)) +
scale_color_manual(values = c("indianred", "lightblue"))+
theme(legend.position = "top")
Now let’s do an empirical cumulative distribution function. This reports any given number percentile of individuals that are above or below that threshold.
set.seed(1234)
wdata = data.frame(
sex = factor(rep(c("F", "M"), each = 200)),
weight = c(rnorm(200, 55)), rnorm(200, 58))
Now let’s look at our daataframe
head(wdata, 5)
## sex weight rnorm.200..58.
## 1 F 53.79293 58.48523
## 2 F 55.27743 58.69677
## 3 F 56.08444 58.18551
## 4 F 52.65430 58.70073
## 5 F 55.42912 58.31168
Now let’s load our plotting package
library(ggplot2)
theme_set(
theme_classic() +
theme(legend.position = "bottom")
)
Now let’s create our ECDF Plot
ggplot(wdata, aes(x = weight)) +
stat_ecdf(aes(color = sex, linetype = sex),
geom = "step", size = 1.5) +
scale_color_manual(values = c("#00AFBB", "#E7B900")) +
labs(y = "weight")
Now, let’s take a look at qq plots. These are used to determine if the given data follows a normal distribution
install.packages("ggpubr")
## Installing package into '/home/student/R/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)
## also installing the dependencies 'lifecycle', 'rlang', 'vctrs', 'ggplot2', 'rstatix'
BiocManager::install("ggpubr")
## 'getOption("repos")' replaces Bioconductor standard repositories, see
## '?repositories' for details
##
## replacement repositories:
## CRAN: https://cloud.r-project.org
## Bioconductor version 3.15 (BiocManager 1.30.18), R 4.2.1 (2022-06-23)
## Warning: package(s) not installed when version(s) same as current; use `force = TRUE` to
## re-install: 'ggpubr'
## Installation paths not writeable, unable to update packages
## path: /usr/lib/R/library
## packages:
## foreign, MASS, Matrix, mgcv, nlme, nnet, rpart, spatial
## Old packages: 'BiocManager', 'bit', 'brew', 'bslib', 'callr', 'car', 'cli',
## 'commonmark', 'cpp11', 'crayon', 'curl', 'data.table', 'devtools', 'digest',
## 'DT', 'evaluate', 'fontawesome', 'gert', 'ggforce', 'ggraph', 'ggrepel',
## 'ggridges', 'ggsignif', 'ggstance', 'graphlayouts', 'igraph', 'isoband',
## 'jsonlite', 'knitr', 'labelled', 'limma', 'lme4', 'lubridate', 'maps',
## 'maptools', 'markdown', 'MatrixModels', 'matrixStats', 'minqa', 'modelr',
## 'mosaic', 'mosaicCore', 'openssl', 'pkgload', 'plotly', 'plyr', 'polyclip',
## 'processx', 'ps', 'purrr', 'ragg', 'raster', 'RcppArmadillo', 'RcppEigen',
## 'RCurl', 'readr', 'rmarkdown', 'RMySQL', 'roxygen2', 'rsconnect', 'RSQLite',
## 'sass', 'shiny', 'sp', 'sys', 'terra', 'testthat', 'tidyselect', 'tinytex',
## 'tm', 'tokenizers', 'vroom', 'widyr', 'xfun', 'XML', 'xts', 'yaml', 'zip',
## 'zoo'
library(ggpubr)
set.seed(1234)
Now, let’s randomly generate some data
wdata = data.frame(
sex = factor(rep(c("F", "M"), each = 200)),
weight = c(rnorm(200, 55), rnorm(200, 58))
)
Let’s set our theme for the graphing with ggplot
library(ggplot2)
theme_set(
theme_classic() +
theme(legend.position = "top")
)
Create a qq plot of the weight
ggplot(wdata, aes(sample = weight)) +
stat_qq(aes(color = sex)) +
scale_color_manual(values = c("#0073C2FF", "#FC4E07")) +
labs(y = "weight")
library(ggpubr)
ggqqplot(wdata, x = "weight",
color = "sex",
palettes = c("#0073C2FF", "#FC4E07"),
ggtheme = theme_pubclean())
Now, what would a non-normal distribution look like?
#install.packages(mnonr)
library(mnonr)
data2 <- mnonr::mnonr(n = 1000, p = 2, ms = 3, mk = 61, Sigma = matrix(c(1, 0.5, 0.5, 1), 2, 2), initial = NULL)
data2 <- as.data.frame(data2)
Now, let’s plot the non-normal data
ggplot(data2, aes(sample = V1)) +
stat_qq()
ggqqplot(data2, x = "V1",
palette = "#0073C2FF",
ggtheme = theme_pubclean())
Let’s look at how to put multiple plots together into a single figure
library(ggpubr)
library(ggplot2)
theme_set(
theme_bw() +
theme(legend.position = "top")
)
First, let’s create a nice boxplot
Let’s load the data
df <- ToothGrowth
df$dose <- as.factor(df$dose)
and create the plot object
p <- ggplot(df, aes(x = dose, y = len)) +
geom_boxplot(aes(fill = supp), position = position_dodge(0.9)) +
scale_fill_manual(values = c("#00AFBB", "#E7B800"))
p
Now let’s look at the gvgplot facet function
p + facet_grid(rows = vars(supp))
Now, let’s do a facet with multiple variables
p + facet_grid(rows = vars(dose), cols = vars(supp))
p
Now, let’s look at the facet_wrap function. This allows facets to be placed side-by-side
p + facet_wrap(vars(dose), ncol = 2)
Now, how do we combine multiple plots using ggarrange()
Let’s start by making some basic plots. First, we will define a color palette and data
my3cols <- c("#e7B800", "#2E9FDF", "#FC4E07")
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
Now, let’s make some basic plots
p <- ggplot(ToothGrowth, aes(x = dose, y = len))
bxp <- p + geom_boxplot(aes(color = dose)) +
scale_color_manual(values = my3cols)
Okay, now let’s do a dotplot
dp <- p + geom_dotplot(aes(color = dose, fill = dose),
binaxis = 'y', stackdir = 'center') +
scale_color_manual(values = my3cols) +
scale_fill_manual(values = my3cols)
Now, lastly let’s create a lineplot
lp <- ggplot(economics, aes(x = date, y = psavert)) +
geom_line(color = "indianred")
Now, we can make the figure
figure <- ggarrange(bxp, dp, lp, labels = c("A", "B", "C"), ncol = 2, nrow = 2)
## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.
figure
This looks great, but we can make it look even better
figure2 <- ggarrange(
lp,
ggarrange(bxp, dp, ncol = 2, labels = c("B", "C")),
nrow = 2,
labels = "A")
## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.
figure2
Okay… This looks really good, but you’ll notice that there are two legends that are the same.
ggarrange(
bxp, dp, labels = c("A", "B"),
common.legend = TRUE, legend = "bottom")
## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.
Lastly, we should export the plot
ggexport(figure2, filename = "facetfigure.pdf")
## file saved to facetfigure.pdf
We can also export multiple plots to a pdf
ggexport(bxp, dp, lp, filename = "multi.pdf")
## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.
## file saved to multi.pdf
Lastly, we can export to a pdf with multople pages and multiple columns
ggexport(bxp, dp, lp, bxp, filename = "test2.pdf", nrow =2, ncol = 1)
## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.
## file saved to test2.pdf
Let’s get started with heatmaps
#install.packages(heatmap3)
library(heatmap3)
Now, let’s get our data.
data <- ldeaths
data2 <- do.call(cbind, split(data, cycle(data)))
dimnames(data2) <- dimnames(.preformat.ts(data))
Now, let’s generate a heat map
heatmap(data2)
heatmap(data2, Rowv = NA, Colv = NA)
Now, let’s play with the colors
rc <- rainbow(nrow(data2), start = 0, end = 0.3)
cc <- rainbow(ncol(data2), start = 0, end = 0.3)
Now, let’s apply our color selections
heatmap(data2, ColSideColors = cc)
library(RColorBrewer)
heatmap(data2, ColSideColors = cc,
col = colorRampPalette(brewer.pal(8, "PiYG"))(25))
There’s more that we can customize
library(gplots)
##
## Attaching package: 'gplots'
## The following object is masked from 'package:plotrix':
##
## plotCI
## The following object is masked from 'package:stats':
##
## lowess
heatmap(data2, ColSideColors = cc,
col = colorRampPalette(brewer.pal(8, "PiYG"))(25))
If you encounter an unusual value in your dataset and simply want to move on to the rest of your analysis, you have two options:
Drop the entire row with the strange values:
library(dplyr)
library(ggplot2)
diamonds <- diamonds
diamonds2 <- diamonds %>%
filter(between(y, 3, 20))
In this instance, y is the width of the diamond, so anything undr 3 mm or above 20 is excluded
I don’t recommend this option, just because there is one bad measurement doesn’t mean they are all bad
Instead, I recommend replacing the unusual values with missing values
diamonds3 <- diamonds %>%
dplyr::mutate(y = ifelse(y < 3 | y > 20, NA, y))
Like R, ggplot2 subscribe to the idea that missing values shouldn’t pass silently into the night.
ggplot(data = diamonds3, mapping = aes( x = x, y = y)) +
geom_point()
## Warning: Removed 9 rows containing missing values (geom_point).
If you want to suppress that warning you can use na.rm = TRUE
ggplot(data = diamonds3, mapping = aes(x = x, y = y)) +
geom_point(na.rm = TRUE)
Other times you want to understand what makes observations with missing values different to the observation with recorded values. For example, in the NYCflights13 dataset, missing values in the dep_time variable indicate that the flight was canceled. So you might want to compare the scheduled departure times for cancelled and non-cancelled times.
Might have to install this package.
BiocManager::install("nycflights13")
## 'getOption("repos")' replaces Bioconductor standard repositories, see
## '?repositories' for details
##
## replacement repositories:
## CRAN: https://cloud.r-project.org
## Bioconductor version 3.15 (BiocManager 1.30.18), R 4.2.1 (2022-06-23)
## Warning: package(s) not installed when version(s) same as current; use `force = TRUE` to
## re-install: 'nycflights13'
## Installation paths not writeable, unable to update packages
## path: /usr/lib/R/library
## packages:
## foreign, MASS, Matrix, mgcv, nlme, nnet, rpart, spatial
## Old packages: 'BiocManager', 'bit', 'brew', 'bslib', 'callr', 'car', 'cli',
## 'commonmark', 'cpp11', 'crayon', 'curl', 'data.table', 'devtools', 'digest',
## 'DT', 'evaluate', 'fontawesome', 'gert', 'ggforce', 'ggraph', 'ggrepel',
## 'ggridges', 'ggsignif', 'ggstance', 'graphlayouts', 'igraph', 'isoband',
## 'jsonlite', 'knitr', 'labelled', 'limma', 'lme4', 'lubridate', 'maps',
## 'maptools', 'markdown', 'MatrixModels', 'matrixStats', 'minqa', 'modelr',
## 'mosaic', 'mosaicCore', 'openssl', 'pkgload', 'plotly', 'plyr', 'polyclip',
## 'processx', 'ps', 'purrr', 'ragg', 'raster', 'RcppArmadillo', 'RcppEigen',
## 'RCurl', 'readr', 'rmarkdown', 'RMySQL', 'roxygen2', 'rsconnect', 'RSQLite',
## 'sass', 'shiny', 'sp', 'sys', 'terra', 'testthat', 'tidyselect', 'tinytex',
## 'tm', 'tokenizers', 'vroom', 'widyr', 'xfun', 'XML', 'xts', 'yaml', 'zip',
## 'zoo'
library(nycflights13)
nycflights13::flights %>%
dplyr::mutate(
cancelled = is.na(dep_time),
sched_hour = sched_dep_time %/% 100,
sched_min = sched_dep_time %% 100,
sched_dep_time = sched_hour + sched_min / 60
) %>%
ggplot(mapping = aes(sched_dep_time)) +
geom_freqpoly(mapping = aes(color = cancelled), binwidth = 1/4)
What if we want to know what our outliers are?
First, we need to load the required libraries
library(outliers)
library(ggplot2)
And let’s load our packages
library(readr)
library(readxl)
library(outliers)
library(RCurl)
##
## Attaching package: 'RCurl'
## The following object is masked from 'package:tidyr':
##
## complete
library(dplyr)
And reload the dataset because we removed outliers
Air_data <- read_excel("AirQualityUCI.xlsx")
Let’s create a function using the grubb test to identify all outliers. The grubbs test identifies outliers in a univariate dataset that is presumed. To come from a normal distribution.
grubbs.flag <- function(x) {
# lets create a variable called outliers and save nothing in it, we'll add to the variable
#as we identify them
outliers <- NULL
# We'll create a variable called test to idenitfy which univariate we are testing
test <- x
# now using the outliers package, use grubbs.test to find outliers in our variable
grubbs.result <- grubbs.test(test)
# lets get the p-values of all tested variables
pv <- grubbs.result$p.value
# now lets search through our p-values for ones that are outside of 0.5
while(pv < 0.05) {
# anything with a p-value greater than p = 0.05, we add to our empty outliers vector
outliers <- c(outliers, as.numeric(strsplit(grubbs.result$alternative, " ")[[1]][[3]]))
# now we want to remove those outliers from our test variable
test <- x[!x %in% outliers]
# and run the grubbs test again without the outliers
grubbs.result <- grubbs.test(test)
# and save the new p-values
pv <- grubbs.result$p.value
}
return(data.frame(x = x, Outliers = (x %in% outliers)))
}
Let’s create the function
identified_outliers <- grubbs.flag(Air_data$AH)
Now we can create a histogram showing where the outliers were
ggplot(grubbs.flag(Air_data$AH), aes(x = Air_data$AH, color = Outliers, fill = Outliers)) +
geom_histogram(binwidth = diff(range(Air_data$AH))) +
theme_bw()
First, let’s load our required packages.
ggplot(data = diamonds, mapping = aes(x= price)) +
geom_freqpoly(mapping = aes(color = cut), binwidth = 500)
It’s hard to see the difference in distribution because the counts differ so much.
ggplot(diamonds) +
geom_bar(mapping = aes(x = cut))
To make the comparison easier, we need to swap the display on the y-axis. Instead of displaying count, we’ll display density, which is the count standardized so that the area under the curve is one.
ggplot(data = diamonds, mapping = aes(x = price, y = ..density..)) +
geom_freqpoly(mapping = aes(color = cut), binwidth = 500)
It appears the fair diamonds (the lowest cut quality) have the highest average price. But maybe that’s because frequency Polygons are a little hard to interpret.
Another alternative is the boxplot. A boxplot is a type of visual shorthand for a distribution of values.
ggplot(data = diamonds, mapping = aes(x = cut, y = price)) +
geom_boxplot()
We see much less information about the distribution, but the boxplots are much more compact, so we can more easily Compare them. It supports the counterintuitive finding that better quality diamonds are cheaper on average! Let’s look at some car data
ggplot(data = mpg, mapping = aes(x = class, y = hwy)) +
geom_boxplot()
Let’s plot the data.
ggplot(data = mpg) +
geom_boxplot(mapping = aes(x = reorder(class, hwy, FUN = median), y = hwy))
If you have long variable names, you can switch the axis and flip it 90 degrees.
ggplot(data = mpg) +
geom_boxplot(mapping = aes(x = reorder(class, hwy, FUN = median), y = hwy)) +
coord_flip()
To visualize the correlation between two continuous variables, we can use a scatter plot.
ggplot(data = diamonds) +
geom_point(mapping = aes(x = carat, y = price))
Scatterplots become less useful as the size of your dataset grows, because we get overplot. We can fix this using the alpha aesthetic
ggplot(data = diamonds) +
geom_point(mapping = aes(x = carat, y = price), alpha = 1/100)
First let’s load a required library
library(RCurl)
library(dplyr)
Now, let’s get our data
site <- "https://raw.githubusercontent.com/nytimes/covid-19-data/master/colleges/colleges.csv"
College_Data <- read.csv(site)
First, let’s use the str function, this shows the structure of the graph
str(College_Data)
## 'data.frame': 1948 obs. of 9 variables:
## $ date : chr "2021-05-26" "2021-05-26" "2021-05-26" "2021-05-26" ...
## $ state : chr "Alabama" "Alabama" "Alabama" "Alabama" ...
## $ county : chr "Madison" "Montgomery" "Limestone" "Lee" ...
## $ city : chr "Huntsville" "Montgomery" "Athens" "Auburn" ...
## $ ipeds_id : chr "100654" "100724" "100812" "100858" ...
## $ college : chr "Alabama A&M University" "Alabama State University" "Athens State University" "Auburn University" ...
## $ cases : int 41 2 45 2742 220 4 263 137 49 76 ...
## $ cases_2021: int NA NA 10 567 80 NA 49 53 10 35 ...
## $ notes : chr "" "" "" "" ...
What if we want to arrange our dataset alphabetically by college?
alphabetical <- College_Data %>%
arrange(College_Data$college)
The glimpse package is another way to preview data.
glimpse(College_Data)
## Rows: 1,948
## Columns: 9
## $ date <chr> "2021-05-26", "2021-05-26", "2021-05-26", "2021-05-26", "20…
## $ state <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Ala…
## $ county <chr> "Madison", "Montgomery", "Limestone", "Lee", "Montgomery", …
## $ city <chr> "Huntsville", "Montgomery", "Athens", "Auburn", "Montgomery…
## $ ipeds_id <chr> "100654", "100724", "100812", "100858", "100830", "102429",…
## $ college <chr> "Alabama A&M University", "Alabama State University", "Athe…
## $ cases <int> 41, 2, 45, 2742, 220, 4, 263, 137, 49, 76, 67, 0, 229, 19, …
## $ cases_2021 <int> NA, NA, 10, 567, 80, NA, 49, 53, 10, 35, 5, NA, 10, NA, 19,…
## $ notes <chr> "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",…
We can also subset with select()
College_Cases <- select(College_Data, college, cases)
We can also filter or subset with the filter function
Louisiana_Cases <- filter(College_Data, state == "Louisiana")
Let’s filter out a smaller amount of states
South_Cases <- filter(College_Data, state == "Louisiana" | state == "Texas" | state == "Arkansas" | state == "Mississippi")
Let’s look at some time series data.
First, we’ll load the required libraries
library(lubridate)
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
library(dplyr)
library(ggplot2)
library(gridExtra)
##
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
##
## combine
library(scales)
##
## Attaching package: 'scales'
## The following object is masked from 'package:readr':
##
## col_factor
## The following object is masked from 'package:plotrix':
##
## rescale
## The following object is masked from 'package:viridis':
##
## viridis_pal
state_site <- 'https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv'
State_Data <- read.csv(state_site)
Now, let’s load some data
Let’s create group_by object using the state column
state_cases <- group_by(State_Data, state)
class(state_cases)
## [1] "grouped_df" "tbl_df" "tbl" "data.frame"
How many measurements were made by state? This gives us a idea of when states started reporting.
Days_since_first_reported <- tally(state_cases)
Let’s visualize some data
First, let’s start off with some definition
Data – obvious – the stuff we want to visualize Layer – made of geometric elements and requisite statistical information. Include geometic objects, which represents the plot
Scales – used to map values in the data space that is used for creation of values (color, size, shape, etc)
Coordinate system – describes how the data coordinates are mapped together in relation to the plane on the graphic
Faceting – how to break up data into subsets to display multiple types or groups of data
Theme – controls the finer points of the display, such as font, size, and background color
options(repr.plot.width = 6, repr.plot.height = 6)
class(College_Data)
## [1] "data.frame"
head(College_Data)
## date state county city ipeds_id
## 1 2021-05-26 Alabama Madison Huntsville 100654
## 2 2021-05-26 Alabama Montgomery Montgomery 100724
## 3 2021-05-26 Alabama Limestone Athens 100812
## 4 2021-05-26 Alabama Lee Auburn 100858
## 5 2021-05-26 Alabama Montgomery Montgomery 100830
## 6 2021-05-26 Alabama Walker Jasper 102429
## college cases cases_2021 notes
## 1 Alabama A&M University 41 NA
## 2 Alabama State University 2 NA
## 3 Athens State University 45 10
## 4 Auburn University 2742 567
## 5 Auburn University at Montgomery 220 80
## 6 Bevill State Community College 4 NA
summary(College_Data)
## date state county city
## Length:1948 Length:1948 Length:1948 Length:1948
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## ipeds_id college cases cases_2021
## Length:1948 Length:1948 Min. : 0.0 Min. : 0.0
## Class :character Class :character 1st Qu.: 32.0 1st Qu.: 23.0
## Mode :character Mode :character Median : 114.5 Median : 65.0
## Mean : 363.5 Mean : 168.1
## 3rd Qu.: 303.0 3rd Qu.: 159.0
## Max. :9914.0 Max. :3158.0
## NA's :337
## notes
## Length:1948
## Class :character
## Mode :character
##
##
##
##
Now, let’s look at a different dataset
iris <- as.data.frame(iris)
class(iris)
## [1] "data.frame"
head(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
summary(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
##
Let’s start by creating a scatterplot of College Data
ggplot(data = College_Data, aes(x = cases, y = cases_2021)) +
geom_point() +
theme_minimal()
## Warning: Removed 337 rows containing missing values (geom_point).
Now, let’s do the iris data
ggplot(data = iris, aes(x = Sepal.Width, y = Sepal.Length)) +
geom_point() +
theme_minimal()
Let’s color coordinate our college data
ggplot(data = College_Data, aes(x = cases, y = cases_2021, color = state)) +
geom_point() +
theme_minimal()
## Warning: Removed 337 rows containing missing values (geom_point).
Let’s color coordinate the iris data
ggplot(data = iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
geom_point() +
theme_minimal()
Let’s run a single histogram of our Louisiana Case Data
hist(Louisiana_Cases$cases, freq = NULL, density = NULL, breaks = 10, xlab = "Total Cases", ylab = "Frequency",
main = "Total College Covid-19 Infections (Louisiana)")
Let’s run a simple histogram for the Iris Data
hist(iris$Sepal.Width, freq = NULL, density = NULL, breaks = 10, xlab = "Sepal Width",
ylab = "Frequency", main = "Iris Sepal Width")
Let’s plot our data.
histogram_college <- ggplot(data = Louisiana_Cases, aes(x = cases))
histogram_college + geom_histogram(bindwidth = 100, color = "black", aes(fill = county))+
xlab("cases") + ylab("Frequency") + ggtitle("Histogram of Covid-19 Cases in Louisiana")
## Warning: Ignoring unknown parameters: bindwidth
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Let’s create a ggplot for the IRIS data
histogram_iris <- ggplot(data = iris, aes(x = Sepal.Width))
histogram_iris + geom_histogram(bindwidth = 0.2, color = "black", aes(fill = Species)) +
xlab("sepal Width") + ylab("Frequency") +ggtitle("Histogram Iris Sepal Width by Species")
## Warning: Ignoring unknown parameters: bindwidth
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Maybe a density plot makes more sense for our college data.
ggplot(South_Cases) +
geom_density(aes(x = cases, fill = state), alpha = 0.50)
Let’s do it with the iris data.
ggplot(iris) +
geom_density(aes(x = Sepal.Width, fill = Species), alpha = 0.25)
Let’s look at violin plots for iris data.
ggplot(data = iris, aes(x = Species, y = Sepal.Length, color = Species)) +
geom_violin() +
theme_classic() +
theme(legend.position = "none")
Now let’s try the South Data
ggplot(data = South_Cases, aes(x = state, y = cases, color = state)) +
geom_violin() +
theme_gray() +
theme(legend.position = "none")
Now, let’s take a look at residual plots. This is a graph that displays the residuals on the vertical axis, and the independent variable on the horizontal. In the event that the points in a residual plot are dispersed in a random manner around the horizontal axis, it is appropriate to use a linear regression. If they are not randomly dispersed, a non-linear model is more appropriate.
Let’s start with the iris data.
ggplot(lm(Sepal.Length ~ Sepal.Width, data = iris)) +
geom_point(aes(x = .fitted, y = .resid))
Now, let’s look at the Southern States cases.
ggplot(lm(cases ~ cases_2021, data = South_Cases)) +
geom_point(aes(x = .fitted, y = .resid))
A linear model is not a good call for the state cases. Now, let’s use some correlations.
obesity <- read.csv("Obesity_insurance.csv")
Let’s load the required packages.
library(tidyr)
library(dplyr)
Let’s look at the structure of the dataset.
str(obesity)
## 'data.frame': 1338 obs. of 7 variables:
## $ age : int 19 18 28 33 32 31 46 37 37 60 ...
## $ sex : chr "female" "male" "male" "male" ...
## $ bmi : num 27.9 33.8 33 22.7 28.9 ...
## $ children: int 0 1 3 0 0 0 1 3 2 0 ...
## $ smoker : chr "yes" "no" "no" "no" ...
## $ region : chr "southwest" "southeast" "southeast" "northwest" ...
## $ charges : num 16885 1726 4449 21984 3867 ...
Let’s look at the column classes
class(obesity)
## [1] "data.frame"
And get a summary of distribution of the variables.
summary(obesity)
## age sex bmi children
## Min. :18.00 Length:1338 Min. :15.96 Min. :0.000
## 1st Qu.:27.00 Class :character 1st Qu.:26.30 1st Qu.:0.000
## Median :39.00 Mode :character Median :30.40 Median :1.000
## Mean :39.21 Mean :30.66 Mean :1.095
## 3rd Qu.:51.00 3rd Qu.:34.69 3rd Qu.:2.000
## Max. :64.00 Max. :53.13 Max. :5.000
## smoker region charges
## Length:1338 Length:1338 Min. : 1122
## Class :character Class :character 1st Qu.: 4740
## Mode :character Mode :character Median : 9382
## Mean :13270
## 3rd Qu.:16640
## Max. :63770
Now, let’s look at the distribution for insurance charges.
hist(obesity$charges)
We can also get an idea of the distribution using a boxplot.
boxplot(obesity$charges)
boxplot(obesity$bmi)
Now, let’s look at correlation. The cor() command is used to determine correlation between two vectors, all of the columns of a data frame, or two data frames. The cov() command, on the otherhand, examines the covariance. The cor.test() command carries out a test as to the significance of the correlation.
cor(obesity$charges, obesity$bmi)
## [1] 0.198341
This test uses a spearman Rho correlation, or you can use Kendall’s tau by specifying it
cor(obesity$charges, obesity$bmi, method = 'kendall')
## [1] 0.08252397
This correlation measures strength of a correlation between -1 and 1.
Now, let’s look at the Tietjen=Moore test. This is used for the univariate datasets. The algorithm depicts the detection of the outliers in a univariate dataset.
TietjenMoore <- function(dataSeries, k)
{
n = length(dataSeries)
# Compute the absolute residuals
r = abs(dataSeries - mean(dataSeries))
# Sort data according to size of residual
df = data.frame(dataSeries, r)
dfs = df[order(df$r),]
# Create a subset of the data without the largest values.
klarge = c((n-k+1):n)
subdataSeries = dfs$dataSeries[-klarge]
# Compute the sums of squares
ksub = (subdataSeries - mean(subdataSeries)) **2
all = (df$dataSeries - mean(df$dataSeries)) **2
# Compute the test statistic.
sum(ksub)/sum(all)
}
This function helps to compute the absolute residuals and sorts data, according to the size of the residuals. Later, we will focus on the computation of sum of squares.
FindOutliersTietjenMooreTest <- function(dataSeries, k, alpha = 0.5){
ek <- TietjenMoore(dataSeries, k)
# Compute critical values based on simulation.
test = c(1:10000)
for (i in 1:10000){
dataSeriesdataSeries = rnorm(length(dataSeries))
test[i] = TietjenMoore(dataSeriesdataSeries, k)}
Talpha = quantile(test, alpha)
list(T = ek, Talpha = Talpha)
}
This function helps us to compute the critical values based on simulation data. Now lets demonstrate these functions with sample data and the obesity dataset for evaluating this algorithm. The critical region for the Tietjen-Moore test is determined by simulation. The simulation is performed by generating the standard normal random sample of size n and computing the Tietjen Moore test statistic. Typically, 10,000 random samples are used. The values of the Tietjen-Moore statistic obtained from the data is compared to this reference distribution. The values of the test statistic is between zero and one. If there are no outliers in the data, the test statistic is close to 1. If there are outliers, the test statistic will be closer to zero. Thus, the test is always a lower, one-tailed test regardless of which test statistic is used, Lk or Ek. First we will look at charges.
boxplot(obesity$charges)
FindOutliersTietjenMooreTest(obesity$charges, 100)
## $T
## [1] 0.4556033
##
## $Talpha
## 50%
## 0.6350056
Let’s check out bmi
boxplot(obesity$bmi)
FindOutliersTietjenMooreTest(obesity$bmi, 7)
## $T
## [1] 0.9475628
##
## $Talpha
## 50%
## 0.950515
Probability Plots
library(ggplot2)
library(tigerstats)
## Loading required package: abd
## Loading required package: nlme
##
## Attaching package: 'nlme'
## The following object is masked from 'package:dplyr':
##
## collapse
## Loading required package: lattice
## Loading required package: grid
## Loading required package: mosaic
## Registered S3 method overwritten by 'mosaic':
## method from
## fortify.SpatialPolygonsDataFrame ggplot2
##
## The 'mosaic' package masks several functions from core packages in order to add
## additional features. The original behavior of these functions should not be affected by this.
##
## Attaching package: 'mosaic'
## The following object is masked from 'package:Matrix':
##
## mean
## The following object is masked from 'package:scales':
##
## rescale
## The following object is masked from 'package:plotrix':
##
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## The following object is masked from 'package:plotly':
##
## do
## The following objects are masked from 'package:dplyr':
##
## count, do, tally
## The following object is masked from 'package:ggplot2':
##
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## The following objects are masked from 'package:stats':
##
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## quantile, sd, t.test, var
## The following objects are masked from 'package:base':
##
## max, mean, min, prod, range, sample, sum
## Welcome to tigerstats!
## To learn more about this package, consult its website:
## http://homerhanumat.github.io/tigerstats
We will use the probability plot function and their output dnorm: density function of the normal distribution. Using the density, it is possible to determine the probability of events. Or, for example, you may wonder “What is the likelihood that a person has an IQ of exactly 140? In this case, you would need to retrieve the density of the IQ distribution at values 140. The IQ distribution can be modeled with a mean of 100 and a standard deviation of 15. The corresponding density is:
bmi.mean <- mean(obesity$bmi)
bmi.sd <- sd(obesity$bmi)
Let’s create a plot of our normal distribution
bmi.dist <- dnorm(obesity$bmi, mean = bmi.mean, sd = bmi.sd)
bmi.df <- data.frame("bmi" = obesity$bmi, "Density" = bmi.dist)
ggplot(bmi.df, aes(x = bmi, y = Density)) +
geom_point()
This gives us the probability of every single point occurring. Now, let’s use the pnorm function for more info.
bmi.dist <- pnorm(obesity$bmi, mean = bmi.mean, sd = bmi.sd)
bmi.df <- data.frame("bmi" = obesity$bmi, "Density" = bmi.dist)
ggplot(bmi.df, aes(x = bmi, y = Density)) +
geom_point()
What if we want to find the probability of the bmi being greater than 40 in our distribution?
pp_greater <- function(x) {
paste(round(100 * pnorm(x,, mean = 30.66339, sd = 6.09818, lower.tail = FALSE), 2), "%")
}
pp_greater(40)
## [1] "6.29 %"
pnormGC(40, region = "above", mean = 30.66339, sd = 6.09818, graph = TRUE)
## [1] 0.06287869
What about the probability that bmi is less than 40 in our population?
pp_less <- function(x) {
paste(round(100 * (1-pnorm(x, mean = 30.66339, sd = 6.09818, lower.tail = FALSE)), 2), "%")
}
pp_less(40)
## [1] "93.71 %"
pnormGC(40, region = "below", mean = 30.66339, sd = 6.09818, graph = TRUE)
## [1] 0.9371213
What if we want to find the area in between?
pnormGC(c(20, 40), region = "between", mean = 30.66339, sd = 6.09818, graph = TRUE)
## [1] 0.8969428
What if we want to know the qnorm? Let’s use the pnorm function. We need to assume a normal distribution for this. What bmi represents the lowest 1% of the population?
qnorm(0.01, mean = 30.6639, sd= 6.09818, lower.tail = TRUE)
## [1] 16.47741
What if you want a random sampling of values within your distribution?
subset <- rnorm(50, mean = 30.6639, sd = 6.09818)
hist(subset)
subset2 <- rnorm(50000, mean = 30.6639, sd = 6.09818)
hist(subset2)
Shapiro-Wilk Test So, now we know how to generate a normal distribution, how do we tell if our samples Came from a normal distribution?
shapiro.test(obesity$charges[1:5])
##
## Shapiro-Wilk normality test
##
## data: obesity$charges[1:5]
## W = 0.84164, p-value = 0.1695
You can see here, with a small sample size, we would reject the null hypothesis that the samples came from a normal distribution. We can increase the power of the test by increasing the sample size.
shapiro.test(obesity$charges[1:1000])
##
## Shapiro-Wilk normality test
##
## data: obesity$charges[1:1000]
## W = 0.8119, p-value < 2.2e-16
Now, let’s check out age.
shapiro.test(obesity$age[1:1000])
##
## Shapiro-Wilk normality test
##
## data: obesity$age[1:1000]
## W = 0.94406, p-value < 2.2e-16
And lastly bmi.
shapiro.test(obesity$bmi[1:1000])
##
## Shapiro-Wilk normality test
##
## data: obesity$bmi[1:1000]
## W = 0.99471, p-value = 0.001426
Here is the time series data. First, let’s load our packages.
library(readr)
library(readxl)
Air_data <- read_xlsx("AirQualityUCI.xlsx")
Date- date of measurement Time – time of measurement CO(GT) – average hourly CO2 PT08,s1(CO) – tin oxide hourly average sensor response NMHC – average hourly non-metallic hydrocarbon concentration C6HC – average benzene concentration PT08.S3)NMHC) – titania average hourly sensor response NOX – average hourly NOX concentration NO2 – average hourly NO2 concentration T – temperature RH – relative humidity AH – absolute humidity Now, let’s load the data
str(Air_data)
## tibble [9,357 × 15] (S3: tbl_df/tbl/data.frame)
## $ Date : POSIXct[1:9357], format: "2004-03-10" "2004-03-10" ...
## $ Time : POSIXct[1:9357], format: "1899-12-31 18:00:00" "1899-12-31 19:00:00" ...
## $ CO(GT) : num [1:9357] 2.6 2 2.2 2.2 1.6 1.2 1.2 1 0.9 0.6 ...
## $ PT08.S1(CO) : num [1:9357] 1360 1292 1402 1376 1272 ...
## $ NMHC(GT) : num [1:9357] 150 112 88 80 51 38 31 31 24 19 ...
## $ C6H6(GT) : num [1:9357] 11.88 9.4 9 9.23 6.52 ...
## $ PT08.S2(NMHC): num [1:9357] 1046 955 939 948 836 ...
## $ NOx(GT) : num [1:9357] 166 103 131 172 131 89 62 62 45 -200 ...
## $ PT08.S3(NOx) : num [1:9357] 1056 1174 1140 1092 1205 ...
## $ NO2(GT) : num [1:9357] 113 92 114 122 116 96 77 76 60 -200 ...
## $ PT08.S4(NO2) : num [1:9357] 1692 1559 1554 1584 1490 ...
## $ PT08.S5(O3) : num [1:9357] 1268 972 1074 1203 1110 ...
## $ T : num [1:9357] 13.6 13.3 11.9 11 11.2 ...
## $ RH : num [1:9357] 48.9 47.7 54 60 59.6 ...
## $ AH : num [1:9357] 0.758 0.725 0.75 0.787 0.789 ...
library(tidyr)
library(dplyr)
library(lubridate)
library(hms)
##
## Attaching package: 'hms'
## The following object is masked from 'package:lubridate':
##
## hms
library(ggplot2)
Let’s get rid of the date in the time column
Air_data$Time <- as_hms(Air_data$Time)
glimpse(Air_data)
## Rows: 9,357
## Columns: 15
## $ Date <dttm> 2004-03-10, 2004-03-10, 2004-03-10, 2004-03-10, 2004-…
## $ Time <time> 18:00:00, 19:00:00, 20:00:00, 21:00:00, 22:00:00, 23:…
## $ `CO(GT)` <dbl> 2.6, 2.0, 2.2, 2.2, 1.6, 1.2, 1.2, 1.0, 0.9, 0.6, -200…
## $ `PT08.S1(CO)` <dbl> 1360.00, 1292.25, 1402.00, 1375.50, 1272.25, 1197.00, …
## $ `NMHC(GT)` <dbl> 150, 112, 88, 80, 51, 38, 31, 31, 24, 19, 14, 8, 16, 2…
## $ `C6H6(GT)` <dbl> 11.881723, 9.397165, 8.997817, 9.228796, 6.518224, 4.7…
## $ `PT08.S2(NMHC)` <dbl> 1045.50, 954.75, 939.25, 948.25, 835.50, 750.25, 689.5…
## $ `NOx(GT)` <dbl> 166, 103, 131, 172, 131, 89, 62, 62, 45, -200, 21, 16,…
## $ `PT08.S3(NOx)` <dbl> 1056.25, 1173.75, 1140.00, 1092.00, 1205.00, 1336.50, …
## $ `NO2(GT)` <dbl> 113, 92, 114, 122, 116, 96, 77, 76, 60, -200, 34, 28, …
## $ `PT08.S4(NO2)` <dbl> 1692.00, 1558.75, 1554.50, 1583.75, 1490.00, 1393.00, …
## $ `PT08.S5(O3)` <dbl> 1267.50, 972.25, 1074.00, 1203.25, 1110.00, 949.25, 73…
## $ T <dbl> 13.600, 13.300, 11.900, 11.000, 11.150, 11.175, 11.325…
## $ RH <dbl> 48.875, 47.700, 53.975, 60.000, 59.575, 59.175, 56.775…
## $ AH <dbl> 0.7577538, 0.7254874, 0.7502391, 0.7867125, 0.7887942,…
Let’s plot the data
plot(Air_data$AH, Air_data$RH, main = "Humidity Analysis", xlab = "Absolute Humidity", ylab = "Relative Humidity")
Notice that we have an outlier in our data.
t.test(Air_data$RH, Air_data$AH)
##
## Welch Two Sample t-test
##
## data: Air_data$RH and Air_data$AH
## t = 69.62, df = 17471, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 45.01707 47.62536
## sample estimates:
## mean of x mean of y
## 39.483611 -6.837604
First, we will look at the unnest_token function. Let’s start by looking at an Emily Dickinson passage.
text <- c("Because I could not stop from Death - ",
"He kindly stopped for me - ",
"The Carriage held but just ourselves - ",
"and Immortality")
text
## [1] "Because I could not stop from Death - "
## [2] "He kindly stopped for me - "
## [3] "The Carriage held but just ourselves - "
## [4] "and Immortality"
This is a typical character vector that we might want to analyze. In order to turn it into a tidytext dataset, we first need to put it into a dataframe.
library(dplyr)
text_df <- tibble(line = 1:4, text = text)
text_df
## # A tibble: 4 × 2
## line text
## <int> <chr>
## 1 1 "Because I could not stop from Death - "
## 2 2 "He kindly stopped for me - "
## 3 3 "The Carriage held but just ourselves - "
## 4 4 "and Immortality"
Reminder: A tibble is modern class of dataframe within R. It is available within the dplyr and tibble packages, that has a convenient print method, will not convert strings to factors, and does not use row names. Tibbles are great for use with tidy tools. Next, we will use the “unnest_tokens” function. First, we have the output column name that will be created as the text as unnested into it.
library(tidytext)
text_df %>%
unnest_tokens(word, text)
## # A tibble: 20 × 2
## line word
## <int> <chr>
## 1 1 because
## 2 1 i
## 3 1 could
## 4 1 not
## 5 1 stop
## 6 1 from
## 7 1 death
## 8 2 he
## 9 2 kindly
## 10 2 stopped
## 11 2 for
## 12 2 me
## 13 3 the
## 14 3 carriage
## 15 3 held
## 16 3 but
## 17 3 just
## 18 3 ourselves
## 19 4 and
## 20 4 immortality
Let’s use the janeaustenr package to analyze Jane Austen texts. There are 6 books in this package.
library(janeaustenr)
library(dplyr)
library(stringr)
original_books <- austen_books() %>%
group_by(book) %>%
dplyr:: mutate(linenumber = row_number(),
chapter = cumsum(str_detect(text, regex("^chapter [\\divxlc]",
ignore_case = TRUE))))%>%
ungroup()
original_books
## # A tibble: 73,422 × 4
## text book linenumber chapter
## <chr> <fct> <int> <int>
## 1 "SENSE AND SENSIBILITY" Sense & Sensibility 1 0
## 2 "" Sense & Sensibility 2 0
## 3 "by Jane Austen" Sense & Sensibility 3 0
## 4 "" Sense & Sensibility 4 0
## 5 "(1811)" Sense & Sensibility 5 0
## 6 "" Sense & Sensibility 6 0
## 7 "" Sense & Sensibility 7 0
## 8 "" Sense & Sensibility 8 0
## 9 "" Sense & Sensibility 9 0
## 10 "CHAPTER 1" Sense & Sensibility 10 1
## # … with 73,412 more rows
To work with this as a tidy dataset, we need to restructure in the one-token-per-row format, which, as we saw earlier, is done with the unnest_tokens() function.
library(tidytext)
tidy_books <- original_books %>%
unnest_tokens(word, text)
tidy_books
## # A tibble: 725,055 × 4
## book linenumber chapter word
## <fct> <int> <int> <chr>
## 1 Sense & Sensibility 1 0 sense
## 2 Sense & Sensibility 1 0 and
## 3 Sense & Sensibility 1 0 sensibility
## 4 Sense & Sensibility 3 0 by
## 5 Sense & Sensibility 3 0 jane
## 6 Sense & Sensibility 3 0 austen
## 7 Sense & Sensibility 5 0 1811
## 8 Sense & Sensibility 10 1 chapter
## 9 Sense & Sensibility 10 1 1
## 10 Sense & Sensibility 13 1 the
## # … with 725,045 more rows
This function uses the tokenizers package to separate each line of text in the original dataframe into tokens. The default tokenizing is for words, but other options, including characters, n-grams, sentences, lines, or paragraphs can be used. Now that the data is a one-word-per-row format, we can manipulate it with tools like dplyr. Often, in a text analysis we will want to remove stop words. Stop words are words that are NOT USEFUL for an analysis. These include words like the, of, to, and, and so forth. We can remove stop words (kept in the tidytext data set “stop_words”) with an anti_join().
data(stop_words)
tidy_books <- tidy_books %>%
anti_join(stop_words)
## Joining, by = "word"
The stop words data set in the tidy text contains stop words from three lexisons. We can use them all together, as we have here, or filter() to only use one set of stop words if that is more appropriate for your analysis.
tidy_books %>%
count(word, sort = TRUE)
## # A tibble: 13,914 × 2
## word n
## <chr> <int>
## 1 miss 1855
## 2 time 1337
## 3 fanny 862
## 4 dear 822
## 5 lady 817
## 6 sir 806
## 7 day 797
## 8 emma 787
## 9 sister 727
## 10 house 699
## # … with 13,904 more rows
Because we have been using tidy tools, our word counts are stored in a tidy dataframe. This allows us to pipe this directly into ggplot2. For example, we can create a visualization of the most common words.
library(ggplot2)
tidy_books %>%
count(word, sort = TRUE) %>%
filter(n > 600) %>%
dplyr::mutate(word = reorder(word, n)) %>%
ggplot(aes(n, word)) +
geom_col() +
labs(y = NULL, x = "word count")
The gutenbergr package: The gutenbergr package: This package provides access to the public domain works from the gutenberg project (www.gutenburg.org). This package includes tools for both downloading books and a complete dataset of project gutenberg metadata that can be used to find works of interest. We will mostly use the function gutenberg_download(). Word Frequencies Lets look at some biology texts, starting with Darwin. The Voyage of the Beagle - 944 On the origin of species by the means of natural selection - 1228 The expression of emotions in man and animals - 1227 The descent of man, and selection in relation to sex - 2300 We can access these words using gutenberg_download() and the Project Gutenberg IDnumbers.
library(gutenbergr)
darwin <- gutenberg_download(c(944, 1227, 1228, 2300), mirror = "https://mirror2.sandyriver.net/pub/gutenberg")
Let’s break these into tokens
tidy_darwin <- darwin %>%
unnest_tokens(word, text) %>%
anti_join(stop_words)
## Joining, by = "word"
Let’s check out what the most common Darwin words are
tidy_darwin %>%
count(word, sort = TRUE)
## # A tibble: 23,630 × 2
## word n
## <chr> <int>
## 1 species 2998
## 2 male 1672
## 3 males 1337
## 4 animals 1310
## 5 birds 1292
## 6 female 1197
## 7 sexes 1095
## 8 females 1038
## 9 selection 1038
## 10 sexual 801
## # … with 23,620 more rows
Now lets get some work from Thomas Morgan, who is credited with discovering chromosomes. Regeneration - 57198 The genetic and operative evidence relating to secondary sexual characteristics - 57460 Evolution and Adaptation - 63540
morgan <- gutenberg_download(c(57198, 57460, 63540), mirror = "https://mirror2.sandyriver.net/pub/gutenberg")
Let’s tokenize THM
tidy_morgan <- morgan %>%
unnest_tokens(word, text) %>%
anti_join(stop_words)
## Joining, by = "word"
What are THM’s most common words?
tidy_morgan %>%
count(word, sort = TRUE)
## # A tibble: 13,855 × 2
## word n
## <chr> <int>
## 1 species 869
## 2 regeneration 814
## 3 piece 702
## 4 cut 669
## 5 male 668
## 6 forms 631
## 7 selection 604
## 8 cells 576
## 9 found 552
## 10 development 546
## # … with 13,845 more rows
Lastly, let’s look at Thomas Henry Huxley. Evidence as to mans place in nature - 2931 On the reception of the Origin of Species - 2089 Evolution and Ethics, and other essays - 2940 Science and Culture, and other essays – 52344
huxley <- gutenberg_download(c(2931, 2089, 2940, 52344), mirror = "https://mirror2.sandyriver.net/pub/gutenberg")
Let’s create the function
tidy_huxley <- huxley %>%
unnest_tokens(word, text) %>%
anti_join(stop_words)
## Joining, by = "word"
Let’s sort the function
tidy_huxley %>%
count(word, sort = TRUE)
## # A tibble: 16,090 × 2
## word n
## <chr> <int>
## 1 species 339
## 2 nature 331
## 3 time 287
## 4 life 286
## 5 existence 255
## 6 knowledge 238
## 7 animals 227
## 8 natural 223
## 9 animal 216
## 10 science 207
## # … with 16,080 more rows
Now, let’s calculate the frequency for each word for the works of Darwin, Morgan, and Huxley by binding the dataframes together.
library(tidyr)
frequency <- bind_rows(dplyr::mutate(tidy_morgan, author = "Thomas Hunt Morgan"),
dplyr::mutate(tidy_darwin, author = "Charles Darwin"),
dplyr::mutate(tidy_huxley, author = "Thomas Henry Huxley")) %>%
dplyr::mutate(word = str_extract(word, "[a-z']+")) %>%
count(author, word) %>%
group_by(author) %>%
dplyr::mutate(proportion = n/ sum(n)) %>%
select(-n) %>%
pivot_wider(names_from = author, values_from = proportion) %>%
pivot_longer('Thomas Hunt Morgan': 'Charles Darwin', names_to = "author", values_to = "proportion")
frequency
## # A tibble: 95,895 × 3
## word author proportion
## <chr> <chr> <dbl>
## 1 a Thomas Hunt Morgan 0.00206
## 2 a Thomas Henry Huxley 0.0000856
## 3 a Charles Darwin 0.000141
## 4 ab Thomas Hunt Morgan 0.000165
## 5 ab Thomas Henry Huxley 0.0000978
## 6 ab Charles Darwin 0.00000642
## 7 abaiss Thomas Hunt Morgan NA
## 8 abaiss Thomas Henry Huxley NA
## 9 abaiss Charles Darwin 0.00000642
## 10 abandon Thomas Hunt Morgan 0.00000752
## # … with 95,885 more rows
Now, we need to change the table so that each author has its own row.
frequency2 <- pivot_wider(frequency, names_from = author, values_from = proportion)
frequency2
## # A tibble: 31,965 × 4
## word `Thomas Hunt Morgan` `Thomas Henry Huxley` `Charles Darwin`
## <chr> <dbl> <dbl> <dbl>
## 1 a 0.00206 0.0000856 0.000141
## 2 ab 0.000165 0.0000978 0.00000642
## 3 abaiss NA NA 0.00000642
## 4 abandon 0.00000752 0.0000122 0.00000321
## 5 abandoned 0.0000150 0.0000245 0.00000321
## 6 abashed NA NA 0.00000321
## 7 abatement NA 0.0000245 0.00000321
## 8 abbot NA 0.0000245 0.00000321
## 9 abbott NA NA 0.00000642
## 10 abbreviated NA NA 0.0000128
## # … with 31,955 more rows
Now, let’s plot
library(scales)
ggplot(frequency2, aes(x = `Charles Darwin`, y = `Thomas Hunt Morgan`), color = abs(- 'Charles Darwin' - 'Thomas Hunt Morgan')) +
geom_abline(color = "gray40", lty = 2) +
geom_jitter(alpha = 0.1, size = 2.5, width = 0.3, height = 0.3) +
geom_text(aes(label = word), check_overlap = TRUE, vjust = 1.5) +
scale_x_log10(labels = percent_format()) +
scale_y_log10(labels = percent_format()) +
scale_color_gradient(limits = "darkslategray4", high = "gray75") +
theme(legend.position = "none") +
labs(y = "Thomas Hunt Morgan", x = "Charles Darwin")
## Warning: Removed 24513 rows containing missing values (geom_point).
## Warning: Removed 24514 rows containing missing values (geom_text).
The Sentiments datasets There are a variety of methods and dictionaries that exist for evaluating the opinion or emotion of the text. AFFIN Bing Nrc Bing categorizes words in a binary fashion into positive or negative. Nrc categorizes into positive, negative, anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. AFFIN assigns a score between -5 and 5, with negative indicating negative sentiment, and 5 positive.
The function get_sentiment() allows us to get the specific sentiments lexicon with the measures for each one. Let’s install the package.
BiocManager::install("textdata")
## 'getOption("repos")' replaces Bioconductor standard repositories, see
## '?repositories' for details
##
## replacement repositories:
## CRAN: https://cloud.r-project.org
## Bioconductor version 3.15 (BiocManager 1.30.18), R 4.2.1 (2022-06-23)
## Warning: package(s) not installed when version(s) same as current; use `force = TRUE` to
## re-install: 'textdata'
## Installation paths not writeable, unable to update packages
## path: /usr/lib/R/library
## packages:
## foreign, MASS, Matrix, mgcv, nlme, nnet, rpart, spatial
## Old packages: 'BiocManager', 'bit', 'brew', 'bslib', 'callr', 'car', 'cli',
## 'commonmark', 'cpp11', 'crayon', 'curl', 'data.table', 'devtools', 'digest',
## 'DT', 'evaluate', 'fontawesome', 'gert', 'ggforce', 'ggraph', 'ggrepel',
## 'ggridges', 'ggsignif', 'ggstance', 'graphlayouts', 'igraph', 'isoband',
## 'jsonlite', 'knitr', 'labelled', 'limma', 'lme4', 'lubridate', 'maps',
## 'maptools', 'markdown', 'MatrixModels', 'matrixStats', 'minqa', 'modelr',
## 'mosaic', 'mosaicCore', 'openssl', 'pkgload', 'plotly', 'plyr', 'polyclip',
## 'processx', 'ps', 'purrr', 'ragg', 'raster', 'RcppArmadillo', 'RcppEigen',
## 'RCurl', 'readr', 'rmarkdown', 'RMySQL', 'roxygen2', 'rsconnect', 'RSQLite',
## 'sass', 'shiny', 'sp', 'sys', 'terra', 'testthat', 'tidyselect', 'tinytex',
## 'tm', 'tokenizers', 'vroom', 'widyr', 'xfun', 'XML', 'xts', 'yaml', 'zip',
## 'zoo'
Let’s load the required packages
library(tidytext)
library(textdata)
afinn <- read.csv("affin.csv")
afinn
## X word value
## 1 1 abandon -2
## 2 2 abandoned -2
## 3 3 abandons -2
## 4 4 abducted -2
## 5 5 abduction -2
## 6 6 abductions -2
## 7 7 abhor -3
## 8 8 abhorred -3
## 9 9 abhorrent -3
## 10 10 abhors -3
## 11 11 abilities 2
## 12 12 ability 2
## 13 13 aboard 1
## 14 14 absentee -1
## 15 15 absentees -1
## 16 16 absolve 2
## 17 17 absolved 2
## 18 18 absolves 2
## 19 19 absolving 2
## 20 20 absorbed 1
## 21 21 abuse -3
## 22 22 abused -3
## 23 23 abuses -3
## 24 24 abusive -3
## 25 25 accept 1
## 26 26 accepted 1
## 27 27 accepting 1
## 28 28 accepts 1
## 29 29 accident -2
## 30 30 accidental -2
## 31 31 accidentally -2
## 32 32 accidents -2
## 33 33 accomplish 2
## 34 34 accomplished 2
## 35 35 accomplishes 2
## 36 36 accusation -2
## 37 37 accusations -2
## 38 38 accuse -2
## 39 39 accused -2
## 40 40 accuses -2
## 41 41 accusing -2
## 42 42 ache -2
## 43 43 achievable 1
## 44 44 aching -2
## 45 45 acquit 2
## 46 46 acquits 2
## 47 47 acquitted 2
## 48 48 acquitting 2
## 49 49 acrimonious -3
## 50 50 active 1
## 51 51 adequate 1
## 52 52 admire 3
## 53 53 admired 3
## 54 54 admires 3
## 55 55 admiring 3
## 56 56 admit -1
## 57 57 admits -1
## 58 58 admitted -1
## 59 59 admonish -2
## 60 60 admonished -2
## 61 61 adopt 1
## 62 62 adopts 1
## 63 63 adorable 3
## 64 64 adore 3
## 65 65 adored 3
## 66 66 adores 3
## 67 67 advanced 1
## 68 68 advantage 2
## 69 69 advantages 2
## 70 70 adventure 2
## 71 71 adventures 2
## 72 72 adventurous 2
## 73 73 affected -1
## 74 74 affection 3
## 75 75 affectionate 3
## 76 76 afflicted -1
## 77 77 affronted -1
## 78 78 afraid -2
## 79 79 aggravate -2
## 80 80 aggravated -2
## 81 81 aggravates -2
## 82 82 aggravating -2
## 83 83 aggression -2
## 84 84 aggressions -2
## 85 85 aggressive -2
## 86 86 aghast -2
## 87 87 agog 2
## 88 88 agonise -3
## 89 89 agonised -3
## 90 90 agonises -3
## 91 91 agonising -3
## 92 92 agonize -3
## 93 93 agonized -3
## 94 94 agonizes -3
## 95 95 agonizing -3
## 96 96 agree 1
## 97 97 agreeable 2
## 98 98 agreed 1
## 99 99 agreement 1
## 100 100 agrees 1
## 101 101 alarm -2
## 102 102 alarmed -2
## 103 103 alarmist -2
## 104 104 alarmists -2
## 105 105 alas -1
## 106 106 alert -1
## 107 107 alienation -2
## 108 108 alive 1
## 109 109 allergic -2
## 110 110 allow 1
## 111 111 alone -2
## 112 112 amaze 2
## 113 113 amazed 2
## 114 114 amazes 2
## 115 115 amazing 4
## 116 116 ambitious 2
## 117 117 ambivalent -1
## 118 118 amuse 3
## 119 119 amused 3
## 120 120 amusement 3
## 121 121 amusements 3
## 122 122 anger -3
## 123 123 angers -3
## 124 124 angry -3
## 125 125 anguish -3
## 126 126 anguished -3
## 127 127 animosity -2
## 128 128 annoy -2
## 129 129 annoyance -2
## 130 130 annoyed -2
## 131 131 annoying -2
## 132 132 annoys -2
## 133 133 antagonistic -2
## 134 134 anti -1
## 135 135 anticipation 1
## 136 136 anxiety -2
## 137 137 anxious -2
## 138 138 apathetic -3
## 139 139 apathy -3
## 140 140 apeshit -3
## 141 141 apocalyptic -2
## 142 142 apologise -1
## 143 143 apologised -1
## 144 144 apologises -1
## 145 145 apologising -1
## 146 146 apologize -1
## 147 147 apologized -1
## 148 148 apologizes -1
## 149 149 apologizing -1
## 150 150 apology -1
## 151 151 appalled -2
## 152 152 appalling -2
## 153 153 appease 2
## 154 154 appeased 2
## 155 155 appeases 2
## 156 156 appeasing 2
## 157 157 applaud 2
## 158 158 applauded 2
## 159 159 applauding 2
## 160 160 applauds 2
## 161 161 applause 2
## 162 162 appreciate 2
## 163 163 appreciated 2
## 164 164 appreciates 2
## 165 165 appreciating 2
## 166 166 appreciation 2
## 167 167 apprehensive -2
## 168 168 approval 2
## 169 169 approved 2
## 170 170 approves 2
## 171 171 ardent 1
## 172 172 arrest -2
## 173 173 arrested -3
## 174 174 arrests -2
## 175 175 arrogant -2
## 176 176 ashame -2
## 177 177 ashamed -2
## 178 178 ass -4
## 179 179 assassination -3
## 180 180 assassinations -3
## 181 181 asset 2
## 182 182 assets 2
## 183 183 assfucking -4
## 184 184 asshole -4
## 185 185 astonished 2
## 186 186 astound 3
## 187 187 astounded 3
## 188 188 astounding 3
## 189 189 astoundingly 3
## 190 190 astounds 3
## 191 191 attack -1
## 192 192 attacked -1
## 193 193 attacking -1
## 194 194 attacks -1
## 195 195 attract 1
## 196 196 attracted 1
## 197 197 attracting 2
## 198 198 attraction 2
## 199 199 attractions 2
## 200 200 attracts 1
## 201 201 audacious 3
## 202 202 authority 1
## 203 203 avert -1
## 204 204 averted -1
## 205 205 averts -1
## 206 206 avid 2
## 207 207 avoid -1
## 208 208 avoided -1
## 209 209 avoids -1
## 210 210 await -1
## 211 211 awaited -1
## 212 212 awaits -1
## 213 213 award 3
## 214 214 awarded 3
## 215 215 awards 3
## 216 216 awesome 4
## 217 217 awful -3
## 218 218 awkward -2
## 219 219 axe -1
## 220 220 axed -1
## 221 221 backed 1
## 222 222 backing 2
## 223 223 backs 1
## 224 224 bad -3
## 225 225 badass -3
## 226 226 badly -3
## 227 227 bailout -2
## 228 228 bamboozle -2
## 229 229 bamboozled -2
## 230 230 bamboozles -2
## 231 231 ban -2
## 232 232 banish -1
## 233 233 bankrupt -3
## 234 234 bankster -3
## 235 235 banned -2
## 236 236 bargain 2
## 237 237 barrier -2
## 238 238 bastard -5
## 239 239 bastards -5
## 240 240 battle -1
## 241 241 battles -1
## 242 242 beaten -2
## 243 243 beatific 3
## 244 244 beating -1
## 245 245 beauties 3
## 246 246 beautiful 3
## 247 247 beautifully 3
## 248 248 beautify 3
## 249 249 belittle -2
## 250 250 belittled -2
## 251 251 beloved 3
## 252 252 benefit 2
## 253 253 benefits 2
## 254 254 benefitted 2
## 255 255 benefitting 2
## 256 256 bereave -2
## 257 257 bereaved -2
## 258 258 bereaves -2
## 259 259 bereaving -2
## 260 260 best 3
## 261 261 betray -3
## 262 262 betrayal -3
## 263 263 betrayed -3
## 264 264 betraying -3
## 265 265 betrays -3
## 266 266 better 2
## 267 267 bias -1
## 268 268 biased -2
## 269 269 big 1
## 270 270 bitch -5
## 271 271 bitches -5
## 272 272 bitter -2
## 273 273 bitterly -2
## 274 274 bizarre -2
## 275 275 blah -2
## 276 276 blame -2
## 277 277 blamed -2
## 278 278 blames -2
## 279 279 blaming -2
## 280 280 bless 2
## 281 281 blesses 2
## 282 282 blessing 3
## 283 283 blind -1
## 284 284 bliss 3
## 285 285 blissful 3
## 286 286 blithe 2
## 287 287 block -1
## 288 288 blockbuster 3
## 289 289 blocked -1
## 290 290 blocking -1
## 291 291 blocks -1
## 292 292 bloody -3
## 293 293 blurry -2
## 294 294 boastful -2
## 295 295 bold 2
## 296 296 boldly 2
## 297 297 bomb -1
## 298 298 boost 1
## 299 299 boosted 1
## 300 300 boosting 1
## 301 301 boosts 1
## 302 302 bore -2
## 303 303 bored -2
## 304 304 boring -3
## 305 305 bother -2
## 306 306 bothered -2
## 307 307 bothers -2
## 308 308 bothersome -2
## 309 309 boycott -2
## 310 310 boycotted -2
## 311 311 boycotting -2
## 312 312 boycotts -2
## 313 313 brainwashing -3
## 314 314 brave 2
## 315 315 breakthrough 3
## 316 316 breathtaking 5
## 317 317 bribe -3
## 318 318 bright 1
## 319 319 brightest 2
## 320 320 brightness 1
## 321 321 brilliant 4
## 322 322 brisk 2
## 323 323 broke -1
## 324 324 broken -1
## 325 325 brooding -2
## 326 326 bullied -2
## 327 327 bullshit -4
## 328 328 bully -2
## 329 329 bullying -2
## 330 330 bummer -2
## 331 331 buoyant 2
## 332 332 burden -2
## 333 333 burdened -2
## 334 334 burdening -2
## 335 335 burdens -2
## 336 336 calm 2
## 337 337 calmed 2
## 338 338 calming 2
## 339 339 calms 2
## 340 340 can't stand -3
## 341 341 cancel -1
## 342 342 cancelled -1
## 343 343 cancelling -1
## 344 344 cancels -1
## 345 345 cancer -1
## 346 346 capable 1
## 347 347 captivated 3
## 348 348 care 2
## 349 349 carefree 1
## 350 350 careful 2
## 351 351 carefully 2
## 352 352 careless -2
## 353 353 cares 2
## 354 354 cashing in -2
## 355 355 casualty -2
## 356 356 catastrophe -3
## 357 357 catastrophic -4
## 358 358 cautious -1
## 359 359 celebrate 3
## 360 360 celebrated 3
## 361 361 celebrates 3
## 362 362 celebrating 3
## 363 363 censor -2
## 364 364 censored -2
## 365 365 censors -2
## 366 366 certain 1
## 367 367 chagrin -2
## 368 368 chagrined -2
## 369 369 challenge -1
## 370 370 chance 2
## 371 371 chances 2
## 372 372 chaos -2
## 373 373 chaotic -2
## 374 374 charged -3
## 375 375 charges -2
## 376 376 charm 3
## 377 377 charming 3
## 378 378 charmless -3
## 379 379 chastise -3
## 380 380 chastised -3
## 381 381 chastises -3
## 382 382 chastising -3
## 383 383 cheat -3
## 384 384 cheated -3
## 385 385 cheater -3
## 386 386 cheaters -3
## 387 387 cheats -3
## 388 388 cheer 2
## 389 389 cheered 2
## 390 390 cheerful 2
## 391 391 cheering 2
## 392 392 cheerless -2
## 393 393 cheers 2
## 394 394 cheery 3
## 395 395 cherish 2
## 396 396 cherished 2
## 397 397 cherishes 2
## 398 398 cherishing 2
## 399 399 chic 2
## 400 400 childish -2
## 401 401 chilling -1
## 402 402 choke -2
## 403 403 choked -2
## 404 404 chokes -2
## 405 405 choking -2
## 406 406 clarifies 2
## 407 407 clarity 2
## 408 408 clash -2
## 409 409 classy 3
## 410 410 clean 2
## 411 411 cleaner 2
## 412 412 clear 1
## 413 413 cleared 1
## 414 414 clearly 1
## 415 415 clears 1
## 416 416 clever 2
## 417 417 clouded -1
## 418 418 clueless -2
## 419 419 cock -5
## 420 420 cocksucker -5
## 421 421 cocksuckers -5
## 422 422 cocky -2
## 423 423 coerced -2
## 424 424 collapse -2
## 425 425 collapsed -2
## 426 426 collapses -2
## 427 427 collapsing -2
## 428 428 collide -1
## 429 429 collides -1
## 430 430 colliding -1
## 431 431 collision -2
## 432 432 collisions -2
## 433 433 colluding -3
## 434 434 combat -1
## 435 435 combats -1
## 436 436 comedy 1
## 437 437 comfort 2
## 438 438 comfortable 2
## 439 439 comforting 2
## 440 440 comforts 2
## 441 441 commend 2
## 442 442 commended 2
## 443 443 commit 1
## 444 444 commitment 2
## 445 445 commits 1
## 446 446 committed 1
## 447 447 committing 1
## 448 448 compassionate 2
## 449 449 compelled 1
## 450 450 competent 2
## 451 451 competitive 2
## 452 452 complacent -2
## 453 453 complain -2
## 454 454 complained -2
## 455 455 complains -2
## 456 456 comprehensive 2
## 457 457 conciliate 2
## 458 458 conciliated 2
## 459 459 conciliates 2
## 460 460 conciliating 2
## 461 461 condemn -2
## 462 462 condemnation -2
## 463 463 condemned -2
## 464 464 condemns -2
## 465 465 confidence 2
## 466 466 confident 2
## 467 467 conflict -2
## 468 468 conflicting -2
## 469 469 conflictive -2
## 470 470 conflicts -2
## 471 471 confuse -2
## 472 472 confused -2
## 473 473 confusing -2
## 474 474 congrats 2
## 475 475 congratulate 2
## 476 476 congratulation 2
## 477 477 congratulations 2
## 478 478 consent 2
## 479 479 consents 2
## 480 480 consolable 2
## 481 481 conspiracy -3
## 482 482 constrained -2
## 483 483 contagion -2
## 484 484 contagions -2
## 485 485 contagious -1
## 486 486 contempt -2
## 487 487 contemptuous -2
## 488 488 contemptuously -2
## 489 489 contend -1
## 490 490 contender -1
## 491 491 contending -1
## 492 492 contentious -2
## 493 493 contestable -2
## 494 494 controversial -2
## 495 495 controversially -2
## 496 496 convince 1
## 497 497 convinced 1
## 498 498 convinces 1
## 499 499 convivial 2
## 500 500 cool 1
## 501 501 cool stuff 3
## 502 502 cornered -2
## 503 503 corpse -1
## 504 504 costly -2
## 505 505 courage 2
## 506 506 courageous 2
## 507 507 courteous 2
## 508 508 courtesy 2
## 509 509 cover-up -3
## 510 510 coward -2
## 511 511 cowardly -2
## 512 512 coziness 2
## 513 513 cramp -1
## 514 514 crap -3
## 515 515 crash -2
## 516 516 crazier -2
## 517 517 craziest -2
## 518 518 crazy -2
## 519 519 creative 2
## 520 520 crestfallen -2
## 521 521 cried -2
## 522 522 cries -2
## 523 523 crime -3
## 524 524 criminal -3
## 525 525 criminals -3
## 526 526 crisis -3
## 527 527 critic -2
## 528 528 criticism -2
## 529 529 criticize -2
## 530 530 criticized -2
## 531 531 criticizes -2
## 532 532 criticizing -2
## 533 533 critics -2
## 534 534 cruel -3
## 535 535 cruelty -3
## 536 536 crush -1
## 537 537 crushed -2
## 538 538 crushes -1
## 539 539 crushing -1
## 540 540 cry -1
## 541 541 crying -2
## 542 542 cunt -5
## 543 543 curious 1
## 544 544 curse -1
## 545 545 cut -1
## 546 546 cute 2
## 547 547 cuts -1
## 548 548 cutting -1
## 549 549 cynic -2
## 550 550 cynical -2
## 551 551 cynicism -2
## 552 552 damage -3
## 553 553 damages -3
## 554 554 damn -4
## 555 555 damned -4
## 556 556 damnit -4
## 557 557 danger -2
## 558 558 daredevil 2
## 559 559 daring 2
## 560 560 darkest -2
## 561 561 darkness -1
## 562 562 dauntless 2
## 563 563 dead -3
## 564 564 deadlock -2
## 565 565 deafening -1
## 566 566 dear 2
## 567 567 dearly 3
## 568 568 death -2
## 569 569 debonair 2
## 570 570 debt -2
## 571 571 deceit -3
## 572 572 deceitful -3
## 573 573 deceive -3
## 574 574 deceived -3
## 575 575 deceives -3
## 576 576 deceiving -3
## 577 577 deception -3
## 578 578 decisive 1
## 579 579 dedicated 2
## 580 580 defeated -2
## 581 581 defect -3
## 582 582 defects -3
## 583 583 defender 2
## 584 584 defenders 2
## 585 585 defenseless -2
## 586 586 defer -1
## 587 587 deferring -1
## 588 588 defiant -1
## 589 589 deficit -2
## 590 590 degrade -2
## 591 591 degraded -2
## 592 592 degrades -2
## 593 593 dehumanize -2
## 594 594 dehumanized -2
## 595 595 dehumanizes -2
## 596 596 dehumanizing -2
## 597 597 deject -2
## 598 598 dejected -2
## 599 599 dejecting -2
## 600 600 dejects -2
## 601 601 delay -1
## 602 602 delayed -1
## 603 603 delight 3
## 604 604 delighted 3
## 605 605 delighting 3
## 606 606 delights 3
## 607 607 demand -1
## 608 608 demanded -1
## 609 609 demanding -1
## 610 610 demands -1
## 611 611 demonstration -1
## 612 612 demoralized -2
## 613 613 denied -2
## 614 614 denier -2
## 615 615 deniers -2
## 616 616 denies -2
## 617 617 denounce -2
## 618 618 denounces -2
## 619 619 deny -2
## 620 620 denying -2
## 621 621 depressed -2
## 622 622 depressing -2
## 623 623 derail -2
## 624 624 derailed -2
## 625 625 derails -2
## 626 626 deride -2
## 627 627 derided -2
## 628 628 derides -2
## 629 629 deriding -2
## 630 630 derision -2
## 631 631 desirable 2
## 632 632 desire 1
## 633 633 desired 2
## 634 634 desirous 2
## 635 635 despair -3
## 636 636 despairing -3
## 637 637 despairs -3
## 638 638 desperate -3
## 639 639 desperately -3
## 640 640 despondent -3
## 641 641 destroy -3
## 642 642 destroyed -3
## 643 643 destroying -3
## 644 644 destroys -3
## 645 645 destruction -3
## 646 646 destructive -3
## 647 647 detached -1
## 648 648 detain -2
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## 2288 2288 unapproved -2
## 2289 2289 unaware -2
## 2290 2290 unbelievable -1
## 2291 2291 unbelieving -1
## 2292 2292 unbiased 2
## 2293 2293 uncertain -1
## 2294 2294 unclear -1
## 2295 2295 uncomfortable -2
## 2296 2296 unconcerned -2
## 2297 2297 unconfirmed -1
## 2298 2298 unconvinced -1
## 2299 2299 uncredited -1
## 2300 2300 undecided -1
## 2301 2301 underestimate -1
## 2302 2302 underestimated -1
## 2303 2303 underestimates -1
## 2304 2304 underestimating -1
## 2305 2305 undermine -2
## 2306 2306 undermined -2
## 2307 2307 undermines -2
## 2308 2308 undermining -2
## 2309 2309 undeserving -2
## 2310 2310 undesirable -2
## 2311 2311 uneasy -2
## 2312 2312 unemployment -2
## 2313 2313 unequal -1
## 2314 2314 unequaled 2
## 2315 2315 unethical -2
## 2316 2316 unfair -2
## 2317 2317 unfocused -2
## 2318 2318 unfulfilled -2
## 2319 2319 unhappy -2
## 2320 2320 unhealthy -2
## 2321 2321 unified 1
## 2322 2322 unimpressed -2
## 2323 2323 unintelligent -2
## 2324 2324 united 1
## 2325 2325 unjust -2
## 2326 2326 unlovable -2
## 2327 2327 unloved -2
## 2328 2328 unmatched 1
## 2329 2329 unmotivated -2
## 2330 2330 unprofessional -2
## 2331 2331 unresearched -2
## 2332 2332 unsatisfied -2
## 2333 2333 unsecured -2
## 2334 2334 unsettled -1
## 2335 2335 unsophisticated -2
## 2336 2336 unstable -2
## 2337 2337 unstoppable 2
## 2338 2338 unsupported -2
## 2339 2339 unsure -1
## 2340 2340 untarnished 2
## 2341 2341 unwanted -2
## 2342 2342 unworthy -2
## 2343 2343 upset -2
## 2344 2344 upsets -2
## 2345 2345 upsetting -2
## 2346 2346 uptight -2
## 2347 2347 urgent -1
## 2348 2348 useful 2
## 2349 2349 usefulness 2
## 2350 2350 useless -2
## 2351 2351 uselessness -2
## 2352 2352 vague -2
## 2353 2353 validate 1
## 2354 2354 validated 1
## 2355 2355 validates 1
## 2356 2356 validating 1
## 2357 2357 verdict -1
## 2358 2358 verdicts -1
## 2359 2359 vested 1
## 2360 2360 vexation -2
## 2361 2361 vexing -2
## 2362 2362 vibrant 3
## 2363 2363 vicious -2
## 2364 2364 victim -3
## 2365 2365 victimize -3
## 2366 2366 victimized -3
## 2367 2367 victimizes -3
## 2368 2368 victimizing -3
## 2369 2369 victims -3
## 2370 2370 vigilant 3
## 2371 2371 vile -3
## 2372 2372 vindicate 2
## 2373 2373 vindicated 2
## 2374 2374 vindicates 2
## 2375 2375 vindicating 2
## 2376 2376 violate -2
## 2377 2377 violated -2
## 2378 2378 violates -2
## 2379 2379 violating -2
## 2380 2380 violence -3
## 2381 2381 violent -3
## 2382 2382 virtuous 2
## 2383 2383 virulent -2
## 2384 2384 vision 1
## 2385 2385 visionary 3
## 2386 2386 visioning 1
## 2387 2387 visions 1
## 2388 2388 vitality 3
## 2389 2389 vitamin 1
## 2390 2390 vitriolic -3
## 2391 2391 vivacious 3
## 2392 2392 vociferous -1
## 2393 2393 vulnerability -2
## 2394 2394 vulnerable -2
## 2395 2395 walkout -2
## 2396 2396 walkouts -2
## 2397 2397 wanker -3
## 2398 2398 want 1
## 2399 2399 war -2
## 2400 2400 warfare -2
## 2401 2401 warm 1
## 2402 2402 warmth 2
## 2403 2403 warn -2
## 2404 2404 warned -2
## 2405 2405 warning -3
## 2406 2406 warnings -3
## 2407 2407 warns -2
## 2408 2408 waste -1
## 2409 2409 wasted -2
## 2410 2410 wasting -2
## 2411 2411 wavering -1
## 2412 2412 weak -2
## 2413 2413 weakness -2
## 2414 2414 wealth 3
## 2415 2415 wealthy 2
## 2416 2416 weary -2
## 2417 2417 weep -2
## 2418 2418 weeping -2
## 2419 2419 weird -2
## 2420 2420 welcome 2
## 2421 2421 welcomed 2
## 2422 2422 welcomes 2
## 2423 2423 whimsical 1
## 2424 2424 whitewash -3
## 2425 2425 whore -4
## 2426 2426 wicked -2
## 2427 2427 widowed -1
## 2428 2428 willingness 2
## 2429 2429 win 4
## 2430 2430 winner 4
## 2431 2431 winning 4
## 2432 2432 wins 4
## 2433 2433 winwin 3
## 2434 2434 wish 1
## 2435 2435 wishes 1
## 2436 2436 wishing 1
## 2437 2437 withdrawal -3
## 2438 2438 woebegone -2
## 2439 2439 woeful -3
## 2440 2440 won 3
## 2441 2441 wonderful 4
## 2442 2442 woo 3
## 2443 2443 woohoo 3
## 2444 2444 wooo 4
## 2445 2445 woow 4
## 2446 2446 worn -1
## 2447 2447 worried -3
## 2448 2448 worry -3
## 2449 2449 worrying -3
## 2450 2450 worse -3
## 2451 2451 worsen -3
## 2452 2452 worsened -3
## 2453 2453 worsening -3
## 2454 2454 worsens -3
## 2455 2455 worshiped 3
## 2456 2456 worst -3
## 2457 2457 worth 2
## 2458 2458 worthless -2
## 2459 2459 worthy 2
## 2460 2460 wow 4
## 2461 2461 wowow 4
## 2462 2462 wowww 4
## 2463 2463 wrathful -3
## 2464 2464 wreck -2
## 2465 2465 wrong -2
## 2466 2466 wronged -2
## 2467 2467 wtf -4
## 2468 2468 yeah 1
## 2469 2469 yearning 1
## 2470 2470 yeees 2
## 2471 2471 yes 1
## 2472 2472 youthful 2
## 2473 2473 yucky -2
## 2474 2474 yummy 3
## 2475 2475 zealot -2
## 2476 2476 zealots -2
## 2477 2477 zealous 2
Let’s look at bing
bing <- get_sentiments("bing")
bing
## # A tibble: 6,786 × 2
## word sentiment
## <chr> <chr>
## 1 2-faces negative
## 2 abnormal negative
## 3 abolish negative
## 4 abominable negative
## 5 abominably negative
## 6 abominate negative
## 7 abomination negative
## 8 abort negative
## 9 aborted negative
## 10 aborts negative
## # … with 6,776 more rows
An lastly nrc
nrc <- read.csv("nrc.csv")
nrc
## X word sentiment
## 1 1 abacus trust
## 2 2 abandon fear
## 3 3 abandon negative
## 4 4 abandon sadness
## 5 5 abandoned anger
## 6 6 abandoned fear
## 7 7 abandoned negative
## 8 8 abandoned sadness
## 9 9 abandonment anger
## 10 10 abandonment fear
## 11 11 abandonment negative
## 12 12 abandonment sadness
## 13 13 abandonment surprise
## 14 14 abba positive
## 15 15 abbot trust
## 16 16 abduction fear
## 17 17 abduction negative
## 18 18 abduction sadness
## 19 19 abduction surprise
## 20 20 aberrant negative
## 21 21 aberration disgust
## 22 22 aberration negative
## 23 23 abhor anger
## 24 24 abhor disgust
## 25 25 abhor fear
## 26 26 abhor negative
## 27 27 abhorrent anger
## 28 28 abhorrent disgust
## 29 29 abhorrent fear
## 30 30 abhorrent negative
## 31 31 ability positive
## 32 32 abject disgust
## 33 33 abject negative
## 34 34 abnormal disgust
## 35 35 abnormal negative
## 36 36 abolish anger
## 37 37 abolish negative
## 38 38 abolition negative
## 39 39 abominable disgust
## 40 40 abominable fear
## 41 41 abominable negative
## 42 42 abomination anger
## 43 43 abomination disgust
## 44 44 abomination fear
## 45 45 abomination negative
## 46 46 abort negative
## 47 47 abortion disgust
## 48 48 abortion fear
## 49 49 abortion negative
## 50 50 abortion sadness
## 51 51 abortive negative
## 52 52 abortive sadness
## 53 53 abovementioned positive
## 54 54 abrasion negative
## 55 55 abrogate negative
## 56 56 abrupt surprise
## 57 57 abscess negative
## 58 58 abscess sadness
## 59 59 absence fear
## 60 60 absence negative
## 61 61 absence sadness
## 62 62 absent negative
## 63 63 absent sadness
## 64 64 absentee negative
## 65 65 absentee sadness
## 66 66 absenteeism negative
## 67 67 absolute positive
## 68 68 absolution joy
## 69 69 absolution positive
## 70 70 absolution trust
## 71 71 absorbed positive
## 72 72 absurd negative
## 73 73 absurdity negative
## 74 74 abundance anticipation
## 75 75 abundance disgust
## 76 76 abundance joy
## 77 77 abundance negative
## 78 78 abundance positive
## 79 79 abundance trust
## 80 80 abundant joy
## 81 81 abundant positive
## 82 82 abuse anger
## 83 83 abuse disgust
## 84 84 abuse fear
## 85 85 abuse negative
## 86 86 abuse sadness
## 87 87 abysmal negative
## 88 88 abysmal sadness
## 89 89 abyss fear
## 90 90 abyss negative
## 91 91 abyss sadness
## 92 92 academic positive
## 93 93 academic trust
## 94 94 academy positive
## 95 95 accelerate anticipation
## 96 96 acceptable positive
## 97 97 acceptance positive
## 98 98 accessible positive
## 99 99 accident fear
## 100 100 accident negative
## 101 101 accident sadness
## 102 102 accident surprise
## 103 103 accidental fear
## 104 104 accidental negative
## 105 105 accidental surprise
## 106 106 accidentally surprise
## 107 107 accolade anticipation
## 108 108 accolade joy
## 109 109 accolade positive
## 110 110 accolade surprise
## 111 111 accolade trust
## 112 112 accommodation positive
## 113 113 accompaniment anticipation
## 114 114 accompaniment joy
## 115 115 accompaniment positive
## 116 116 accompaniment trust
## 117 117 accomplish joy
## 118 118 accomplish positive
## 119 119 accomplished joy
## 120 120 accomplished positive
## 121 121 accomplishment positive
## 122 122 accord positive
## 123 123 accord trust
## 124 124 account trust
## 125 125 accountability positive
## 126 126 accountability trust
## 127 127 accountable positive
## 128 128 accountable trust
## 129 129 accountant trust
## 130 130 accounts trust
## 131 131 accredited positive
## 132 132 accredited trust
## 133 133 accueil positive
## 134 134 accurate positive
## 135 135 accurate trust
## 136 136 accursed anger
## 137 137 accursed fear
## 138 138 accursed negative
## 139 139 accursed sadness
## 140 140 accusation anger
## 141 141 accusation disgust
## 142 142 accusation negative
## 143 143 accusative negative
## 144 144 accused anger
## 145 145 accused fear
## 146 146 accused negative
## 147 147 accuser anger
## 148 148 accuser fear
## 149 149 accuser negative
## 150 150 accusing anger
## 151 151 accusing fear
## 152 152 accusing negative
## 153 153 ace positive
## 154 154 ache negative
## 155 155 ache sadness
## 156 156 achieve joy
## 157 157 achieve positive
## 158 158 achieve trust
## 159 159 achievement anticipation
## 160 160 achievement joy
## 161 161 achievement positive
## 162 162 achievement trust
## 163 163 aching negative
## 164 164 aching sadness
## 165 165 acid negative
## 166 166 acknowledgment positive
## 167 167 acquire positive
## 168 168 acquiring anticipation
## 169 169 acquiring positive
## 170 170 acrobat fear
## 171 171 acrobat joy
## 172 172 acrobat positive
## 173 173 acrobat trust
## 174 174 action positive
## 175 175 actionable anger
## 176 176 actionable disgust
## 177 177 actionable negative
## 178 178 actual positive
## 179 179 acuity positive
## 180 180 acumen positive
## 181 181 adapt positive
## 182 182 adaptable positive
## 183 183 adder anger
## 184 184 adder disgust
## 185 185 adder fear
## 186 186 adder negative
## 187 187 adder sadness
## 188 188 addiction negative
## 189 189 addresses anticipation
## 190 190 addresses positive
## 191 191 adept positive
## 192 192 adequacy positive
## 193 193 adhering trust
## 194 194 adipose negative
## 195 195 adjudicate fear
## 196 196 adjudicate negative
## 197 197 adjunct positive
## 198 198 administrative trust
## 199 199 admirable joy
## 200 200 admirable positive
## 201 201 admirable trust
## 202 202 admiral positive
## 203 203 admiral trust
## 204 204 admiration joy
## 205 205 admiration positive
## 206 206 admiration trust
## 207 207 admire positive
## 208 208 admire trust
## 209 209 admirer positive
## 210 210 admissible positive
## 211 211 admissible trust
## 212 212 admonition fear
## 213 213 admonition negative
## 214 214 adorable joy
## 215 215 adorable positive
## 216 216 adoration joy
## 217 217 adoration positive
## 218 218 adoration trust
## 219 219 adore anticipation
## 220 220 adore joy
## 221 221 adore positive
## 222 222 adore trust
## 223 223 adrift anticipation
## 224 224 adrift fear
## 225 225 adrift negative
## 226 226 adrift sadness
## 227 227 adulterated negative
## 228 228 adultery disgust
## 229 229 adultery negative
## 230 230 adultery sadness
## 231 231 advance anticipation
## 232 232 advance fear
## 233 233 advance joy
## 234 234 advance positive
## 235 235 advance surprise
## 236 236 advanced positive
## 237 237 advancement positive
## 238 238 advantage positive
## 239 239 advantageous positive
## 240 240 advent anticipation
## 241 241 advent joy
## 242 242 advent positive
## 243 243 advent trust
## 244 244 adventure anticipation
## 245 245 adventure positive
## 246 246 adventurous positive
## 247 247 adversary anger
## 248 248 adversary negative
## 249 249 adverse anger
## 250 250 adverse disgust
## 251 251 adverse fear
## 252 252 adverse negative
## 253 253 adverse sadness
## 254 254 adversity anger
## 255 255 adversity fear
## 256 256 adversity negative
## 257 257 adversity sadness
## 258 258 advice trust
## 259 259 advisable positive
## 260 260 advisable trust
## 261 261 advise positive
## 262 262 advise trust
## 263 263 advised trust
## 264 264 adviser positive
## 265 265 adviser trust
## 266 266 advocacy anger
## 267 267 advocacy anticipation
## 268 268 advocacy joy
## 269 269 advocacy positive
## 270 270 advocacy trust
## 271 271 advocate trust
## 272 272 aesthetic positive
## 273 273 aesthetics joy
## 274 274 aesthetics positive
## 275 275 affable positive
## 276 276 affection joy
## 277 277 affection positive
## 278 278 affection trust
## 279 279 affiliated positive
## 280 280 affirm positive
## 281 281 affirm trust
## 282 282 affirmation positive
## 283 283 affirmative positive
## 284 284 affirmatively positive
## 285 285 affirmatively trust
## 286 286 afflict fear
## 287 287 afflict negative
## 288 288 afflict sadness
## 289 289 afflicted negative
## 290 290 affliction disgust
## 291 291 affliction fear
## 292 292 affliction negative
## 293 293 affliction sadness
## 294 294 affluence joy
## 295 295 affluence positive
## 296 296 affluent positive
## 297 297 afford positive
## 298 298 affront anger
## 299 299 affront disgust
## 300 300 affront fear
## 301 301 affront negative
## 302 302 affront sadness
## 303 303 affront surprise
## 304 304 afraid fear
## 305 305 afraid negative
## 306 306 aftermath anger
## 307 307 aftermath disgust
## 308 308 aftermath fear
## 309 309 aftermath negative
## 310 310 aftermath sadness
## 311 311 aftertaste negative
## 312 312 aga fear
## 313 313 aga positive
## 314 314 aga trust
## 315 315 aggravated anger
## 316 316 aggravated negative
## 317 317 aggravating anger
## 318 318 aggravating negative
## 319 319 aggravating sadness
## 320 320 aggravation anger
## 321 321 aggravation disgust
## 322 322 aggravation negative
## 323 323 aggression anger
## 324 324 aggression fear
## 325 325 aggression negative
## 326 326 aggressive anger
## 327 327 aggressive fear
## 328 328 aggressive negative
## 329 329 aggressor anger
## 330 330 aggressor fear
## 331 331 aggressor negative
## 332 332 aghast disgust
## 333 333 aghast fear
## 334 334 aghast negative
## 335 335 aghast surprise
## 336 336 agile positive
## 337 337 agility positive
## 338 338 agitated anger
## 339 339 agitated negative
## 340 340 agitation anger
## 341 341 agitation negative
## 342 342 agonizing fear
## 343 343 agonizing negative
## 344 344 agony anger
## 345 345 agony fear
## 346 346 agony negative
## 347 347 agony sadness
## 348 348 agree positive
## 349 349 agreeable positive
## 350 350 agreeable trust
## 351 351 agreed positive
## 352 352 agreed trust
## 353 353 agreeing positive
## 354 354 agreeing trust
## 355 355 agreement positive
## 356 356 agreement trust
## 357 357 agriculture positive
## 358 358 aground negative
## 359 359 ahead positive
## 360 360 aid positive
## 361 361 aiding positive
## 362 362 ail negative
## 363 363 ail sadness
## 364 364 ailing fear
## 365 365 ailing negative
## 366 366 ailing sadness
## 367 367 aimless negative
## 368 368 airport anticipation
## 369 369 airs disgust
## 370 370 airs negative
## 371 371 akin trust
## 372 372 alabaster positive
## 373 373 alarm fear
## 374 374 alarm negative
## 375 375 alarm surprise
## 376 376 alarming fear
## 377 377 alarming negative
## 378 378 alarming surprise
## 379 379 alb trust
## 380 380 alcoholism anger
## 381 381 alcoholism disgust
## 382 382 alcoholism fear
## 383 383 alcoholism negative
## 384 384 alcoholism sadness
## 385 385 alertness anticipation
## 386 386 alertness fear
## 387 387 alertness positive
## 388 388 alertness surprise
## 389 389 alerts anticipation
## 390 390 alerts fear
## 391 391 alerts surprise
## 392 392 alien disgust
## 393 393 alien fear
## 394 394 alien negative
## 395 395 alienate anger
## 396 396 alienate disgust
## 397 397 alienate negative
## 398 398 alienated negative
## 399 399 alienated sadness
## 400 400 alienation anger
## 401 401 alienation disgust
## 402 402 alienation fear
## 403 403 alienation negative
## 404 404 alienation sadness
## 405 405 alimentation positive
## 406 406 alimony negative
## 407 407 alive anticipation
## 408 408 alive joy
## 409 409 alive positive
## 410 410 alive trust
## 411 411 allay positive
## 412 412 allegation anger
## 413 413 allegation negative
## 414 414 allege negative
## 415 415 allegiance positive
## 416 416 allegiance trust
## 417 417 allegro positive
## 418 418 alleviate positive
## 419 419 alleviation positive
## 420 420 alliance trust
## 421 421 allied positive
## 422 422 allied trust
## 423 423 allowable positive
## 424 424 allure anticipation
## 425 425 allure joy
## 426 426 allure positive
## 427 427 allure surprise
## 428 428 alluring positive
## 429 429 ally positive
## 430 430 ally trust
## 431 431 almighty positive
## 432 432 aloha anticipation
## 433 433 aloha joy
## 434 434 aloha positive
## 435 435 aloof negative
## 436 436 altercation anger
## 437 437 altercation negative
## 438 438 amaze surprise
## 439 439 amazingly joy
## 440 440 amazingly positive
## 441 441 amazingly surprise
## 442 442 ambassador positive
## 443 443 ambassador trust
## 444 444 ambiguous negative
## 445 445 ambition anticipation
## 446 446 ambition joy
## 447 447 ambition positive
## 448 448 ambition trust
## 449 449 ambulance fear
## 450 450 ambulance trust
## 451 451 ambush anger
## 452 452 ambush fear
## 453 453 ambush negative
## 454 454 ambush surprise
## 455 455 ameliorate positive
## 456 456 amen joy
## 457 457 amen positive
## 458 458 amen trust
## 459 459 amenable positive
## 460 460 amend positive
## 461 461 amends positive
## 462 462 amenity positive
## 463 463 amiable positive
## 464 464 amicable joy
## 465 465 amicable positive
## 466 466 ammonia disgust
## 467 467 amnesia negative
## 468 468 amnesty joy
## 469 469 amnesty positive
## 470 470 amortization trust
## 471 471 amour anticipation
## 472 472 amour joy
## 473 473 amour positive
## 474 474 amour trust
## 475 475 amphetamines disgust
## 476 476 amphetamines negative
## 477 477 amuse joy
## 478 478 amuse positive
## 479 479 amused joy
## 480 480 amused positive
## 481 481 amusement joy
## 482 482 amusement positive
## 483 483 amusing joy
## 484 484 amusing positive
## 485 485 anaconda disgust
## 486 486 anaconda fear
## 487 487 anaconda negative
## 488 488 anal negative
## 489 489 analyst anticipation
## 490 490 analyst positive
## 491 491 analyst trust
## 492 492 anarchism anger
## 493 493 anarchism fear
## 494 494 anarchism negative
## 495 495 anarchist anger
## 496 496 anarchist fear
## 497 497 anarchist negative
## 498 498 anarchy anger
## 499 499 anarchy fear
## 500 500 anarchy negative
## 501 501 anathema anger
## 502 502 anathema disgust
## 503 503 anathema fear
## 504 504 anathema negative
## 505 505 anathema sadness
## 506 506 ancestral trust
## 507 507 anchor positive
## 508 508 anchorage positive
## 509 509 anchorage sadness
## 510 510 ancient negative
## 511 511 angel anticipation
## 512 512 angel joy
## 513 513 angel positive
## 514 514 angel surprise
## 515 515 angel trust
## 516 516 angelic joy
## 517 517 angelic positive
## 518 518 angelic trust
## 519 519 anger anger
## 520 520 anger negative
## 521 521 angina fear
## 522 522 angina negative
## 523 523 angling anticipation
## 524 524 angling negative
## 525 525 angry anger
## 526 526 angry disgust
## 527 527 angry negative
## 528 528 anguish anger
## 529 529 anguish fear
## 530 530 anguish negative
## 531 531 anguish sadness
## 532 532 animate positive
## 533 533 animated joy
## 534 534 animated positive
## 535 535 animosity anger
## 536 536 animosity disgust
## 537 537 animosity fear
## 538 538 animosity negative
## 539 539 animosity sadness
## 540 540 animus anger
## 541 541 animus negative
## 542 542 annihilate anger
## 543 543 annihilate fear
## 544 544 annihilate negative
## 545 545 annihilated anger
## 546 546 annihilated fear
## 547 547 annihilated negative
## 548 548 annihilated sadness
## 549 549 annihilation anger
## 550 550 annihilation fear
## 551 551 annihilation negative
## 552 552 annihilation sadness
## 553 553 announcement anticipation
## 554 554 annoy anger
## 555 555 annoy disgust
## 556 556 annoy negative
## 557 557 annoyance anger
## 558 558 annoyance disgust
## 559 559 annoyance negative
## 560 560 annoying anger
## 561 561 annoying negative
## 562 562 annul negative
## 563 563 annulment negative
## 564 564 annulment sadness
## 565 565 anomaly fear
## 566 566 anomaly negative
## 567 567 anomaly surprise
## 568 568 anonymous negative
## 569 569 answerable trust
## 570 570 antagonism anger
## 571 571 antagonism negative
## 572 572 antagonist anger
## 573 573 antagonist negative
## 574 574 antagonistic anger
## 575 575 antagonistic disgust
## 576 576 antagonistic negative
## 577 577 anthrax disgust
## 578 578 anthrax fear
## 579 579 anthrax negative
## 580 580 anthrax sadness
## 581 581 antibiotics positive
## 582 582 antichrist anger
## 583 583 antichrist disgust
## 584 584 antichrist fear
## 585 585 antichrist negative
## 586 586 anticipation anticipation
## 587 587 anticipatory anticipation
## 588 588 antidote anticipation
## 589 589 antidote positive
## 590 590 antidote trust
## 591 591 antifungal positive
## 592 592 antifungal trust
## 593 593 antipathy anger
## 594 594 antipathy disgust
## 595 595 antipathy negative
## 596 596 antiquated negative
## 597 597 antique positive
## 598 598 antiseptic positive
## 599 599 antiseptic trust
## 600 600 antisocial anger
## 601 601 antisocial disgust
## 602 602 antisocial fear
## 603 603 antisocial negative
## 604 604 antisocial sadness
## 605 605 antithesis anger
## 606 606 antithesis negative
## 607 607 anxiety anger
## 608 608 anxiety anticipation
## 609 609 anxiety fear
## 610 610 anxiety negative
## 611 611 anxiety sadness
## 612 612 anxious anticipation
## 613 613 anxious fear
## 614 614 anxious negative
## 615 615 apache fear
## 616 616 apache negative
## 617 617 apathetic negative
## 618 618 apathetic sadness
## 619 619 apathy negative
## 620 620 apathy sadness
## 621 621 aphid disgust
## 622 622 aphid negative
## 623 623 aplomb positive
## 624 624 apologetic positive
## 625 625 apologetic trust
## 626 626 apologize positive
## 627 627 apologize sadness
## 628 628 apologize trust
## 629 629 apology positive
## 630 630 apostle positive
## 631 631 apostle trust
## 632 632 apostolic trust
## 633 633 appalling disgust
## 634 634 appalling fear
## 635 635 appalling negative
## 636 636 apparition fear
## 637 637 apparition surprise
## 638 638 appeal anticipation
## 639 639 appendicitis fear
## 640 640 appendicitis negative
## 641 641 appendicitis sadness
## 642 642 applause joy
## 643 643 applause positive
## 644 644 applause surprise
## 645 645 applause trust
## 646 646 applicant anticipation
## 647 647 appreciation joy
## 648 648 appreciation positive
## 649 649 appreciation trust
## 650 650 apprehend fear
## 651 651 apprehension fear
## 652 652 apprehension negative
## 653 653 apprehensive anticipation
## 654 654 apprehensive fear
## 655 655 apprehensive negative
## 656 656 apprentice trust
## 657 657 approaching anticipation
## 658 658 approbation positive
## 659 659 approbation trust
## 660 660 appropriation negative
## 661 661 approval positive
## 662 662 approve joy
## 663 663 approve positive
## 664 664 approve trust
## 665 665 approving positive
## 666 666 apt positive
## 667 667 aptitude positive
## 668 668 arbiter trust
## 669 669 arbitration anticipation
## 670 670 arbitrator trust
## 671 671 archaeology anticipation
## 672 672 archaeology positive
## 673 673 archaic negative
## 674 674 architecture trust
## 675 675 ardent anticipation
## 676 676 ardent joy
## 677 677 ardent positive
## 678 678 ardor positive
## 679 679 arduous negative
## 680 680 argue anger
## 681 681 argue negative
## 682 682 argument anger
## 683 683 argument negative
## 684 684 argumentation anger
## 685 685 argumentative negative
## 686 686 arguments anger
## 687 687 arid negative
## 688 688 arid sadness
## 689 689 aristocracy positive
## 690 690 aristocratic positive
## 691 691 armament anger
## 692 692 armament fear
## 693 693 armaments fear
## 694 694 armaments negative
## 695 695 armed anger
## 696 696 armed fear
## 697 697 armed negative
## 698 698 armed positive
## 699 699 armor fear
## 700 700 armor positive
## 701 701 armor trust
## 702 702 armored fear
## 703 703 armory trust
## 704 704 aroma positive
## 705 705 arouse anticipation
## 706 706 arouse positive
## 707 707 arraignment anger
## 708 708 arraignment fear
## 709 709 arraignment negative
## 710 710 arraignment sadness
## 711 711 array positive
## 712 712 arrears negative
## 713 713 arrest negative
## 714 714 arrival anticipation
## 715 715 arrive anticipation
## 716 716 arrogance negative
## 717 717 arrogant anger
## 718 718 arrogant disgust
## 719 719 arrogant negative
## 720 720 arsenic disgust
## 721 721 arsenic fear
## 722 722 arsenic negative
## 723 723 arsenic sadness
## 724 724 arson anger
## 725 725 arson fear
## 726 726 arson negative
## 727 727 art anticipation
## 728 728 art joy
## 729 729 art positive
## 730 730 art sadness
## 731 731 art surprise
## 732 732 articulate positive
## 733 733 articulation positive
## 734 734 artillery fear
## 735 735 artillery negative
## 736 736 artisan positive
## 737 737 artiste positive
## 738 738 artistic positive
## 739 739 ascendancy positive
## 740 740 ascent positive
## 741 741 ash negative
## 742 742 ashamed disgust
## 743 743 ashamed negative
## 744 744 ashamed sadness
## 745 745 ashes negative
## 746 746 ashes sadness
## 747 747 asp fear
## 748 748 aspiration anticipation
## 749 749 aspiration joy
## 750 750 aspiration positive
## 751 751 aspiration surprise
## 752 752 aspiration trust
## 753 753 aspire anticipation
## 754 754 aspire joy
## 755 755 aspire positive
## 756 756 aspiring anticipation
## 757 757 aspiring joy
## 758 758 aspiring positive
## 759 759 aspiring trust
## 760 760 ass negative
## 761 761 assail anger
## 762 762 assail fear
## 763 763 assail negative
## 764 764 assail surprise
## 765 765 assailant anger
## 766 766 assailant fear
## 767 767 assailant negative
## 768 768 assailant sadness
## 769 769 assassin anger
## 770 770 assassin fear
## 771 771 assassin negative
## 772 772 assassin sadness
## 773 773 assassinate anger
## 774 774 assassinate fear
## 775 775 assassinate negative
## 776 776 assassination anger
## 777 777 assassination fear
## 778 778 assassination negative
## 779 779 assassination sadness
## 780 780 assault anger
## 781 781 assault fear
## 782 782 assault negative
## 783 783 assembly positive
## 784 784 assembly trust
## 785 785 assent positive
## 786 786 asserting positive
## 787 787 asserting trust
## 788 788 assessment surprise
## 789 789 assessment trust
## 790 790 assessor trust
## 791 791 assets positive
## 792 792 asshole anger
## 793 793 asshole disgust
## 794 794 asshole negative
## 795 795 assignee trust
## 796 796 assist positive
## 797 797 assist trust
## 798 798 assistance positive
## 799 799 associate positive
## 800 800 associate trust
## 801 801 association trust
## 802 802 assuage positive
## 803 803 assurance positive
## 804 804 assurance trust
## 805 805 assure trust
## 806 806 assured positive
## 807 807 assured trust
## 808 808 assuredly trust
## 809 809 astonishingly positive
## 810 810 astonishingly surprise
## 811 811 astonishment joy
## 812 812 astonishment positive
## 813 813 astonishment surprise
## 814 814 astray fear
## 815 815 astray negative
## 816 816 astringent negative
## 817 817 astrologer anticipation
## 818 818 astrologer positive
## 819 819 astronaut positive
## 820 820 astronomer anticipation
## 821 821 astronomer positive
## 822 822 astute positive
## 823 823 asylum fear
## 824 824 asylum negative
## 825 825 asymmetry disgust
## 826 826 atheism negative
## 827 827 atherosclerosis fear
## 828 828 atherosclerosis negative
## 829 829 atherosclerosis sadness
## 830 830 athlete positive
## 831 831 athletic positive
## 832 832 atom positive
## 833 833 atone anticipation
## 834 834 atone joy
## 835 835 atone positive
## 836 836 atone trust
## 837 837 atonement positive
## 838 838 atrocious anger
## 839 839 atrocious disgust
## 840 840 atrocious negative
## 841 841 atrocity anger
## 842 842 atrocity disgust
## 843 843 atrocity fear
## 844 844 atrocity negative
## 845 845 atrocity sadness
## 846 846 atrophy disgust
## 847 847 atrophy fear
## 848 848 atrophy negative
## 849 849 atrophy sadness
## 850 850 attachment positive
## 851 851 attack anger
## 852 852 attack fear
## 853 853 attack negative
## 854 854 attacking anger
## 855 855 attacking disgust
## 856 856 attacking fear
## 857 857 attacking negative
## 858 858 attacking sadness
## 859 859 attacking surprise
## 860 860 attainable anticipation
## 861 861 attainable positive
## 862 862 attainment positive
## 863 863 attempt anticipation
## 864 864 attendance anticipation
## 865 865 attendant positive
## 866 866 attendant trust
## 867 867 attention positive
## 868 868 attentive positive
## 869 869 attentive trust
## 870 870 attenuated negative
## 871 871 attenuation negative
## 872 872 attenuation sadness
## 873 873 attest positive
## 874 874 attest trust
## 875 875 attestation trust
## 876 876 attorney anger
## 877 877 attorney fear
## 878 878 attorney positive
## 879 879 attorney trust
## 880 880 attraction positive
## 881 881 attractiveness positive
## 882 882 auction anticipation
## 883 883 audacity negative
## 884 884 audience anticipation
## 885 885 auditor fear
## 886 886 auditor trust
## 887 887 augment positive
## 888 888 august positive
## 889 889 aunt positive
## 890 890 aunt trust
## 891 891 aura positive
## 892 892 auspicious anticipation
## 893 893 auspicious joy
## 894 894 auspicious positive
## 895 895 austere fear
## 896 896 austere negative
## 897 897 austere sadness
## 898 898 austerity negative
## 899 899 authentic joy
## 900 900 authentic positive
## 901 901 authentic trust
## 902 902 authenticate trust
## 903 903 authentication trust
## 904 904 authenticity positive
## 905 905 authenticity trust
## 906 906 author positive
## 907 907 author trust
## 908 908 authoritative positive
## 909 909 authoritative trust
## 910 910 authority positive
## 911 911 authority trust
## 912 912 authorization positive
## 913 913 authorization trust
## 914 914 authorize trust
## 915 915 authorized positive
## 916 916 autocratic negative
## 917 917 automatic trust
## 918 918 autopsy disgust
## 919 919 autopsy fear
## 920 920 autopsy negative
## 921 921 autopsy sadness
## 922 922 avalanche fear
## 923 923 avalanche negative
## 924 924 avalanche sadness
## 925 925 avalanche surprise
## 926 926 avarice anger
## 927 927 avarice disgust
## 928 928 avarice negative
## 929 929 avatar positive
## 930 930 avenger anger
## 931 931 avenger negative
## 932 932 averse anger
## 933 933 averse disgust
## 934 934 averse fear
## 935 935 averse negative
## 936 936 aversion anger
## 937 937 aversion disgust
## 938 938 aversion fear
## 939 939 aversion negative
## 940 940 avoid fear
## 941 941 avoid negative
## 942 942 avoidance fear
## 943 943 avoidance negative
## 944 944 avoiding fear
## 945 945 await anticipation
## 946 946 award anticipation
## 947 947 award joy
## 948 948 award positive
## 949 949 award surprise
## 950 950 award trust
## 951 951 awful anger
## 952 952 awful disgust
## 953 953 awful fear
## 954 954 awful negative
## 955 955 awful sadness
## 956 956 awkwardness disgust
## 957 957 awkwardness negative
## 958 958 awry negative
## 959 959 axiom trust
## 960 960 axiomatic trust
## 961 961 ay positive
## 962 962 aye positive
## 963 963 babble negative
## 964 964 babbling negative
## 965 965 baboon disgust
## 966 966 baboon negative
## 967 967 baby joy
## 968 968 baby positive
## 969 969 babysitter trust
## 970 970 baccalaureate positive
## 971 971 backbone anger
## 972 972 backbone positive
## 973 973 backbone trust
## 974 974 backer trust
## 975 975 backward negative
## 976 976 backwards disgust
## 977 977 backwards negative
## 978 978 backwater negative
## 979 979 backwater sadness
## 980 980 bacteria disgust
## 981 981 bacteria fear
## 982 982 bacteria negative
## 983 983 bacteria sadness
## 984 984 bacterium disgust
## 985 985 bacterium fear
## 986 986 bacterium negative
## 987 987 bad anger
## 988 988 bad disgust
## 989 989 bad fear
## 990 990 bad negative
## 991 991 bad sadness
## 992 992 badge trust
## 993 993 badger anger
## 994 994 badger negative
## 995 995 badly negative
## 996 996 badly sadness
## 997 997 badness anger
## 998 998 badness disgust
## 999 999 badness fear
## 1000 1000 badness negative
## 1001 1001 bailiff fear
## 1002 1002 bailiff negative
## 1003 1003 bailiff trust
## 1004 1004 bait fear
## 1005 1005 bait negative
## 1006 1006 bait trust
## 1007 1007 balance positive
## 1008 1008 balanced positive
## 1009 1009 bale fear
## 1010 1010 bale negative
## 1011 1011 balk negative
## 1012 1012 ballad positive
## 1013 1013 ballet positive
## 1014 1014 ballot anticipation
## 1015 1015 ballot positive
## 1016 1016 ballot trust
## 1017 1017 balm anticipation
## 1018 1018 balm joy
## 1019 1019 balm negative
## 1020 1020 balm positive
## 1021 1021 balsam positive
## 1022 1022 ban negative
## 1023 1023 bandit negative
## 1024 1024 bane anger
## 1025 1025 bane disgust
## 1026 1026 bane fear
## 1027 1027 bane negative
## 1028 1028 bang anger
## 1029 1029 bang disgust
## 1030 1030 bang fear
## 1031 1031 bang negative
## 1032 1032 bang sadness
## 1033 1033 bang surprise
## 1034 1034 banger anger
## 1035 1035 banger anticipation
## 1036 1036 banger fear
## 1037 1037 banger negative
## 1038 1038 banger surprise
## 1039 1039 banish anger
## 1040 1040 banish disgust
## 1041 1041 banish fear
## 1042 1042 banish negative
## 1043 1043 banish sadness
## 1044 1044 banished anger
## 1045 1045 banished fear
## 1046 1046 banished negative
## 1047 1047 banished sadness
## 1048 1048 banishment anger
## 1049 1049 banishment disgust
## 1050 1050 banishment negative
## 1051 1051 banishment sadness
## 1052 1052 bank trust
## 1053 1053 banker trust
## 1054 1054 bankrupt fear
## 1055 1055 bankrupt negative
## 1056 1056 bankrupt sadness
## 1057 1057 bankruptcy anger
## 1058 1058 bankruptcy disgust
## 1059 1059 bankruptcy fear
## 1060 1060 bankruptcy negative
## 1061 1061 bankruptcy sadness
## 1062 1062 banquet anticipation
## 1063 1063 banquet joy
## 1064 1064 banquet positive
## 1065 1065 banshee anger
## 1066 1066 banshee disgust
## 1067 1067 banshee fear
## 1068 1068 banshee negative
## 1069 1069 banshee sadness
## 1070 1070 baptism positive
## 1071 1071 baptismal joy
## 1072 1072 baptismal positive
## 1073 1073 barb anger
## 1074 1074 barb negative
## 1075 1075 barbarian fear
## 1076 1076 barbarian negative
## 1077 1077 barbaric anger
## 1078 1078 barbaric disgust
## 1079 1079 barbaric fear
## 1080 1080 barbaric negative
## 1081 1081 barbarism negative
## 1082 1082 bard positive
## 1083 1083 barf disgust
## 1084 1084 bargain positive
## 1085 1085 bargain trust
## 1086 1086 bark anger
## 1087 1087 bark negative
## 1088 1088 barred negative
## 1089 1089 barren negative
## 1090 1090 barren sadness
## 1091 1091 barricade fear
## 1092 1092 barricade negative
## 1093 1093 barrier anger
## 1094 1094 barrier negative
## 1095 1095 barrow disgust
## 1096 1096 bartender trust
## 1097 1097 barter trust
## 1098 1098 base trust
## 1099 1099 baseless negative
## 1100 1100 basketball anticipation
## 1101 1101 basketball joy
## 1102 1102 basketball positive
## 1103 1103 bastard disgust
## 1104 1104 bastard negative
## 1105 1105 bastard sadness
## 1106 1106 bastion anger
## 1107 1107 bastion positive
## 1108 1108 bath positive
## 1109 1109 battalion anger
## 1110 1110 batter anger
## 1111 1111 batter fear
## 1112 1112 batter negative
## 1113 1113 battered fear
## 1114 1114 battered negative
## 1115 1115 battered sadness
## 1116 1116 battery anger
## 1117 1117 battery negative
## 1118 1118 battle anger
## 1119 1119 battle negative
## 1120 1120 battled anger
## 1121 1121 battled fear
## 1122 1122 battled negative
## 1123 1123 battled sadness
## 1124 1124 battlefield fear
## 1125 1125 battlefield negative
## 1126 1126 bawdy negative
## 1127 1127 bayonet anger
## 1128 1128 bayonet fear
## 1129 1129 bayonet negative
## 1130 1130 beach joy
## 1131 1131 beam joy
## 1132 1132 beam positive
## 1133 1133 beaming anticipation
## 1134 1134 beaming joy
## 1135 1135 beaming positive
## 1136 1136 bear anger
## 1137 1137 bear fear
## 1138 1138 bearer negative
## 1139 1139 bearish anger
## 1140 1140 bearish fear
## 1141 1141 beast anger
## 1142 1142 beast fear
## 1143 1143 beast negative
## 1144 1144 beastly disgust
## 1145 1145 beastly fear
## 1146 1146 beastly negative
## 1147 1147 beating anger
## 1148 1148 beating fear
## 1149 1149 beating negative
## 1150 1150 beating sadness
## 1151 1151 beautification joy
## 1152 1152 beautification positive
## 1153 1153 beautification trust
## 1154 1154 beautiful joy
## 1155 1155 beautiful positive
## 1156 1156 beautify joy
## 1157 1157 beautify positive
## 1158 1158 beauty joy
## 1159 1159 beauty positive
## 1160 1160 bedrock positive
## 1161 1161 bedrock trust
## 1162 1162 bee anger
## 1163 1163 bee fear
## 1164 1164 beer joy
## 1165 1165 beer positive
## 1166 1166 befall negative
## 1167 1167 befitting positive
## 1168 1168 befriend joy
## 1169 1169 befriend positive
## 1170 1170 befriend trust
## 1171 1171 beg negative
## 1172 1172 beg sadness
## 1173 1173 beggar negative
## 1174 1174 beggar sadness
## 1175 1175 begging negative
## 1176 1176 begun anticipation
## 1177 1177 behemoth fear
## 1178 1178 behemoth negative
## 1179 1179 beholden negative
## 1180 1180 belated negative
## 1181 1181 believed trust
## 1182 1182 believer trust
## 1183 1183 believing positive
## 1184 1184 believing trust
## 1185 1185 belittle anger
## 1186 1186 belittle disgust
## 1187 1187 belittle fear
## 1188 1188 belittle negative
## 1189 1189 belittle sadness
## 1190 1190 belligerent anger
## 1191 1191 belligerent fear
## 1192 1192 belligerent negative
## 1193 1193 bellows anger
## 1194 1194 belt anger
## 1195 1195 belt fear
## 1196 1196 belt negative
## 1197 1197 bender negative
## 1198 1198 benefactor positive
## 1199 1199 benefactor trust
## 1200 1200 beneficial positive
## 1201 1201 benefit positive
## 1202 1202 benevolence joy
## 1203 1203 benevolence positive
## 1204 1204 benevolence trust
## 1205 1205 benign joy
## 1206 1206 benign positive
## 1207 1207 bequest trust
## 1208 1208 bereaved negative
## 1209 1209 bereaved sadness
## 1210 1210 bereavement negative
## 1211 1211 bereavement sadness
## 1212 1212 bereft negative
## 1213 1213 berserk anger
## 1214 1214 berserk negative
## 1215 1215 berth positive
## 1216 1216 bestial disgust
## 1217 1217 bestial fear
## 1218 1218 bestial negative
## 1219 1219 betray anger
## 1220 1220 betray disgust
## 1221 1221 betray negative
## 1222 1222 betray sadness
## 1223 1223 betray surprise
## 1224 1224 betrayal anger
## 1225 1225 betrayal disgust
## 1226 1226 betrayal negative
## 1227 1227 betrayal sadness
## 1228 1228 betrothed anticipation
## 1229 1229 betrothed joy
## 1230 1230 betrothed positive
## 1231 1231 betrothed trust
## 1232 1232 betterment positive
## 1233 1233 beverage positive
## 1234 1234 beware anticipation
## 1235 1235 beware fear
## 1236 1236 beware negative
## 1237 1237 bewildered fear
## 1238 1238 bewildered negative
## 1239 1239 bewildered surprise
## 1240 1240 bewilderment fear
## 1241 1241 bewilderment surprise
## 1242 1242 bias anger
## 1243 1243 bias negative
## 1244 1244 biased negative
## 1245 1245 biblical positive
## 1246 1246 bickering anger
## 1247 1247 bickering disgust
## 1248 1248 bickering negative
## 1249 1249 biennial anticipation
## 1250 1250 bier fear
## 1251 1251 bier negative
## 1252 1252 bier sadness
## 1253 1253 bigot anger
## 1254 1254 bigot disgust
## 1255 1255 bigot fear
## 1256 1256 bigot negative
## 1257 1257 bigoted anger
## 1258 1258 bigoted disgust
## 1259 1259 bigoted fear
## 1260 1260 bigoted negative
## 1261 1261 bigoted sadness
## 1262 1262 bile anger
## 1263 1263 bile disgust
## 1264 1264 bile negative
## 1265 1265 bilingual positive
## 1266 1266 biopsy fear
## 1267 1267 biopsy negative
## 1268 1268 birch anger
## 1269 1269 birch disgust
## 1270 1270 birch fear
## 1271 1271 birch negative
## 1272 1272 birth anticipation
## 1273 1273 birth fear
## 1274 1274 birth joy
## 1275 1275 birth positive
## 1276 1276 birth trust
## 1277 1277 birthday anticipation
## 1278 1278 birthday joy
## 1279 1279 birthday positive
## 1280 1280 birthday surprise
## 1281 1281 birthplace anger
## 1282 1282 birthplace negative
## 1283 1283 bitch anger
## 1284 1284 bitch disgust
## 1285 1285 bitch fear
## 1286 1286 bitch negative
## 1287 1287 bitch sadness
## 1288 1288 bite negative
## 1289 1289 bitterly anger
## 1290 1290 bitterly disgust
## 1291 1291 bitterly negative
## 1292 1292 bitterly sadness
## 1293 1293 bitterness anger
## 1294 1294 bitterness disgust
## 1295 1295 bitterness negative
## 1296 1296 bitterness sadness
## 1297 1297 bizarre negative
## 1298 1298 bizarre surprise
## 1299 1299 blackjack negative
## 1300 1300 blackmail anger
## 1301 1301 blackmail fear
## 1302 1302 blackmail negative
## 1303 1303 blackness fear
## 1304 1304 blackness negative
## 1305 1305 blackness sadness
## 1306 1306 blame anger
## 1307 1307 blame disgust
## 1308 1308 blame negative
## 1309 1309 blameless positive
## 1310 1310 bland negative
## 1311 1311 blanket trust
## 1312 1312 blasphemous anger
## 1313 1313 blasphemous disgust
## 1314 1314 blasphemous negative
## 1315 1315 blasphemy anger
## 1316 1316 blasphemy negative
## 1317 1317 blast anger
## 1318 1318 blast fear
## 1319 1319 blast negative
## 1320 1320 blast surprise
## 1321 1321 blatant anger
## 1322 1322 blatant disgust
## 1323 1323 blatant negative
## 1324 1324 blather negative
## 1325 1325 blaze anger
## 1326 1326 blaze negative
## 1327 1327 bleak negative
## 1328 1328 bleak sadness
## 1329 1329 bleeding disgust
## 1330 1330 bleeding fear
## 1331 1331 bleeding negative
## 1332 1332 bleeding sadness
## 1333 1333 blemish anger
## 1334 1334 blemish disgust
## 1335 1335 blemish fear
## 1336 1336 blemish negative
## 1337 1337 blemish sadness
## 1338 1338 bless anticipation
## 1339 1339 bless joy
## 1340 1340 bless positive
## 1341 1341 bless trust
## 1342 1342 blessed joy
## 1343 1343 blessed positive
## 1344 1344 blessing anticipation
## 1345 1345 blessing joy
## 1346 1346 blessing positive
## 1347 1347 blessing trust
## 1348 1348 blessings anticipation
## 1349 1349 blessings joy
## 1350 1350 blessings positive
## 1351 1351 blessings surprise
## 1352 1352 blessings trust
## 1353 1353 blight disgust
## 1354 1354 blight fear
## 1355 1355 blight negative
## 1356 1356 blight sadness
## 1357 1357 blighted disgust
## 1358 1358 blighted negative
## 1359 1359 blighted sadness
## 1360 1360 blinded negative
## 1361 1361 blindfold anticipation
## 1362 1362 blindfold fear
## 1363 1363 blindfold surprise
## 1364 1364 blindly negative
## 1365 1365 blindly sadness
## 1366 1366 blindness negative
## 1367 1367 blindness sadness
## 1368 1368 bliss joy
## 1369 1369 bliss positive
## 1370 1370 blissful joy
## 1371 1371 blissful positive
## 1372 1372 blister disgust
## 1373 1373 blister negative
## 1374 1374 blitz surprise
## 1375 1375 bloated disgust
## 1376 1376 bloated negative
## 1377 1377 blob disgust
## 1378 1378 blob fear
## 1379 1379 blob negative
## 1380 1380 blockade anger
## 1381 1381 blockade fear
## 1382 1382 blockade negative
## 1383 1383 blockade sadness
## 1384 1384 bloodless positive
## 1385 1385 bloodshed anger
## 1386 1386 bloodshed disgust
## 1387 1387 bloodshed fear
## 1388 1388 bloodshed negative
## 1389 1389 bloodshed sadness
## 1390 1390 bloodshed surprise
## 1391 1391 bloodthirsty anger
## 1392 1392 bloodthirsty disgust
## 1393 1393 bloodthirsty fear
## 1394 1394 bloodthirsty negative
## 1395 1395 bloody anger
## 1396 1396 bloody disgust
## 1397 1397 bloody fear
## 1398 1398 bloody negative
## 1399 1399 bloody sadness
## 1400 1400 bloom anticipation
## 1401 1401 bloom joy
## 1402 1402 bloom positive
## 1403 1403 bloom trust
## 1404 1404 blossom joy
## 1405 1405 blossom positive
## 1406 1406 blot negative
## 1407 1407 blower negative
## 1408 1408 blowout negative
## 1409 1409 blue sadness
## 1410 1410 blues fear
## 1411 1411 blues negative
## 1412 1412 blues sadness
## 1413 1413 bluff negative
## 1414 1414 blunder disgust
## 1415 1415 blunder negative
## 1416 1416 blunder sadness
## 1417 1417 blur negative
## 1418 1418 blurred negative
## 1419 1419 blush negative
## 1420 1420 board anticipation
## 1421 1421 boast negative
## 1422 1422 boast positive
## 1423 1423 boasting negative
## 1424 1424 bodyguard positive
## 1425 1425 bodyguard trust
## 1426 1426 bog negative
## 1427 1427 bogus anger
## 1428 1428 bogus disgust
## 1429 1429 bogus negative
## 1430 1430 boil disgust
## 1431 1431 boil negative
## 1432 1432 boilerplate negative
## 1433 1433 boisterous anger
## 1434 1434 boisterous anticipation
## 1435 1435 boisterous joy
## 1436 1436 boisterous negative
## 1437 1437 boisterous positive
## 1438 1438 bold positive
## 1439 1439 boldness positive
## 1440 1440 bolster positive
## 1441 1441 bomb anger
## 1442 1442 bomb fear
## 1443 1443 bomb negative
## 1444 1444 bomb sadness
## 1445 1445 bomb surprise
## 1446 1446 bombard anger
## 1447 1447 bombard fear
## 1448 1448 bombard negative
## 1449 1449 bombardment anger
## 1450 1450 bombardment fear
## 1451 1451 bombardment negative
## 1452 1452 bombed disgust
## 1453 1453 bombed negative
## 1454 1454 bomber fear
## 1455 1455 bomber sadness
## 1456 1456 bonanza joy
## 1457 1457 bonanza positive
## 1458 1458 bondage fear
## 1459 1459 bondage negative
## 1460 1460 bondage sadness
## 1461 1461 bonds negative
## 1462 1462 bonne positive
## 1463 1463 bonus anticipation
## 1464 1464 bonus joy
## 1465 1465 bonus positive
## 1466 1466 bonus surprise
## 1467 1467 boo negative
## 1468 1468 booby negative
## 1469 1469 bookish positive
## 1470 1470 bookshop positive
## 1471 1471 bookworm negative
## 1472 1472 bookworm positive
## 1473 1473 boomerang anticipation
## 1474 1474 boomerang trust
## 1475 1475 boon positive
## 1476 1476 booze negative
## 1477 1477 bore negative
## 1478 1478 boredom negative
## 1479 1479 boredom sadness
## 1480 1480 boring negative
## 1481 1481 borrower negative
## 1482 1482 bother negative
## 1483 1483 bothering anger
## 1484 1484 bothering negative
## 1485 1485 bothering sadness
## 1486 1486 bottom negative
## 1487 1487 bottom sadness
## 1488 1488 bottomless fear
## 1489 1489 bound negative
## 1490 1490 bountiful anticipation
## 1491 1491 bountiful joy
## 1492 1492 bountiful positive
## 1493 1493 bounty anticipation
## 1494 1494 bounty joy
## 1495 1495 bounty positive
## 1496 1496 bounty trust
## 1497 1497 bouquet joy
## 1498 1498 bouquet positive
## 1499 1499 bouquet trust
## 1500 1500 bout anger
## 1501 1501 bout negative
## 1502 1502 bovine disgust
## 1503 1503 bovine negative
## 1504 1504 bowels disgust
## 1505 1505 boxing anger
## 1506 1506 boycott negative
## 1507 1507 brag negative
## 1508 1508 brains positive
## 1509 1509 bran disgust
## 1510 1510 brandy negative
## 1511 1511 bravado negative
## 1512 1512 bravery positive
## 1513 1513 brawl anger
## 1514 1514 brawl disgust
## 1515 1515 brawl fear
## 1516 1516 brawl negative
## 1517 1517 brazen anger
## 1518 1518 brazen negative
## 1519 1519 breach negative
## 1520 1520 break surprise
## 1521 1521 breakdown negative
## 1522 1522 breakfast positive
## 1523 1523 breakneck negative
## 1524 1524 breakup negative
## 1525 1525 breakup sadness
## 1526 1526 bribe negative
## 1527 1527 bribery disgust
## 1528 1528 bribery negative
## 1529 1529 bridal anticipation
## 1530 1530 bridal joy
## 1531 1531 bridal positive
## 1532 1532 bridal trust
## 1533 1533 bride anticipation
## 1534 1534 bride joy
## 1535 1535 bride positive
## 1536 1536 bride trust
## 1537 1537 bridegroom anticipation
## 1538 1538 bridegroom joy
## 1539 1539 bridegroom positive
## 1540 1540 bridegroom trust
## 1541 1541 bridesmaid joy
## 1542 1542 bridesmaid positive
## 1543 1543 bridesmaid trust
## 1544 1544 brigade fear
## 1545 1545 brigade negative
## 1546 1546 brighten joy
## 1547 1547 brighten positive
## 1548 1548 brighten surprise
## 1549 1549 brighten trust
## 1550 1550 brightness positive
## 1551 1551 brilliant anticipation
## 1552 1552 brilliant joy
## 1553 1553 brilliant positive
## 1554 1554 brilliant trust
## 1555 1555 brimstone anger
## 1556 1556 brimstone fear
## 1557 1557 brimstone negative
## 1558 1558 bristle negative
## 1559 1559 broadside anticipation
## 1560 1560 broadside negative
## 1561 1561 brocade positive
## 1562 1562 broil anger
## 1563 1563 broil negative
## 1564 1564 broke fear
## 1565 1565 broke negative
## 1566 1566 broke sadness
## 1567 1567 broken anger
## 1568 1568 broken fear
## 1569 1569 broken negative
## 1570 1570 broken sadness
## 1571 1571 brothel disgust
## 1572 1572 brothel negative
## 1573 1573 brother positive
## 1574 1574 brother trust
## 1575 1575 brotherhood positive
## 1576 1576 brotherhood trust
## 1577 1577 brotherly anticipation
## 1578 1578 brotherly joy
## 1579 1579 brotherly positive
## 1580 1580 brotherly trust
## 1581 1581 bruise anticipation
## 1582 1582 bruise negative
## 1583 1583 brunt anger
## 1584 1584 brunt negative
## 1585 1585 brutal anger
## 1586 1586 brutal fear
## 1587 1587 brutal negative
## 1588 1588 brutality anger
## 1589 1589 brutality fear
## 1590 1590 brutality negative
## 1591 1591 brute anger
## 1592 1592 brute fear
## 1593 1593 brute negative
## 1594 1594 brute sadness
## 1595 1595 buck fear
## 1596 1596 buck negative
## 1597 1597 buck positive
## 1598 1598 buck surprise
## 1599 1599 buddy anticipation
## 1600 1600 buddy joy
## 1601 1601 buddy positive
## 1602 1602 buddy trust
## 1603 1603 budget trust
## 1604 1604 buffet anger
## 1605 1605 buffet negative
## 1606 1606 bug disgust
## 1607 1607 bug fear
## 1608 1608 bug negative
## 1609 1609 bugaboo anger
## 1610 1610 bugaboo fear
## 1611 1611 bugaboo negative
## 1612 1612 bugaboo sadness
## 1613 1613 bugle anticipation
## 1614 1614 build positive
## 1615 1615 building positive
## 1616 1616 bulbous negative
## 1617 1617 bulldog positive
## 1618 1618 bulletproof positive
## 1619 1619 bully anger
## 1620 1620 bully fear
## 1621 1621 bully negative
## 1622 1622 bum disgust
## 1623 1623 bum negative
## 1624 1624 bum sadness
## 1625 1625 bummer anger
## 1626 1626 bummer disgust
## 1627 1627 bummer negative
## 1628 1628 bunker fear
## 1629 1629 buoy positive
## 1630 1630 burdensome fear
## 1631 1631 burdensome negative
## 1632 1632 burdensome sadness
## 1633 1633 bureaucracy negative
## 1634 1634 bureaucracy trust
## 1635 1635 bureaucrat disgust
## 1636 1636 bureaucrat negative
## 1637 1637 burglar disgust
## 1638 1638 burglar fear
## 1639 1639 burglar negative
## 1640 1640 burglary negative
## 1641 1641 burial anger
## 1642 1642 burial fear
## 1643 1643 burial negative
## 1644 1644 burial sadness
## 1645 1645 buried fear
## 1646 1646 buried negative
## 1647 1647 buried sadness
## 1648 1648 burke anger
## 1649 1649 burke disgust
## 1650 1650 burke fear
## 1651 1651 burke negative
## 1652 1652 burke sadness
## 1653 1653 burlesque surprise
## 1654 1654 burnt disgust
## 1655 1655 burnt negative
## 1656 1656 bursary trust
## 1657 1657 bury sadness
## 1658 1658 buss joy
## 1659 1659 buss positive
## 1660 1660 busted anger
## 1661 1661 busted fear
## 1662 1662 busted negative
## 1663 1663 butcher anger
## 1664 1664 butcher disgust
## 1665 1665 butcher fear
## 1666 1666 butcher negative
## 1667 1667 butler positive
## 1668 1668 butler trust
## 1669 1669 butt negative
## 1670 1670 buttery positive
## 1671 1671 buxom positive
## 1672 1672 buzz anticipation
## 1673 1673 buzz fear
## 1674 1674 buzz positive
## 1675 1675 buzzed negative
## 1676 1676 bye anticipation
## 1677 1677 bylaw trust
## 1678 1678 cab positive
## 1679 1679 cabal fear
## 1680 1680 cabal negative
## 1681 1681 cabinet positive
## 1682 1682 cabinet trust
## 1683 1683 cable surprise
## 1684 1684 cacophony anger
## 1685 1685 cacophony disgust
## 1686 1686 cacophony negative
## 1687 1687 cad anger
## 1688 1688 cad disgust
## 1689 1689 cad negative
## 1690 1690 cadaver disgust
## 1691 1691 cadaver fear
## 1692 1692 cadaver negative
## 1693 1693 cadaver sadness
## 1694 1694 cadaver surprise
## 1695 1695 cafe positive
## 1696 1696 cage negative
## 1697 1697 cage sadness
## 1698 1698 calamity sadness
## 1699 1699 calculating negative
## 1700 1700 calculation anticipation
## 1701 1701 calculator positive
## 1702 1702 calculator trust
## 1703 1703 calf joy
## 1704 1704 calf positive
## 1705 1705 calf trust
## 1706 1706 callous anger
## 1707 1707 callous disgust
## 1708 1708 callous negative
## 1709 1709 calls anticipation
## 1710 1710 calls negative
## 1711 1711 calls trust
## 1712 1712 calm positive
## 1713 1713 camouflage surprise
## 1714 1714 camouflaged surprise
## 1715 1715 campaigning anger
## 1716 1716 campaigning fear
## 1717 1717 campaigning negative
## 1718 1718 canary positive
## 1719 1719 cancel negative
## 1720 1720 cancel sadness
## 1721 1721 cancer anger
## 1722 1722 cancer disgust
## 1723 1723 cancer fear
## 1724 1724 cancer negative
## 1725 1725 cancer sadness
## 1726 1726 candid anticipation
## 1727 1727 candid joy
## 1728 1728 candid positive
## 1729 1729 candid surprise
## 1730 1730 candid trust
## 1731 1731 candidate positive
## 1732 1732 candied positive
## 1733 1733 cane anger
## 1734 1734 cane fear
## 1735 1735 canker anger
## 1736 1736 canker disgust
## 1737 1737 canker negative
## 1738 1738 cannibal disgust
## 1739 1739 cannibal fear
## 1740 1740 cannibal negative
## 1741 1741 cannibalism disgust
## 1742 1742 cannibalism negative
## 1743 1743 cannon anger
## 1744 1744 cannon fear
## 1745 1745 cannon negative
## 1746 1746 canons trust
## 1747 1747 cap anticipation
## 1748 1748 cap trust
## 1749 1749 capitalist positive
## 1750 1750 captain positive
## 1751 1751 captivate anticipation
## 1752 1752 captivate joy
## 1753 1753 captivate positive
## 1754 1754 captivate surprise
## 1755 1755 captivate trust
## 1756 1756 captivating positive
## 1757 1757 captive fear
## 1758 1758 captive negative
## 1759 1759 captive sadness
## 1760 1760 captivity negative
## 1761 1761 captivity sadness
## 1762 1762 captor fear
## 1763 1763 captor negative
## 1764 1764 capture negative
## 1765 1765 carcass disgust
## 1766 1766 carcass fear
## 1767 1767 carcass negative
## 1768 1768 carcass sadness
## 1769 1769 carcinoma fear
## 1770 1770 carcinoma negative
## 1771 1771 carcinoma sadness
## 1772 1772 cardiomyopathy fear
## 1773 1773 cardiomyopathy negative
## 1774 1774 cardiomyopathy sadness
## 1775 1775 career anticipation
## 1776 1776 career positive
## 1777 1777 careful positive
## 1778 1778 carefully positive
## 1779 1779 carelessness anger
## 1780 1780 carelessness disgust
## 1781 1781 carelessness negative
## 1782 1782 caress positive
## 1783 1783 caretaker positive
## 1784 1784 caretaker trust
## 1785 1785 caricature negative
## 1786 1786 caries disgust
## 1787 1787 caries negative
## 1788 1788 carnage anger
## 1789 1789 carnage disgust
## 1790 1790 carnage fear
## 1791 1791 carnage negative
## 1792 1792 carnage sadness
## 1793 1793 carnage surprise
## 1794 1794 carnal negative
## 1795 1795 carnivorous fear
## 1796 1796 carnivorous negative
## 1797 1797 carol joy
## 1798 1798 carol positive
## 1799 1799 carol trust
## 1800 1800 cartel negative
## 1801 1801 cartridge fear
## 1802 1802 cascade positive
## 1803 1803 case fear
## 1804 1804 case negative
## 1805 1805 case sadness
## 1806 1806 cash anger
## 1807 1807 cash anticipation
## 1808 1808 cash fear
## 1809 1809 cash joy
## 1810 1810 cash positive
## 1811 1811 cash trust
## 1812 1812 cashier trust
## 1813 1813 casket fear
## 1814 1814 casket negative
## 1815 1815 casket sadness
## 1816 1816 caste negative
## 1817 1817 casualty anger
## 1818 1818 casualty fear
## 1819 1819 casualty negative
## 1820 1820 casualty sadness
## 1821 1821 cataract anticipation
## 1822 1822 cataract fear
## 1823 1823 cataract negative
## 1824 1824 cataract sadness
## 1825 1825 catastrophe anger
## 1826 1826 catastrophe disgust
## 1827 1827 catastrophe fear
## 1828 1828 catastrophe negative
## 1829 1829 catastrophe sadness
## 1830 1830 catastrophe surprise
## 1831 1831 catch surprise
## 1832 1832 catechism disgust
## 1833 1833 categorical positive
## 1834 1834 cater positive
## 1835 1835 cathartic positive
## 1836 1836 cathedral joy
## 1837 1837 cathedral positive
## 1838 1838 cathedral trust
## 1839 1839 catheter negative
## 1840 1840 caution anger
## 1841 1841 caution anticipation
## 1842 1842 caution fear
## 1843 1843 caution negative
## 1844 1844 cautionary fear
## 1845 1845 cautious anticipation
## 1846 1846 cautious fear
## 1847 1847 cautious positive
## 1848 1848 cautious trust
## 1849 1849 cautiously fear
## 1850 1850 cautiously positive
## 1851 1851 cede negative
## 1852 1852 celebrated anticipation
## 1853 1853 celebrated joy
## 1854 1854 celebrated positive
## 1855 1855 celebrating anticipation
## 1856 1856 celebrating joy
## 1857 1857 celebrating positive
## 1858 1858 celebration anticipation
## 1859 1859 celebration joy
## 1860 1860 celebration positive
## 1861 1861 celebration surprise
## 1862 1862 celebration trust
## 1863 1863 celebrity anger
## 1864 1864 celebrity anticipation
## 1865 1865 celebrity disgust
## 1866 1866 celebrity joy
## 1867 1867 celebrity negative
## 1868 1868 celebrity positive
## 1869 1869 celebrity surprise
## 1870 1870 celebrity trust
## 1871 1871 celestial anticipation
## 1872 1872 celestial joy
## 1873 1873 celestial positive
## 1874 1874 cement anticipation
## 1875 1875 cement trust
## 1876 1876 cemetery fear
## 1877 1877 cemetery negative
## 1878 1878 cemetery sadness
## 1879 1879 censor anger
## 1880 1880 censor disgust
## 1881 1881 censor fear
## 1882 1882 censor negative
## 1883 1883 censor trust
## 1884 1884 censure negative
## 1885 1885 center positive
## 1886 1886 center trust
## 1887 1887 centurion positive
## 1888 1888 cerebral positive
## 1889 1889 ceremony joy
## 1890 1890 ceremony positive
## 1891 1891 ceremony surprise
## 1892 1892 certainty positive
## 1893 1893 certify trust
## 1894 1894 cess disgust
## 1895 1895 cess negative
## 1896 1896 cessation negative
## 1897 1897 chaff anger
## 1898 1898 chaff fear
## 1899 1899 chaff negative
## 1900 1900 chafing negative
## 1901 1901 chagrin disgust
## 1902 1902 chagrin negative
## 1903 1903 chagrin sadness
## 1904 1904 chairman positive
## 1905 1905 chairman trust
## 1906 1906 chairwoman positive
## 1907 1907 chairwoman trust
## 1908 1908 challenge anger
## 1909 1909 challenge fear
## 1910 1910 challenge negative
## 1911 1911 champion anticipation
## 1912 1912 champion joy
## 1913 1913 champion positive
## 1914 1914 champion trust
## 1915 1915 chance surprise
## 1916 1916 chancellor trust
## 1917 1917 change fear
## 1918 1918 changeable anticipation
## 1919 1919 changeable surprise
## 1920 1920 chant anger
## 1921 1921 chant anticipation
## 1922 1922 chant joy
## 1923 1923 chant positive
## 1924 1924 chant surprise
## 1925 1925 chaos anger
## 1926 1926 chaos fear
## 1927 1927 chaos negative
## 1928 1928 chaos sadness
## 1929 1929 chaotic anger
## 1930 1930 chaotic negative
## 1931 1931 chaplain trust
## 1932 1932 charade negative
## 1933 1933 chargeable fear
## 1934 1934 chargeable negative
## 1935 1935 chargeable sadness
## 1936 1936 charger positive
## 1937 1937 charitable anticipation
## 1938 1938 charitable joy
## 1939 1939 charitable positive
## 1940 1940 charitable trust
## 1941 1941 charity joy
## 1942 1942 charity positive
## 1943 1943 charm positive
## 1944 1944 charmed joy
## 1945 1945 charmed negative
## 1946 1946 charmed positive
## 1947 1947 charming positive
## 1948 1948 chart trust
## 1949 1949 chase negative
## 1950 1950 chasm fear
## 1951 1951 chastisement negative
## 1952 1952 chastity anticipation
## 1953 1953 chastity positive
## 1954 1954 chastity trust
## 1955 1955 chattering positive
## 1956 1956 chatty negative
## 1957 1957 cheap negative
## 1958 1958 cheat anger
## 1959 1959 cheat disgust
## 1960 1960 cheat negative
## 1961 1961 checklist positive
## 1962 1962 checklist trust
## 1963 1963 cheer anticipation
## 1964 1964 cheer joy
## 1965 1965 cheer positive
## 1966 1966 cheer surprise
## 1967 1967 cheer trust
## 1968 1968 cheerful joy
## 1969 1969 cheerful positive
## 1970 1970 cheerful surprise
## 1971 1971 cheerfulness anticipation
## 1972 1972 cheerfulness joy
## 1973 1973 cheerfulness positive
## 1974 1974 cheerfulness trust
## 1975 1975 cheering joy
## 1976 1976 cheering positive
## 1977 1977 cheery anticipation
## 1978 1978 cheery joy
## 1979 1979 cheery positive
## 1980 1980 cheesecake negative
## 1981 1981 chemist positive
## 1982 1982 chemist trust
## 1983 1983 cherish anticipation
## 1984 1984 cherish joy
## 1985 1985 cherish positive
## 1986 1986 cherish surprise
## 1987 1987 cherish trust
## 1988 1988 cherry positive
## 1989 1989 chicane anticipation
## 1990 1990 chicane negative
## 1991 1991 chicane surprise
## 1992 1992 chicane trust
## 1993 1993 chicken fear
## 1994 1994 chieftain positive
## 1995 1995 child anticipation
## 1996 1996 child joy
## 1997 1997 child positive
## 1998 1998 childhood joy
## 1999 1999 childhood positive
## 2000 2000 childish negative
## 2001 2001 chilly negative
## 2002 2002 chimera fear
## 2003 2003 chimera surprise
## 2004 2004 chirp joy
## 2005 2005 chirp positive
## 2006 2006 chisel positive
## 2007 2007 chivalry positive
## 2008 2008 chloroform negative
## 2009 2009 chocolate anticipation
## 2010 2010 chocolate joy
## 2011 2011 chocolate positive
## 2012 2012 chocolate trust
## 2013 2013 choice positive
## 2014 2014 choir joy
## 2015 2015 choir positive
## 2016 2016 choir trust
## 2017 2017 choke anger
## 2018 2018 choke negative
## 2019 2019 choke sadness
## 2020 2020 cholera disgust
## 2021 2021 cholera fear
## 2022 2022 cholera negative
## 2023 2023 cholera sadness
## 2024 2024 chop negative
## 2025 2025 choral joy
## 2026 2026 choral positive
## 2027 2027 chore negative
## 2028 2028 chorus positive
## 2029 2029 chosen positive
## 2030 2030 chowder positive
## 2031 2031 chronic negative
## 2032 2032 chronic sadness
## 2033 2033 chronicle positive
## 2034 2034 chronicle trust
## 2035 2035 chuckle anticipation
## 2036 2036 chuckle joy
## 2037 2037 chuckle positive
## 2038 2038 chuckle surprise
## 2039 2039 chuckle trust
## 2040 2040 church anticipation
## 2041 2041 church joy
## 2042 2042 church positive
## 2043 2043 church trust
## 2044 2044 cider positive
## 2045 2045 cigarette negative
## 2046 2046 circumcision positive
## 2047 2047 circumvention negative
## 2048 2048 circumvention positive
## 2049 2049 citizen positive
## 2050 2050 civil positive
## 2051 2051 civility positive
## 2052 2052 civilization positive
## 2053 2053 civilization trust
## 2054 2054 civilized joy
## 2055 2055 civilized positive
## 2056 2056 civilized trust
## 2057 2057 claimant anger
## 2058 2058 claimant disgust
## 2059 2059 clairvoyant positive
## 2060 2060 clamor anger
## 2061 2061 clamor anticipation
## 2062 2062 clamor disgust
## 2063 2063 clamor negative
## 2064 2064 clamor surprise
## 2065 2065 clan trust
## 2066 2066 clap anticipation
## 2067 2067 clap joy
## 2068 2068 clap positive
## 2069 2069 clap trust
## 2070 2070 clarify positive
## 2071 2071 clash anger
## 2072 2072 clash negative
## 2073 2073 clashing anger
## 2074 2074 clashing fear
## 2075 2075 clashing negative
## 2076 2076 classic positive
## 2077 2077 classical positive
## 2078 2078 classics joy
## 2079 2079 classics positive
## 2080 2080 classify positive
## 2081 2081 claw anger
## 2082 2082 claw fear
## 2083 2083 claw negative
## 2084 2084 clean joy
## 2085 2085 clean positive
## 2086 2086 clean trust
## 2087 2087 cleaning positive
## 2088 2088 cleanliness positive
## 2089 2089 cleanly positive
## 2090 2090 cleanse positive
## 2091 2091 cleansing positive
## 2092 2092 clearance positive
## 2093 2093 clearance trust
## 2094 2094 clearness positive
## 2095 2095 cleave fear
## 2096 2096 clerical positive
## 2097 2097 clerical trust
## 2098 2098 clever positive
## 2099 2099 cleverness positive
## 2100 2100 cliff fear
## 2101 2101 climax anticipation
## 2102 2102 climax joy
## 2103 2103 climax positive
## 2104 2104 climax surprise
## 2105 2105 climax trust
## 2106 2106 clock anticipation
## 2107 2107 cloister negative
## 2108 2108 closeness joy
## 2109 2109 closeness positive
## 2110 2110 closeness trust
## 2111 2111 closure anticipation
## 2112 2112 closure joy
## 2113 2113 closure positive
## 2114 2114 closure sadness
## 2115 2115 clothe positive
## 2116 2116 clouded negative
## 2117 2117 clouded sadness
## 2118 2118 cloudiness fear
## 2119 2119 cloudiness negative
## 2120 2120 cloudy sadness
## 2121 2121 clown anticipation
## 2122 2122 clown joy
## 2123 2123 clown positive
## 2124 2124 clown surprise
## 2125 2125 clue anticipation
## 2126 2126 clump negative
## 2127 2127 clumsy disgust
## 2128 2128 clumsy negative
## 2129 2129 coach trust
## 2130 2130 coalesce trust
## 2131 2131 coalition positive
## 2132 2132 coast positive
## 2133 2133 coax trust
## 2134 2134 cobra fear
## 2135 2135 cocaine negative
## 2136 2136 cocaine sadness
## 2137 2137 coerce anger
## 2138 2138 coerce disgust
## 2139 2139 coerce fear
## 2140 2140 coerce negative
## 2141 2141 coercion anger
## 2142 2142 coercion disgust
## 2143 2143 coercion fear
## 2144 2144 coercion negative
## 2145 2145 coercion sadness
## 2146 2146 coexist positive
## 2147 2147 coexist trust
## 2148 2148 coexisting trust
## 2149 2149 coffin fear
## 2150 2150 coffin negative
## 2151 2151 coffin sadness
## 2152 2152 cogent positive
## 2153 2153 cogent trust
## 2154 2154 cognitive positive
## 2155 2155 coherence positive
## 2156 2156 coherent positive
## 2157 2157 cohesion trust
## 2158 2158 cohesive positive
## 2159 2159 cohesive trust
## 2160 2160 coincidence surprise
## 2161 2161 cold negative
## 2162 2162 coldly negative
## 2163 2163 coldness anger
## 2164 2164 coldness disgust
## 2165 2165 coldness fear
## 2166 2166 coldness negative
## 2167 2167 coldness sadness
## 2168 2168 colic negative
## 2169 2169 collaborator trust
## 2170 2170 collapse disgust
## 2171 2171 collapse fear
## 2172 2172 collapse negative
## 2173 2173 collapse sadness
## 2174 2174 collateral trust
## 2175 2175 collectively positive
## 2176 2176 collectively trust
## 2177 2177 collision anger
## 2178 2178 collision negative
## 2179 2179 collusion anger
## 2180 2180 collusion disgust
## 2181 2181 collusion fear
## 2182 2182 collusion negative
## 2183 2183 collusion sadness
## 2184 2184 colonel positive
## 2185 2185 colonel trust
## 2186 2186 colossal positive
## 2187 2187 coma fear
## 2188 2188 coma negative
## 2189 2189 coma sadness
## 2190 2190 comatose fear
## 2191 2191 comatose negative
## 2192 2192 comatose sadness
## 2193 2193 combat anger
## 2194 2194 combat fear
## 2195 2195 combat negative
## 2196 2196 combatant anger
## 2197 2197 combatant fear
## 2198 2198 combatant negative
## 2199 2199 combative anger
## 2200 2200 combative fear
## 2201 2201 combative negative
## 2202 2202 comfort anticipation
## 2203 2203 comfort joy
## 2204 2204 comfort positive
## 2205 2205 comfort trust
## 2206 2206 coming anticipation
## 2207 2207 commandant positive
## 2208 2208 commandant trust
## 2209 2209 commanding positive
## 2210 2210 commanding trust
## 2211 2211 commemorate anticipation
## 2212 2212 commemorate joy
## 2213 2213 commemorate positive
## 2214 2214 commemorate sadness
## 2215 2215 commemoration anticipation
## 2216 2216 commemoration joy
## 2217 2217 commemoration positive
## 2218 2218 commemorative anticipation
## 2219 2219 commemorative positive
## 2220 2220 commend positive
## 2221 2221 commendable joy
## 2222 2222 commendable positive
## 2223 2223 commendable trust
## 2224 2224 commentator positive
## 2225 2225 commerce trust
## 2226 2226 commission trust
## 2227 2227 committal negative
## 2228 2228 committal sadness
## 2229 2229 committed positive
## 2230 2230 committed trust
## 2231 2231 committee trust
## 2232 2232 commodore positive
## 2233 2233 commodore trust
## 2234 2234 commonplace anticipation
## 2235 2235 commonplace trust
## 2236 2236 commonwealth positive
## 2237 2237 commonwealth trust
## 2238 2238 commotion anger
## 2239 2239 commotion negative
## 2240 2240 communicate positive
## 2241 2241 communicate trust
## 2242 2242 communication trust
## 2243 2243 communicative positive
## 2244 2244 communion joy
## 2245 2245 communion positive
## 2246 2246 communion trust
## 2247 2247 communism anger
## 2248 2248 communism fear
## 2249 2249 communism negative
## 2250 2250 communism sadness
## 2251 2251 communist negative
## 2252 2252 community positive
## 2253 2253 commutation positive
## 2254 2254 commute positive
## 2255 2255 compact trust
## 2256 2256 companion joy
## 2257 2257 companion positive
## 2258 2258 companion trust
## 2259 2259 compass trust
## 2260 2260 compassion fear
## 2261 2261 compassion positive
## 2262 2262 compassionate positive
## 2263 2263 compatibility positive
## 2264 2264 compatible positive
## 2265 2265 compelling positive
## 2266 2266 compensate anticipation
## 2267 2267 compensate joy
## 2268 2268 compensate positive
## 2269 2269 compensate surprise
## 2270 2270 compensate trust
## 2271 2271 compensatory positive
## 2272 2272 competence positive
## 2273 2273 competence trust
## 2274 2274 competency positive
## 2275 2275 competency trust
## 2276 2276 competent positive
## 2277 2277 competent trust
## 2278 2278 competition anticipation
## 2279 2279 competition negative
## 2280 2280 complacency positive
## 2281 2281 complain anger
## 2282 2282 complain negative
## 2283 2283 complain sadness
## 2284 2284 complaint anger
## 2285 2285 complaint negative
## 2286 2286 complement anticipation
## 2287 2287 complement joy
## 2288 2288 complement positive
## 2289 2289 complement surprise
## 2290 2290 complement trust
## 2291 2291 complementary positive
## 2292 2292 completely positive
## 2293 2293 completeness positive
## 2294 2294 completing anticipation
## 2295 2295 completing joy
## 2296 2296 completing positive
## 2297 2297 completion anticipation
## 2298 2298 completion joy
## 2299 2299 completion positive
## 2300 2300 complexed negative
## 2301 2301 complexity negative
## 2302 2302 compliance positive
## 2303 2303 compliance trust
## 2304 2304 compliant positive
## 2305 2305 complicate anger
## 2306 2306 complicate negative
## 2307 2307 complicated negative
## 2308 2308 complication negative
## 2309 2309 complicity negative
## 2310 2310 complicity positive
## 2311 2311 compliment anticipation
## 2312 2312 compliment joy
## 2313 2313 compliment positive
## 2314 2314 compliment surprise
## 2315 2315 compliment trust
## 2316 2316 composed positive
## 2317 2317 composer positive
## 2318 2318 compost disgust
## 2319 2319 compost negative
## 2320 2320 composure positive
## 2321 2321 comprehend positive
## 2322 2322 comprehensive positive
## 2323 2323 compress anger
## 2324 2324 comptroller trust
## 2325 2325 compulsion anger
## 2326 2326 compulsion negative
## 2327 2327 compulsory negative
## 2328 2328 comrade positive
## 2329 2329 comrade trust
## 2330 2330 conceal negative
## 2331 2331 conceal sadness
## 2332 2332 concealed anticipation
## 2333 2333 concealed fear
## 2334 2334 concealed negative
## 2335 2335 concealed surprise
## 2336 2336 concealment anger
## 2337 2337 concealment anticipation
## 2338 2338 concealment fear
## 2339 2339 concealment negative
## 2340 2340 conceit negative
## 2341 2341 conceited negative
## 2342 2342 concentric positive
## 2343 2343 concerned fear
## 2344 2344 concerned sadness
## 2345 2345 conciliation joy
## 2346 2346 conciliation positive
## 2347 2347 conciliation trust
## 2348 2348 concluding positive
## 2349 2349 concord positive
## 2350 2350 concord trust
## 2351 2351 concordance positive
## 2352 2352 concordance trust
## 2353 2353 concussion anger
## 2354 2354 concussion negative
## 2355 2355 concussion sadness
## 2356 2356 condemn anger
## 2357 2357 condemn negative
## 2358 2358 condemnation anger
## 2359 2359 condemnation anticipation
## 2360 2360 condemnation disgust
## 2361 2361 condemnation fear
## 2362 2362 condemnation negative
## 2363 2363 condemnation sadness
## 2364 2364 condescending negative
## 2365 2365 condescension anger
## 2366 2366 condescension disgust
## 2367 2367 condescension negative
## 2368 2368 condescension sadness
## 2369 2369 condolence positive
## 2370 2370 condolence sadness
## 2371 2371 condone positive
## 2372 2372 conducive positive
## 2373 2373 conductivity positive
## 2374 2374 confederate positive
## 2375 2375 confederate trust
## 2376 2376 confess negative
## 2377 2377 confess positive
## 2378 2378 confess trust
## 2379 2379 confession anticipation
## 2380 2380 confession fear
## 2381 2381 confession negative
## 2382 2382 confession sadness
## 2383 2383 confession surprise
## 2384 2384 confessional fear
## 2385 2385 confessional trust
## 2386 2386 confide trust
## 2387 2387 confidence fear
## 2388 2388 confidence joy
## 2389 2389 confidence positive
## 2390 2390 confidence trust
## 2391 2391 confident joy
## 2392 2392 confident positive
## 2393 2393 confident trust
## 2394 2394 confidential trust
## 2395 2395 confidentially trust
## 2396 2396 confine anger
## 2397 2397 confine fear
## 2398 2398 confine negative
## 2399 2399 confine sadness
## 2400 2400 confined anger
## 2401 2401 confined disgust
## 2402 2402 confined fear
## 2403 2403 confined negative
## 2404 2404 confined sadness
## 2405 2405 confinement anger
## 2406 2406 confinement fear
## 2407 2407 confinement negative
## 2408 2408 confinement sadness
## 2409 2409 confirmation trust
## 2410 2410 confirmed positive
## 2411 2411 confirmed trust
## 2412 2412 confiscate anger
## 2413 2413 confiscate negative
## 2414 2414 confiscate sadness
## 2415 2415 confiscation negative
## 2416 2416 conflagration anger
## 2417 2417 conflagration fear
## 2418 2418 conflagration negative
## 2419 2419 conflict anger
## 2420 2420 conflict fear
## 2421 2421 conflict negative
## 2422 2422 conflict sadness
## 2423 2423 conflicting negative
## 2424 2424 conformance positive
## 2425 2425 conformity trust
## 2426 2426 confound negative
## 2427 2427 confounded negative
## 2428 2428 confront anger
## 2429 2429 confuse negative
## 2430 2430 confusion anger
## 2431 2431 confusion fear
## 2432 2432 confusion negative
## 2433 2433 congenial positive
## 2434 2434 congestion negative
## 2435 2435 conglomerate trust
## 2436 2436 congratulatory joy
## 2437 2437 congratulatory positive
## 2438 2438 congregation positive
## 2439 2439 congregation trust
## 2440 2440 congress disgust
## 2441 2441 congress trust
## 2442 2442 congressman trust
## 2443 2443 congruence positive
## 2444 2444 congruence trust
## 2445 2445 conjecture anticipation
## 2446 2446 conjure anticipation
## 2447 2447 conjure surprise
## 2448 2448 conjuring negative
## 2449 2449 connective trust
## 2450 2450 connoisseur joy
## 2451 2451 connoisseur positive
## 2452 2452 connoisseur trust
## 2453 2453 conquest anger
## 2454 2454 conquest fear
## 2455 2455 conquest negative
## 2456 2456 conscience positive
## 2457 2457 conscience trust
## 2458 2458 conscientious positive
## 2459 2459 conscientious trust
## 2460 2460 consciousness positive
## 2461 2461 consecration anticipation
## 2462 2462 consecration joy
## 2463 2463 consecration positive
## 2464 2464 consecration sadness
## 2465 2465 consecration trust
## 2466 2466 consequent anticipation
## 2467 2467 conservation anticipation
## 2468 2468 conservation positive
## 2469 2469 conservation trust
## 2470 2470 conserve positive
## 2471 2471 considerable positive
## 2472 2472 considerate positive
## 2473 2473 considerate trust
## 2474 2474 consistency positive
## 2475 2475 consistency trust
## 2476 2476 console positive
## 2477 2477 console sadness
## 2478 2478 consonant positive
## 2479 2479 consort trust
## 2480 2480 conspiracy fear
## 2481 2481 conspirator anger
## 2482 2482 conspirator anticipation
## 2483 2483 conspirator disgust
## 2484 2484 conspirator fear
## 2485 2485 conspirator negative
## 2486 2486 conspire fear
## 2487 2487 conspire negative
## 2488 2488 constable trust
## 2489 2489 constancy positive
## 2490 2490 constancy trust
## 2491 2491 constant positive
## 2492 2492 constant trust
## 2493 2493 constantly trust
## 2494 2494 consternation anger
## 2495 2495 consternation fear
## 2496 2496 consternation negative
## 2497 2497 constipation disgust
## 2498 2498 constipation negative
## 2499 2499 constitute trust
## 2500 2500 constitutional positive
## 2501 2501 constitutional trust
## 2502 2502 constrain fear
## 2503 2503 constrain negative
## 2504 2504 constrained negative
## 2505 2505 constraint anger
## 2506 2506 constraint fear
## 2507 2507 constraint negative
## 2508 2508 constraint sadness
## 2509 2509 construct positive
## 2510 2510 consul trust
## 2511 2511 consult trust
## 2512 2512 consummate positive
## 2513 2513 contact positive
## 2514 2514 contagion anticipation
## 2515 2515 contagion disgust
## 2516 2516 contagion fear
## 2517 2517 contagion negative
## 2518 2518 contagious disgust
## 2519 2519 contagious fear
## 2520 2520 contagious negative
## 2521 2521 contaminate disgust
## 2522 2522 contaminate negative
## 2523 2523 contaminated disgust
## 2524 2524 contaminated fear
## 2525 2525 contaminated negative
## 2526 2526 contaminated sadness
## 2527 2527 contamination disgust
## 2528 2528 contamination negative
## 2529 2529 contemplation positive
## 2530 2530 contempt anger
## 2531 2531 contempt disgust
## 2532 2532 contempt fear
## 2533 2533 contempt negative
## 2534 2534 contemptible anger
## 2535 2535 contemptible disgust
## 2536 2536 contemptible negative
## 2537 2537 contemptuous anger
## 2538 2538 contemptuous negative
## 2539 2539 content joy
## 2540 2540 content positive
## 2541 2541 content trust
## 2542 2542 contentious anger
## 2543 2543 contentious disgust
## 2544 2544 contentious fear
## 2545 2545 contentious negative
## 2546 2546 contingent anticipation
## 2547 2547 continuation anticipation
## 2548 2548 continue anticipation
## 2549 2549 continue positive
## 2550 2550 continue trust
## 2551 2551 contour positive
## 2552 2552 contraband anger
## 2553 2553 contraband disgust
## 2554 2554 contraband fear
## 2555 2555 contraband negative
## 2556 2556 contracted negative
## 2557 2557 contradict anger
## 2558 2558 contradict negative
## 2559 2559 contradiction negative
## 2560 2560 contradictory negative
## 2561 2561 contrary negative
## 2562 2562 contrasted negative
## 2563 2563 contravene negative
## 2564 2564 contravention negative
## 2565 2565 contribute positive
## 2566 2566 contributor positive
## 2567 2567 contributor trust
## 2568 2568 controversial anger
## 2569 2569 controversial negative
## 2570 2570 controversy negative
## 2571 2571 convenience positive
## 2572 2572 convenient positive
## 2573 2573 convent positive
## 2574 2574 convent trust
## 2575 2575 convention positive
## 2576 2576 convergence anticipation
## 2577 2577 conversant positive
## 2578 2578 conversational positive
## 2579 2579 convert positive
## 2580 2580 conveyancing trust
## 2581 2581 convict anger
## 2582 2582 convict disgust
## 2583 2583 convict fear
## 2584 2584 convict negative
## 2585 2585 convict sadness
## 2586 2586 conviction negative
## 2587 2587 convince anticipation
## 2588 2588 convince positive
## 2589 2589 convince trust
## 2590 2590 convinced trust
## 2591 2591 convincing trust
## 2592 2592 cool positive
## 2593 2593 coolness positive
## 2594 2594 coop anger
## 2595 2595 coop disgust
## 2596 2596 coop negative
## 2597 2597 cooperate positive
## 2598 2598 cooperating positive
## 2599 2599 cooperating trust
## 2600 2600 cooperation positive
## 2601 2601 cooperation trust
## 2602 2602 cooperative positive
## 2603 2603 cooperative trust
## 2604 2604 cop fear
## 2605 2605 cop trust
## 2606 2606 copy negative
## 2607 2607 copycat anger
## 2608 2608 copycat disgust
## 2609 2609 copycat negative
## 2610 2610 core positive
## 2611 2611 coronation joy
## 2612 2612 coronation positive
## 2613 2613 coronation trust
## 2614 2614 coroner negative
## 2615 2615 corporal negative
## 2616 2616 corporation positive
## 2617 2617 corporation trust
## 2618 2618 corporeal positive
## 2619 2619 corpse disgust
## 2620 2620 corpse negative
## 2621 2621 corpse sadness
## 2622 2622 correction negative
## 2623 2623 corrective positive
## 2624 2624 correctness trust
## 2625 2625 correspondence anticipation
## 2626 2626 correspondence positive
## 2627 2627 corroborate positive
## 2628 2628 corroborate trust
## 2629 2629 corroboration trust
## 2630 2630 corrosion negative
## 2631 2631 corrosive fear
## 2632 2632 corrosive negative
## 2633 2633 corrupt negative
## 2634 2634 corrupting anger
## 2635 2635 corrupting disgust
## 2636 2636 corrupting fear
## 2637 2637 corrupting negative
## 2638 2638 corrupting sadness
## 2639 2639 corruption disgust
## 2640 2640 corruption negative
## 2641 2641 corse sadness
## 2642 2642 cosmopolitan positive
## 2643 2643 cosmopolitan trust
## 2644 2644 cosy positive
## 2645 2645 couch sadness
## 2646 2646 cough disgust
## 2647 2647 cough negative
## 2648 2648 council anticipation
## 2649 2649 council positive
## 2650 2650 council trust
## 2651 2651 counsel positive
## 2652 2652 counsel trust
## 2653 2653 counsellor anger
## 2654 2654 counsellor fear
## 2655 2655 counsellor negative
## 2656 2656 counsellor trust
## 2657 2657 counselor positive
## 2658 2658 counselor trust
## 2659 2659 count positive
## 2660 2660 count trust
## 2661 2661 countdown anticipation
## 2662 2662 countess positive
## 2663 2663 countryman trust
## 2664 2664 county trust
## 2665 2665 coup anger
## 2666 2666 coup surprise
## 2667 2667 courage positive
## 2668 2668 courageous fear
## 2669 2669 courageous positive
## 2670 2670 courier trust
## 2671 2671 coursing negative
## 2672 2672 court anger
## 2673 2673 court anticipation
## 2674 2674 court fear
## 2675 2675 courteous positive
## 2676 2676 courtesy positive
## 2677 2677 courtship anticipation
## 2678 2678 courtship joy
## 2679 2679 courtship positive
## 2680 2680 courtship trust
## 2681 2681 cove anticipation
## 2682 2682 cove disgust
## 2683 2683 cove fear
## 2684 2684 cove joy
## 2685 2685 cove positive
## 2686 2686 covenant positive
## 2687 2687 covenant trust
## 2688 2688 cover trust
## 2689 2689 covet negative
## 2690 2690 coward disgust
## 2691 2691 coward fear
## 2692 2692 coward negative
## 2693 2693 coward sadness
## 2694 2694 cowardice fear
## 2695 2695 cowardice negative
## 2696 2696 cowardly fear
## 2697 2697 cowardly negative
## 2698 2698 coy fear
## 2699 2699 coyote fear
## 2700 2700 crabby anger
## 2701 2701 crabby negative
## 2702 2702 crack negative
## 2703 2703 cracked anger
## 2704 2704 cracked fear
## 2705 2705 cracked negative
## 2706 2706 cracking negative
## 2707 2707 cradle anticipation
## 2708 2708 cradle joy
## 2709 2709 cradle positive
## 2710 2710 cradle trust
## 2711 2711 craft positive
## 2712 2712 craftsman positive
## 2713 2713 cramp anticipation
## 2714 2714 cramp negative
## 2715 2715 cramped negative
## 2716 2716 crank negative
## 2717 2717 cranky anger
## 2718 2718 cranky negative
## 2719 2719 crap disgust
## 2720 2720 crap negative
## 2721 2721 craps anticipation
## 2722 2722 crash fear
## 2723 2723 crash negative
## 2724 2724 crash sadness
## 2725 2725 crash surprise
## 2726 2726 crave anticipation
## 2727 2727 craving anticipation
## 2728 2728 crawl disgust
## 2729 2729 crawl negative
## 2730 2730 crazed anger
## 2731 2731 crazed fear
## 2732 2732 crazed negative
## 2733 2733 crazy anger
## 2734 2734 crazy fear
## 2735 2735 crazy negative
## 2736 2736 crazy sadness
## 2737 2737 creaking negative
## 2738 2738 cream anticipation
## 2739 2739 cream joy
## 2740 2740 cream positive
## 2741 2741 cream surprise
## 2742 2742 create joy
## 2743 2743 create positive
## 2744 2744 creative positive
## 2745 2745 creature disgust
## 2746 2746 creature fear
## 2747 2747 creature negative
## 2748 2748 credence positive
## 2749 2749 credence trust
## 2750 2750 credential positive
## 2751 2751 credential trust
## 2752 2752 credibility positive
## 2753 2753 credibility trust
## 2754 2754 credible positive
## 2755 2755 credible trust
## 2756 2756 credit positive
## 2757 2757 credit trust
## 2758 2758 creditable positive
## 2759 2759 creditable trust
## 2760 2760 credited positive
## 2761 2761 creep negative
## 2762 2762 creeping anticipation
## 2763 2763 cremation sadness
## 2764 2764 crescendo anticipation
## 2765 2765 crescendo joy
## 2766 2766 crescendo positive
## 2767 2767 crescendo surprise
## 2768 2768 crescendo trust
## 2769 2769 crew trust
## 2770 2770 crime anger
## 2771 2771 crime negative
## 2772 2772 criminal anger
## 2773 2773 criminal disgust
## 2774 2774 criminal fear
## 2775 2775 criminal negative
## 2776 2776 criminality anger
## 2777 2777 criminality disgust
## 2778 2778 criminality fear
## 2779 2779 criminality negative
## 2780 2780 cringe disgust
## 2781 2781 cringe fear
## 2782 2782 cringe negative
## 2783 2783 cringe sadness
## 2784 2784 cripple fear
## 2785 2785 cripple negative
## 2786 2786 cripple sadness
## 2787 2787 crippled negative
## 2788 2788 crippled sadness
## 2789 2789 crisis negative
## 2790 2790 crisp negative
## 2791 2791 crisp trust
## 2792 2792 critic negative
## 2793 2793 criticism anger
## 2794 2794 criticism negative
## 2795 2795 criticism sadness
## 2796 2796 criticize anger
## 2797 2797 criticize disgust
## 2798 2798 criticize fear
## 2799 2799 criticize negative
## 2800 2800 criticize sadness
## 2801 2801 critique positive
## 2802 2802 critter disgust
## 2803 2803 crocodile fear
## 2804 2804 crook negative
## 2805 2805 cross anger
## 2806 2806 cross fear
## 2807 2807 cross negative
## 2808 2808 cross sadness
## 2809 2809 crouch fear
## 2810 2810 crouching fear
## 2811 2811 crouching negative
## 2812 2812 crowning anticipation
## 2813 2813 crowning joy
## 2814 2814 crowning positive
## 2815 2815 crowning surprise
## 2816 2816 crowning trust
## 2817 2817 crucial positive
## 2818 2818 crucial trust
## 2819 2819 cruciate negative
## 2820 2820 crucifixion anger
## 2821 2821 crucifixion disgust
## 2822 2822 crucifixion fear
## 2823 2823 crucifixion negative
## 2824 2824 crucifixion sadness
## 2825 2825 crude disgust
## 2826 2826 crude negative
## 2827 2827 cruel anger
## 2828 2828 cruel disgust
## 2829 2829 cruel fear
## 2830 2830 cruel negative
## 2831 2831 cruel sadness
## 2832 2832 cruelly anger
## 2833 2833 cruelly fear
## 2834 2834 cruelly negative
## 2835 2835 cruelty anger
## 2836 2836 cruelty disgust
## 2837 2837 cruelty fear
## 2838 2838 cruelty negative
## 2839 2839 cruelty sadness
## 2840 2840 crumbling negative
## 2841 2841 crumbling sadness
## 2842 2842 crunch anger
## 2843 2843 crunch negative
## 2844 2844 crusade anger
## 2845 2845 crusade fear
## 2846 2846 crusade negative
## 2847 2847 crushed anger
## 2848 2848 crushed disgust
## 2849 2849 crushed fear
## 2850 2850 crushed negative
## 2851 2851 crushed sadness
## 2852 2852 crushing anger
## 2853 2853 crushing disgust
## 2854 2854 crushing fear
## 2855 2855 crushing negative
## 2856 2856 crusty disgust
## 2857 2857 crusty negative
## 2858 2858 cry negative
## 2859 2859 cry sadness
## 2860 2860 crying negative
## 2861 2861 crying sadness
## 2862 2862 crypt fear
## 2863 2863 crypt negative
## 2864 2864 crypt sadness
## 2865 2865 crystal positive
## 2866 2866 cube trust
## 2867 2867 cuckold disgust
## 2868 2868 cuckold negative
## 2869 2869 cuckoo negative
## 2870 2870 cuddle joy
## 2871 2871 cuddle positive
## 2872 2872 cuddle trust
## 2873 2873 cue anticipation
## 2874 2874 culinary positive
## 2875 2875 culinary trust
## 2876 2876 cull negative
## 2877 2877 culmination positive
## 2878 2878 culpability negative
## 2879 2879 culpable negative
## 2880 2880 culprit negative
## 2881 2881 cult fear
## 2882 2882 cult negative
## 2883 2883 cultivate anticipation
## 2884 2884 cultivate positive
## 2885 2885 cultivate trust
## 2886 2886 cultivated positive
## 2887 2887 cultivation positive
## 2888 2888 culture positive
## 2889 2889 cumbersome negative
## 2890 2890 cumbersome sadness
## 2891 2891 cunning negative
## 2892 2892 cunning positive
## 2893 2893 cupping disgust
## 2894 2894 cupping fear
## 2895 2895 cupping negative
## 2896 2896 cupping sadness
## 2897 2897 cur anger
## 2898 2898 cur disgust
## 2899 2899 cur fear
## 2900 2900 cur negative
## 2901 2901 curable positive
## 2902 2902 curable trust
## 2903 2903 curiosity anticipation
## 2904 2904 curiosity positive
## 2905 2905 curiosity surprise
## 2906 2906 curl positive
## 2907 2907 curse anger
## 2908 2908 curse disgust
## 2909 2909 curse fear
## 2910 2910 curse negative
## 2911 2911 curse sadness
## 2912 2912 cursed anger
## 2913 2913 cursed fear
## 2914 2914 cursed negative
## 2915 2915 cursed sadness
## 2916 2916 cursing anger
## 2917 2917 cursing disgust
## 2918 2918 cursing negative
## 2919 2919 cursory negative
## 2920 2920 cushion positive
## 2921 2921 cussed anger
## 2922 2922 custodian trust
## 2923 2923 custody trust
## 2924 2924 customer positive
## 2925 2925 cute positive
## 2926 2926 cutter fear
## 2927 2927 cutter negative
## 2928 2928 cutters positive
## 2929 2929 cutthroat anger
## 2930 2930 cutthroat fear
## 2931 2931 cutthroat negative
## 2932 2932 cutting anger
## 2933 2933 cutting disgust
## 2934 2934 cutting fear
## 2935 2935 cutting negative
## 2936 2936 cutting sadness
## 2937 2937 cyanide fear
## 2938 2938 cyanide negative
## 2939 2939 cyclone fear
## 2940 2940 cyclone negative
## 2941 2941 cyclone surprise
## 2942 2942 cyst fear
## 2943 2943 cyst negative
## 2944 2944 cyst sadness
## 2945 2945 cystic disgust
## 2946 2946 cytomegalovirus negative
## 2947 2947 cytomegalovirus sadness
## 2948 2948 dabbling anger
## 2949 2949 dabbling disgust
## 2950 2950 dabbling negative
## 2951 2951 daemon anger
## 2952 2952 daemon disgust
## 2953 2953 daemon fear
## 2954 2954 daemon negative
## 2955 2955 daemon sadness
## 2956 2956 daemon surprise
## 2957 2957 daft disgust
## 2958 2958 daft negative
## 2959 2959 dagger fear
## 2960 2960 dagger negative
## 2961 2961 daily anticipation
## 2962 2962 damage anger
## 2963 2963 damage disgust
## 2964 2964 damage negative
## 2965 2965 damage sadness
## 2966 2966 damages negative
## 2967 2967 damages sadness
## 2968 2968 dame anger
## 2969 2969 dame disgust
## 2970 2970 dame positive
## 2971 2971 dame trust
## 2972 2972 damn anger
## 2973 2973 damn disgust
## 2974 2974 damn negative
## 2975 2975 damnation anger
## 2976 2976 damnation fear
## 2977 2977 damnation negative
## 2978 2978 damnation sadness
## 2979 2979 damned negative
## 2980 2980 damper negative
## 2981 2981 dance joy
## 2982 2982 dance positive
## 2983 2983 dance trust
## 2984 2984 dandruff negative
## 2985 2985 dandy disgust
## 2986 2986 dandy negative
## 2987 2987 danger fear
## 2988 2988 danger negative
## 2989 2989 danger sadness
## 2990 2990 dangerous fear
## 2991 2991 dangerous negative
## 2992 2992 dank disgust
## 2993 2993 dare anticipation
## 2994 2994 dare trust
## 2995 2995 daring positive
## 2996 2996 dark sadness
## 2997 2997 darken fear
## 2998 2998 darken negative
## 2999 2999 darken sadness
## 3000 3000 darkened fear
## 3001 3001 darkened negative
## 3002 3002 darkened sadness
## 3003 3003 darkness anger
## 3004 3004 darkness fear
## 3005 3005 darkness negative
## 3006 3006 darkness sadness
## 3007 3007 darling joy
## 3008 3008 darling positive
## 3009 3009 darling trust
## 3010 3010 dart fear
## 3011 3011 dashed anger
## 3012 3012 dashed fear
## 3013 3013 dashed negative
## 3014 3014 dashed sadness
## 3015 3015 dashing positive
## 3016 3016 dastardly anger
## 3017 3017 dastardly disgust
## 3018 3018 dastardly fear
## 3019 3019 dastardly negative
## 3020 3020 daughter joy
## 3021 3021 daughter positive
## 3022 3022 dawn anticipation
## 3023 3023 dawn joy
## 3024 3024 dawn positive
## 3025 3025 dawn surprise
## 3026 3026 dawn trust
## 3027 3027 dazed negative
## 3028 3028 deacon trust
## 3029 3029 deactivate negative
## 3030 3030 deadlock negative
## 3031 3031 deadly anger
## 3032 3032 deadly disgust
## 3033 3033 deadly fear
## 3034 3034 deadly negative
## 3035 3035 deadly sadness
## 3036 3036 deal anticipation
## 3037 3037 deal joy
## 3038 3038 deal positive
## 3039 3039 deal surprise
## 3040 3040 deal trust
## 3041 3041 dealings trust
## 3042 3042 dear positive
## 3043 3043 death anger
## 3044 3044 death anticipation
## 3045 3045 death disgust
## 3046 3046 death fear
## 3047 3047 death negative
## 3048 3048 death sadness
## 3049 3049 death surprise
## 3050 3050 debacle fear
## 3051 3051 debacle negative
## 3052 3052 debacle sadness
## 3053 3053 debate positive
## 3054 3054 debauchery disgust
## 3055 3055 debauchery fear
## 3056 3056 debauchery negative
## 3057 3057 debenture anticipation
## 3058 3058 debris disgust
## 3059 3059 debris negative
## 3060 3060 debt negative
## 3061 3061 debt sadness
## 3062 3062 debtor negative
## 3063 3063 decay fear
## 3064 3064 decay negative
## 3065 3065 decay sadness
## 3066 3066 decayed disgust
## 3067 3067 decayed negative
## 3068 3068 decayed sadness
## 3069 3069 deceased negative
## 3070 3070 deceased sadness
## 3071 3071 deceit anger
## 3072 3072 deceit disgust
## 3073 3073 deceit fear
## 3074 3074 deceit negative
## 3075 3075 deceit sadness
## 3076 3076 deceit surprise
## 3077 3077 deceitful disgust
## 3078 3078 deceitful negative
## 3079 3079 deceitful sadness
## 3080 3080 deceive anger
## 3081 3081 deceive disgust
## 3082 3082 deceive negative
## 3083 3083 deceive sadness
## 3084 3084 deceived anger
## 3085 3085 deceived negative
## 3086 3086 deceiving negative
## 3087 3087 deceiving trust
## 3088 3088 decency positive
## 3089 3089 decent positive
## 3090 3090 deception negative
## 3091 3091 deceptive negative
## 3092 3092 declaratory positive
## 3093 3093 declination negative
## 3094 3094 decline negative
## 3095 3095 declining negative
## 3096 3096 decompose disgust
## 3097 3097 decomposed sadness
## 3098 3098 decomposition disgust
## 3099 3099 decomposition fear
## 3100 3100 decomposition negative
## 3101 3101 decomposition sadness
## 3102 3102 decoy surprise
## 3103 3103 decrease negative
## 3104 3104 decrement negative
## 3105 3105 decrepit negative
## 3106 3106 decry anger
## 3107 3107 decry negative
## 3108 3108 dedication positive
## 3109 3109 deduct negative
## 3110 3110 deed trust
## 3111 3111 defamation disgust
## 3112 3112 defamation fear
## 3113 3113 defamation negative
## 3114 3114 defamatory anger
## 3115 3115 defamatory negative
## 3116 3116 default disgust
## 3117 3117 default fear
## 3118 3118 default negative
## 3119 3119 default sadness
## 3120 3120 defeat negative
## 3121 3121 defeated negative
## 3122 3122 defeated sadness
## 3123 3123 defect anger
## 3124 3124 defect negative
## 3125 3125 defection fear
## 3126 3126 defection negative
## 3127 3127 defective disgust
## 3128 3128 defective negative
## 3129 3129 defend fear
## 3130 3130 defend positive
## 3131 3131 defendant anger
## 3132 3132 defendant fear
## 3133 3133 defendant sadness
## 3134 3134 defended positive
## 3135 3135 defended trust
## 3136 3136 defender positive
## 3137 3137 defender trust
## 3138 3138 defending positive
## 3139 3139 defense anger
## 3140 3140 defense anticipation
## 3141 3141 defense fear
## 3142 3142 defense positive
## 3143 3143 defenseless fear
## 3144 3144 defenseless negative
## 3145 3145 defenseless sadness
## 3146 3146 deference positive
## 3147 3147 deference trust
## 3148 3148 deferral negative
## 3149 3149 defiance anger
## 3150 3150 defiance disgust
## 3151 3151 defiance fear
## 3152 3152 defiance negative
## 3153 3153 defiant anger
## 3154 3154 defiant negative
## 3155 3155 deficiency negative
## 3156 3156 deficit negative
## 3157 3157 definitive positive
## 3158 3158 definitive trust
## 3159 3159 deflate anger
## 3160 3160 deflate negative
## 3161 3161 deflate sadness
## 3162 3162 deflation fear
## 3163 3163 deflation negative
## 3164 3164 deform disgust
## 3165 3165 deform negative
## 3166 3166 deformed disgust
## 3167 3167 deformed negative
## 3168 3168 deformed sadness
## 3169 3169 deformity disgust
## 3170 3170 deformity fear
## 3171 3171 deformity negative
## 3172 3172 deformity sadness
## 3173 3173 defraud anger
## 3174 3174 defraud disgust
## 3175 3175 defraud negative
## 3176 3176 defunct negative
## 3177 3177 defunct sadness
## 3178 3178 defy anger
## 3179 3179 defy fear
## 3180 3180 defy negative
## 3181 3181 defy sadness
## 3182 3182 defy surprise
## 3183 3183 degeneracy anger
## 3184 3184 degeneracy disgust
## 3185 3185 degeneracy negative
## 3186 3186 degeneracy sadness
## 3187 3187 degenerate negative
## 3188 3188 degradation negative
## 3189 3189 degrade disgust
## 3190 3190 degrade negative
## 3191 3191 degrading disgust
## 3192 3192 degrading fear
## 3193 3193 degrading negative
## 3194 3194 degrading sadness
## 3195 3195 degree positive
## 3196 3196 delay anger
## 3197 3197 delay disgust
## 3198 3198 delay fear
## 3199 3199 delay negative
## 3200 3200 delay sadness
## 3201 3201 delayed negative
## 3202 3202 delectable positive
## 3203 3203 delegate positive
## 3204 3204 delegate trust
## 3205 3205 deleterious anger
## 3206 3206 deleterious disgust
## 3207 3207 deleterious fear
## 3208 3208 deleterious negative
## 3209 3209 deletion negative
## 3210 3210 deliberate positive
## 3211 3211 delicious joy
## 3212 3212 delicious positive
## 3213 3213 delight anticipation
## 3214 3214 delight joy
## 3215 3215 delight positive
## 3216 3216 delighted anticipation
## 3217 3217 delighted joy
## 3218 3218 delighted positive
## 3219 3219 delighted surprise
## 3220 3220 delightful anticipation
## 3221 3221 delightful joy
## 3222 3222 delightful positive
## 3223 3223 delightful trust
## 3224 3224 delinquency negative
## 3225 3225 delinquent anger
## 3226 3226 delinquent disgust
## 3227 3227 delinquent negative
## 3228 3228 delirious negative
## 3229 3229 delirious sadness
## 3230 3230 delirium disgust
## 3231 3231 delirium negative
## 3232 3232 delirium sadness
## 3233 3233 deliverance anticipation
## 3234 3234 deliverance joy
## 3235 3235 deliverance positive
## 3236 3236 deliverance trust
## 3237 3237 delivery anticipation
## 3238 3238 delivery positive
## 3239 3239 deluge fear
## 3240 3240 deluge negative
## 3241 3241 deluge sadness
## 3242 3242 deluge surprise
## 3243 3243 delusion anger
## 3244 3244 delusion fear
## 3245 3245 delusion negative
## 3246 3246 delusion sadness
## 3247 3247 delusional anger
## 3248 3248 delusional fear
## 3249 3249 delusional negative
## 3250 3250 demand anger
## 3251 3251 demand negative
## 3252 3252 demanding negative
## 3253 3253 demented fear
## 3254 3254 demented negative
## 3255 3255 dementia fear
## 3256 3256 dementia negative
## 3257 3257 dementia sadness
## 3258 3258 demise fear
## 3259 3259 demise negative
## 3260 3260 demise sadness
## 3261 3261 democracy positive
## 3262 3262 demolish anger
## 3263 3263 demolish negative
## 3264 3264 demolish sadness
## 3265 3265 demolition negative
## 3266 3266 demon anger
## 3267 3267 demon disgust
## 3268 3268 demon fear
## 3269 3269 demon negative
## 3270 3270 demon sadness
## 3271 3271 demonic anger
## 3272 3272 demonic disgust
## 3273 3273 demonic fear
## 3274 3274 demonic negative
## 3275 3275 demonic sadness
## 3276 3276 demonstrable positive
## 3277 3277 demonstrative joy
## 3278 3278 demonstrative positive
## 3279 3279 demonstrative sadness
## 3280 3280 demoralized fear
## 3281 3281 demoralized negative
## 3282 3282 demoralized sadness
## 3283 3283 denial negative
## 3284 3284 denied negative
## 3285 3285 denied sadness
## 3286 3286 denounce anger
## 3287 3287 denounce disgust
## 3288 3288 denounce negative
## 3289 3289 dentistry fear
## 3290 3290 denunciation anger
## 3291 3291 denunciation disgust
## 3292 3292 denunciation fear
## 3293 3293 denunciation negative
## 3294 3294 deny anger
## 3295 3295 deny negative
## 3296 3296 denying anticipation
## 3297 3297 denying negative
## 3298 3298 depart anticipation
## 3299 3299 depart sadness
## 3300 3300 departed negative
## 3301 3301 departed sadness
## 3302 3302 departure negative
## 3303 3303 departure sadness
## 3304 3304 depend anticipation
## 3305 3305 depend trust
## 3306 3306 dependence fear
## 3307 3307 dependence negative
## 3308 3308 dependence sadness
## 3309 3309 dependency negative
## 3310 3310 dependent negative
## 3311 3311 dependent positive
## 3312 3312 dependent trust
## 3313 3313 deplorable anger
## 3314 3314 deplorable disgust
## 3315 3315 deplorable fear
## 3316 3316 deplorable negative
## 3317 3317 deplorable sadness
## 3318 3318 deplore anger
## 3319 3319 deplore disgust
## 3320 3320 deplore negative
## 3321 3321 deplore sadness
## 3322 3322 deport fear
## 3323 3323 deport negative
## 3324 3324 deport sadness
## 3325 3325 deportation anger
## 3326 3326 deportation fear
## 3327 3327 deportation negative
## 3328 3328 deportation sadness
## 3329 3329 depository trust
## 3330 3330 depraved anger
## 3331 3331 depraved anticipation
## 3332 3332 depraved disgust
## 3333 3333 depraved fear
## 3334 3334 depraved negative
## 3335 3335 depraved sadness
## 3336 3336 depravity anger
## 3337 3337 depravity disgust
## 3338 3338 depravity negative
## 3339 3339 depreciate anger
## 3340 3340 depreciate disgust
## 3341 3341 depreciate negative
## 3342 3342 depreciated anger
## 3343 3343 depreciated disgust
## 3344 3344 depreciated fear
## 3345 3345 depreciated negative
## 3346 3346 depreciated sadness
## 3347 3347 depreciation fear
## 3348 3348 depreciation negative
## 3349 3349 depress fear
## 3350 3350 depress negative
## 3351 3351 depress sadness
## 3352 3352 depressed anger
## 3353 3353 depressed fear
## 3354 3354 depressed negative
## 3355 3355 depressed sadness
## 3356 3356 depressing disgust
## 3357 3357 depressing negative
## 3358 3358 depressing sadness
## 3359 3359 depression negative
## 3360 3360 depression sadness
## 3361 3361 depressive negative
## 3362 3362 depressive sadness
## 3363 3363 deprivation anger
## 3364 3364 deprivation disgust
## 3365 3365 deprivation fear
## 3366 3366 deprivation negative
## 3367 3367 deprivation sadness
## 3368 3368 depth positive
## 3369 3369 depth trust
## 3370 3370 deputy trust
## 3371 3371 deranged anger
## 3372 3372 deranged disgust
## 3373 3373 deranged fear
## 3374 3374 deranged negative
## 3375 3375 derelict negative
## 3376 3376 derision anger
## 3377 3377 derision disgust
## 3378 3378 derision negative
## 3379 3379 dermatologist trust
## 3380 3380 derogation anger
## 3381 3381 derogation disgust
## 3382 3382 derogation fear
## 3383 3383 derogation negative
## 3384 3384 derogation sadness
## 3385 3385 derogatory anger
## 3386 3386 derogatory disgust
## 3387 3387 derogatory fear
## 3388 3388 derogatory negative
## 3389 3389 derogatory sadness
## 3390 3390 descent fear
## 3391 3391 descent sadness
## 3392 3392 descriptive positive
## 3393 3393 desecration anger
## 3394 3394 desecration disgust
## 3395 3395 desecration fear
## 3396 3396 desecration negative
## 3397 3397 desecration sadness
## 3398 3398 desert anger
## 3399 3399 desert disgust
## 3400 3400 desert fear
## 3401 3401 desert negative
## 3402 3402 desert sadness
## 3403 3403 deserted anger
## 3404 3404 deserted disgust
## 3405 3405 deserted fear
## 3406 3406 deserted negative
## 3407 3407 deserted sadness
## 3408 3408 desertion negative
## 3409 3409 deserve anger
## 3410 3410 deserve anticipation
## 3411 3411 deserve positive
## 3412 3412 deserve trust
## 3413 3413 deserved positive
## 3414 3414 designation trust
## 3415 3415 designer positive
## 3416 3416 desirable positive
## 3417 3417 desiring positive
## 3418 3418 desirous positive
## 3419 3419 desist anger
## 3420 3420 desist disgust
## 3421 3421 desist negative
## 3422 3422 desolation fear
## 3423 3423 desolation negative
## 3424 3424 desolation sadness
## 3425 3425 despair anger
## 3426 3426 despair disgust
## 3427 3427 despair fear
## 3428 3428 despair negative
## 3429 3429 despair sadness
## 3430 3430 despairing fear
## 3431 3431 despairing negative
## 3432 3432 despairing sadness
## 3433 3433 desperate negative
## 3434 3434 despicable anger
## 3435 3435 despicable disgust
## 3436 3436 despicable negative
## 3437 3437 despise anger
## 3438 3438 despise disgust
## 3439 3439 despise negative
## 3440 3440 despotic fear
## 3441 3441 despotic negative
## 3442 3442 despotism anger
## 3443 3443 despotism disgust
## 3444 3444 despotism fear
## 3445 3445 despotism negative
## 3446 3446 despotism sadness
## 3447 3447 destination anticipation
## 3448 3448 destination fear
## 3449 3449 destination joy
## 3450 3450 destination positive
## 3451 3451 destination sadness
## 3452 3452 destination surprise
## 3453 3453 destined anticipation
## 3454 3454 destitute fear
## 3455 3455 destitute negative
## 3456 3456 destitute sadness
## 3457 3457 destroyed anger
## 3458 3458 destroyed fear
## 3459 3459 destroyed negative
## 3460 3460 destroyed sadness
## 3461 3461 destroyer anger
## 3462 3462 destroyer fear
## 3463 3463 destroyer negative
## 3464 3464 destroying anger
## 3465 3465 destroying fear
## 3466 3466 destroying negative
## 3467 3467 destroying sadness
## 3468 3468 destruction anger
## 3469 3469 destruction negative
## 3470 3470 destructive anger
## 3471 3471 destructive disgust
## 3472 3472 destructive fear
## 3473 3473 destructive negative
## 3474 3474 detachment negative
## 3475 3475 detain negative
## 3476 3476 detainee anger
## 3477 3477 detainee anticipation
## 3478 3478 detainee fear
## 3479 3479 detainee negative
## 3480 3480 detainee sadness
## 3481 3481 detect positive
## 3482 3482 detection positive
## 3483 3483 detention negative
## 3484 3484 detention sadness
## 3485 3485 deteriorate fear
## 3486 3486 deteriorate negative
## 3487 3487 deteriorate sadness
## 3488 3488 deteriorated disgust
## 3489 3489 deteriorated negative
## 3490 3490 deteriorated sadness
## 3491 3491 deterioration anger
## 3492 3492 deterioration disgust
## 3493 3493 deterioration fear
## 3494 3494 deterioration negative
## 3495 3495 deterioration sadness
## 3496 3496 determinate anticipation
## 3497 3497 determinate trust
## 3498 3498 determination positive
## 3499 3499 determination trust
## 3500 3500 determined positive
## 3501 3501 detest anger
## 3502 3502 detest disgust
## 3503 3503 detest negative
## 3504 3504 detonate fear
## 3505 3505 detonate negative
## 3506 3506 detonate surprise
## 3507 3507 detonation anger
## 3508 3508 detract anger
## 3509 3509 detract negative
## 3510 3510 detriment negative
## 3511 3511 detrimental negative
## 3512 3512 detritus negative
## 3513 3513 devastate anger
## 3514 3514 devastate fear
## 3515 3515 devastate negative
## 3516 3516 devastate sadness
## 3517 3517 devastating anger
## 3518 3518 devastating disgust
## 3519 3519 devastating fear
## 3520 3520 devastating negative
## 3521 3521 devastating sadness
## 3522 3522 devastating trust
## 3523 3523 devastation anger
## 3524 3524 devastation fear
## 3525 3525 devastation negative
## 3526 3526 devastation sadness
## 3527 3527 devastation surprise
## 3528 3528 develop anticipation
## 3529 3529 develop positive
## 3530 3530 deviation sadness
## 3531 3531 devil anger
## 3532 3532 devil anticipation
## 3533 3533 devil disgust
## 3534 3534 devil fear
## 3535 3535 devil negative
## 3536 3536 devil sadness
## 3537 3537 devilish disgust
## 3538 3538 devilish fear
## 3539 3539 devilish negative
## 3540 3540 devious negative
## 3541 3541 devolution negative
## 3542 3542 devotional positive
## 3543 3543 devotional trust
## 3544 3544 devour negative
## 3545 3545 devout anticipation
## 3546 3546 devout joy
## 3547 3547 devout positive
## 3548 3548 devout trust
## 3549 3549 dexterity positive
## 3550 3550 diabolical anger
## 3551 3551 diabolical disgust
## 3552 3552 diabolical fear
## 3553 3553 diabolical negative
## 3554 3554 diagnosis anticipation
## 3555 3555 diagnosis fear
## 3556 3556 diagnosis negative
## 3557 3557 diagnosis trust
## 3558 3558 diamond joy
## 3559 3559 diamond positive
## 3560 3560 diaper disgust
## 3561 3561 diarrhoea disgust
## 3562 3562 diary joy
## 3563 3563 diary positive
## 3564 3564 diary trust
## 3565 3565 diatribe anger
## 3566 3566 diatribe disgust
## 3567 3567 diatribe negative
## 3568 3568 dictator fear
## 3569 3569 dictator negative
## 3570 3570 dictatorial anger
## 3571 3571 dictatorial negative
## 3572 3572 dictatorship anger
## 3573 3573 dictatorship anticipation
## 3574 3574 dictatorship disgust
## 3575 3575 dictatorship fear
## 3576 3576 dictatorship negative
## 3577 3577 dictatorship sadness
## 3578 3578 dictionary positive
## 3579 3579 dictionary trust
## 3580 3580 dictum trust
## 3581 3581 didactic positive
## 3582 3582 die fear
## 3583 3583 die negative
## 3584 3584 die sadness
## 3585 3585 dietary anticipation
## 3586 3586 dietary positive
## 3587 3587 differential trust
## 3588 3588 differently surprise
## 3589 3589 difficult fear
## 3590 3590 difficulties negative
## 3591 3591 difficulties sadness
## 3592 3592 difficulty anger
## 3593 3593 difficulty fear
## 3594 3594 difficulty negative
## 3595 3595 difficulty sadness
## 3596 3596 digit trust
## 3597 3597 dignified positive
## 3598 3598 dignity positive
## 3599 3599 dignity trust
## 3600 3600 digress anticipation
## 3601 3601 digress negative
## 3602 3602 dike fear
## 3603 3603 dilapidated disgust
## 3604 3604 dilapidated negative
## 3605 3605 dilapidated sadness
## 3606 3606 diligence positive
## 3607 3607 diligence trust
## 3608 3608 dilute negative
## 3609 3609 diminish negative
## 3610 3610 diminish sadness
## 3611 3611 diminished negative
## 3612 3612 din negative
## 3613 3613 dinner positive
## 3614 3614 dinosaur fear
## 3615 3615 diplomacy anticipation
## 3616 3616 diplomacy positive
## 3617 3617 diplomacy trust
## 3618 3618 diplomatic positive
## 3619 3619 diplomatic trust
## 3620 3620 dire disgust
## 3621 3621 dire fear
## 3622 3622 dire negative
## 3623 3623 dire sadness
## 3624 3624 dire surprise
## 3625 3625 director positive
## 3626 3626 director trust
## 3627 3627 dirt disgust
## 3628 3628 dirt negative
## 3629 3629 dirty disgust
## 3630 3630 dirty negative
## 3631 3631 disability negative
## 3632 3632 disability sadness
## 3633 3633 disable fear
## 3634 3634 disable negative
## 3635 3635 disable sadness
## 3636 3636 disabled fear
## 3637 3637 disabled negative
## 3638 3638 disabled sadness
## 3639 3639 disaffected negative
## 3640 3640 disagree anger
## 3641 3641 disagree negative
## 3642 3642 disagreeing anger
## 3643 3643 disagreeing negative
## 3644 3644 disagreeing sadness
## 3645 3645 disagreement anger
## 3646 3646 disagreement negative
## 3647 3647 disagreement sadness
## 3648 3648 disallowed anger
## 3649 3649 disallowed disgust
## 3650 3650 disallowed fear
## 3651 3651 disallowed negative
## 3652 3652 disallowed sadness
## 3653 3653 disappear fear
## 3654 3654 disappoint anger
## 3655 3655 disappoint disgust
## 3656 3656 disappoint negative
## 3657 3657 disappoint sadness
## 3658 3658 disappointed anger
## 3659 3659 disappointed disgust
## 3660 3660 disappointed negative
## 3661 3661 disappointed sadness
## 3662 3662 disappointing negative
## 3663 3663 disappointing sadness
## 3664 3664 disappointment disgust
## 3665 3665 disappointment negative
## 3666 3666 disappointment sadness
## 3667 3667 disapproval negative
## 3668 3668 disapproval sadness
## 3669 3669 disapprove anger
## 3670 3670 disapprove disgust
## 3671 3671 disapprove fear
## 3672 3672 disapprove negative
## 3673 3673 disapprove sadness
## 3674 3674 disapproved anger
## 3675 3675 disapproved negative
## 3676 3676 disapproved sadness
## 3677 3677 disapproving anger
## 3678 3678 disapproving disgust
## 3679 3679 disapproving negative
## 3680 3680 disapproving sadness
## 3681 3681 disaster anger
## 3682 3682 disaster disgust
## 3683 3683 disaster fear
## 3684 3684 disaster negative
## 3685 3685 disaster sadness
## 3686 3686 disaster surprise
## 3687 3687 disastrous anger
## 3688 3688 disastrous fear
## 3689 3689 disastrous negative
## 3690 3690 disastrous sadness
## 3691 3691 disbelieve negative
## 3692 3692 discards negative
## 3693 3693 discharge negative
## 3694 3694 disciple trust
## 3695 3695 discipline fear
## 3696 3696 discipline negative
## 3697 3697 disclaim anger
## 3698 3698 disclaim disgust
## 3699 3699 disclaim negative
## 3700 3700 disclaim trust
## 3701 3701 disclosed trust
## 3702 3702 discoloration disgust
## 3703 3703 discoloration negative
## 3704 3704 discolored disgust
## 3705 3705 discolored negative
## 3706 3706 discolored sadness
## 3707 3707 discomfort negative
## 3708 3708 discomfort sadness
## 3709 3709 disconnect negative
## 3710 3710 disconnect sadness
## 3711 3711 disconnected negative
## 3712 3712 disconnected sadness
## 3713 3713 disconnection negative
## 3714 3714 discontent anger
## 3715 3715 discontent disgust
## 3716 3716 discontent fear
## 3717 3717 discontent negative
## 3718 3718 discontent sadness
## 3719 3719 discontinue negative
## 3720 3720 discontinuity disgust
## 3721 3721 discontinuity fear
## 3722 3722 discontinuity negative
## 3723 3723 discontinuity sadness
## 3724 3724 discord anger
## 3725 3725 discord disgust
## 3726 3726 discord negative
## 3727 3727 discourage fear
## 3728 3728 discourage negative
## 3729 3729 discourage sadness
## 3730 3730 discouragement negative
## 3731 3731 discovery positive
## 3732 3732 discredit negative
## 3733 3733 discreet anticipation
## 3734 3734 discreet positive
## 3735 3735 discretion anticipation
## 3736 3736 discretion positive
## 3737 3737 discretion trust
## 3738 3738 discretionary positive
## 3739 3739 discriminate anger
## 3740 3740 discriminate negative
## 3741 3741 discriminate sadness
## 3742 3742 discriminating disgust
## 3743 3743 discriminating negative
## 3744 3744 discrimination anger
## 3745 3745 discrimination disgust
## 3746 3746 discrimination fear
## 3747 3747 discrimination negative
## 3748 3748 discrimination sadness
## 3749 3749 discussion positive
## 3750 3750 disdain anger
## 3751 3751 disdain disgust
## 3752 3752 disdain negative
## 3753 3753 disease anger
## 3754 3754 disease disgust
## 3755 3755 disease fear
## 3756 3756 disease negative
## 3757 3757 disease sadness
## 3758 3758 diseased disgust
## 3759 3759 diseased fear
## 3760 3760 diseased negative
## 3761 3761 diseased sadness
## 3762 3762 disembodied fear
## 3763 3763 disembodied negative
## 3764 3764 disembodied sadness
## 3765 3765 disengagement negative
## 3766 3766 disfigured anger
## 3767 3767 disfigured disgust
## 3768 3768 disfigured fear
## 3769 3769 disfigured negative
## 3770 3770 disfigured sadness
## 3771 3771 disgrace anger
## 3772 3772 disgrace disgust
## 3773 3773 disgrace negative
## 3774 3774 disgrace sadness
## 3775 3775 disgraced anger
## 3776 3776 disgraced disgust
## 3777 3777 disgraced negative
## 3778 3778 disgraced sadness
## 3779 3779 disgraceful anger
## 3780 3780 disgraceful disgust
## 3781 3781 disgraceful negative
## 3782 3782 disgruntled anger
## 3783 3783 disgruntled disgust
## 3784 3784 disgruntled negative
## 3785 3785 disgruntled sadness
## 3786 3786 disgust anger
## 3787 3787 disgust disgust
## 3788 3788 disgust fear
## 3789 3789 disgust negative
## 3790 3790 disgust sadness
## 3791 3791 disgusting anger
## 3792 3792 disgusting disgust
## 3793 3793 disgusting fear
## 3794 3794 disgusting negative
## 3795 3795 disheartened negative
## 3796 3796 disheartened sadness
## 3797 3797 disheartening negative
## 3798 3798 disheartening sadness
## 3799 3799 dishonest anger
## 3800 3800 dishonest disgust
## 3801 3801 dishonest negative
## 3802 3802 dishonest sadness
## 3803 3803 dishonesty disgust
## 3804 3804 dishonesty negative
## 3805 3805 dishonor anger
## 3806 3806 dishonor disgust
## 3807 3807 dishonor fear
## 3808 3808 dishonor negative
## 3809 3809 dishonor sadness
## 3810 3810 disillusionment anger
## 3811 3811 disillusionment disgust
## 3812 3812 disillusionment negative
## 3813 3813 disillusionment sadness
## 3814 3814 disinfection positive
## 3815 3815 disinformation anger
## 3816 3816 disinformation fear
## 3817 3817 disinformation negative
## 3818 3818 disingenuous disgust
## 3819 3819 disingenuous negative
## 3820 3820 disintegrate disgust
## 3821 3821 disintegrate fear
## 3822 3822 disintegrate negative
## 3823 3823 disintegration negative
## 3824 3824 disinterested negative
## 3825 3825 dislike anger
## 3826 3826 dislike disgust
## 3827 3827 dislike negative
## 3828 3828 disliked anger
## 3829 3829 disliked negative
## 3830 3830 disliked sadness
## 3831 3831 dislocated anger
## 3832 3832 dislocated disgust
## 3833 3833 dislocated fear
## 3834 3834 dislocated negative
## 3835 3835 dislocated sadness
## 3836 3836 dismal disgust
## 3837 3837 dismal fear
## 3838 3838 dismal negative
## 3839 3839 dismal sadness
## 3840 3840 dismay anger
## 3841 3841 dismay anticipation
## 3842 3842 dismay fear
## 3843 3843 dismay negative
## 3844 3844 dismay sadness
## 3845 3845 dismay surprise
## 3846 3846 dismemberment disgust
## 3847 3847 dismemberment fear
## 3848 3848 dismemberment negative
## 3849 3849 dismemberment sadness
## 3850 3850 dismissal anger
## 3851 3851 dismissal disgust
## 3852 3852 dismissal fear
## 3853 3853 dismissal negative
## 3854 3854 dismissal sadness
## 3855 3855 dismissal surprise
## 3856 3856 disobedience anger
## 3857 3857 disobedience disgust
## 3858 3858 disobedience negative
## 3859 3859 disobedient anger
## 3860 3860 disobedient negative
## 3861 3861 disobey anger
## 3862 3862 disobey disgust
## 3863 3863 disobey negative
## 3864 3864 disorder fear
## 3865 3865 disorder negative
## 3866 3866 disorderly negative
## 3867 3867 disorganized negative
## 3868 3868 disparage anger
## 3869 3869 disparage disgust
## 3870 3870 disparage negative
## 3871 3871 disparage sadness
## 3872 3872 disparaging anger
## 3873 3873 disparaging disgust
## 3874 3874 disparaging negative
## 3875 3875 disparaging sadness
## 3876 3876 disparity anger
## 3877 3877 disparity disgust
## 3878 3878 disparity negative
## 3879 3879 disparity sadness
## 3880 3880 dispassionate negative
## 3881 3881 dispassionate sadness
## 3882 3882 dispel negative
## 3883 3883 dispel sadness
## 3884 3884 dispersion negative
## 3885 3885 displace negative
## 3886 3886 displaced anger
## 3887 3887 displaced fear
## 3888 3888 displaced sadness
## 3889 3889 displeased anger
## 3890 3890 displeased disgust
## 3891 3891 displeased fear
## 3892 3892 displeased negative
## 3893 3893 displeased sadness
## 3894 3894 displeasure disgust
## 3895 3895 displeasure negative
## 3896 3896 disposal negative
## 3897 3897 dispose disgust
## 3898 3898 disposed anticipation
## 3899 3899 disposed positive
## 3900 3900 disposed trust
## 3901 3901 dispossessed anger
## 3902 3902 dispossessed fear
## 3903 3903 dispossessed negative
## 3904 3904 dispossessed sadness
## 3905 3905 dispute anger
## 3906 3906 dispute negative
## 3907 3907 disqualification negative
## 3908 3908 disqualified anger
## 3909 3909 disqualified disgust
## 3910 3910 disqualified negative
## 3911 3911 disqualified sadness
## 3912 3912 disqualify negative
## 3913 3913 disqualify sadness
## 3914 3914 disregard negative
## 3915 3915 disregarded disgust
## 3916 3916 disregarded negative
## 3917 3917 disreputable anger
## 3918 3918 disreputable disgust
## 3919 3919 disreputable fear
## 3920 3920 disreputable negative
## 3921 3921 disrespect anger
## 3922 3922 disrespect negative
## 3923 3923 disrespectful anger
## 3924 3924 disrespectful disgust
## 3925 3925 disrespectful fear
## 3926 3926 disrespectful negative
## 3927 3927 disrespectful sadness
## 3928 3928 disruption anger
## 3929 3929 disruption fear
## 3930 3930 disruption negative
## 3931 3931 disruption surprise
## 3932 3932 dissatisfaction negative
## 3933 3933 dissection disgust
## 3934 3934 disseminate positive
## 3935 3935 dissension anger
## 3936 3936 dissension negative
## 3937 3937 dissenting negative
## 3938 3938 disservice anger
## 3939 3939 disservice disgust
## 3940 3940 disservice negative
## 3941 3941 disservice sadness
## 3942 3942 dissident anger
## 3943 3943 dissident fear
## 3944 3944 dissident negative
## 3945 3945 dissolution anger
## 3946 3946 dissolution fear
## 3947 3947 dissolution negative
## 3948 3948 dissolution sadness
## 3949 3949 dissolution surprise
## 3950 3950 dissonance anger
## 3951 3951 dissonance negative
## 3952 3952 distaste disgust
## 3953 3953 distaste negative
## 3954 3954 distasteful disgust
## 3955 3955 distasteful negative
## 3956 3956 distillation positive
## 3957 3957 distinction positive
## 3958 3958 distorted disgust
## 3959 3959 distorted negative
## 3960 3960 distortion negative
## 3961 3961 distract negative
## 3962 3962 distracted anger
## 3963 3963 distracted negative
## 3964 3964 distracting anger
## 3965 3965 distracting anticipation
## 3966 3966 distracting negative
## 3967 3967 distraction negative
## 3968 3968 distraught negative
## 3969 3969 distraught sadness
## 3970 3970 distress anger
## 3971 3971 distress disgust
## 3972 3972 distress fear
## 3973 3973 distress negative
## 3974 3974 distress sadness
## 3975 3975 distress surprise
## 3976 3976 distressed fear
## 3977 3977 distressed negative
## 3978 3978 distressing anger
## 3979 3979 distressing fear
## 3980 3980 distressing negative
## 3981 3981 distrust anger
## 3982 3982 distrust disgust
## 3983 3983 distrust fear
## 3984 3984 distrust negative
## 3985 3985 disturbance anger
## 3986 3986 disturbance fear
## 3987 3987 disturbance negative
## 3988 3988 disturbance sadness
## 3989 3989 disturbance surprise
## 3990 3990 disturbed anger
## 3991 3991 disturbed negative
## 3992 3992 disturbed sadness
## 3993 3993 disuse negative
## 3994 3994 disused anger
## 3995 3995 disused negative
## 3996 3996 ditty joy
## 3997 3997 ditty positive
## 3998 3998 divan trust
## 3999 3999 divergent negative
## 4000 4000 divergent surprise
## 4001 4001 diverse negative
## 4002 4002 diverse positive
## 4003 4003 diversified positive
## 4004 4004 diversion positive
## 4005 4005 diversion surprise
## 4006 4006 divested negative
## 4007 4007 divestment negative
## 4008 4008 divination anticipation
## 4009 4009 divinity positive
## 4010 4010 divorce anger
## 4011 4011 divorce disgust
## 4012 4012 divorce fear
## 4013 4013 divorce negative
## 4014 4014 divorce sadness
## 4015 4015 divorce surprise
## 4016 4016 divorce trust
## 4017 4017 dizziness negative
## 4018 4018 dizzy negative
## 4019 4019 docked negative
## 4020 4020 doctor positive
## 4021 4021 doctor trust
## 4022 4022 doctrine trust
## 4023 4023 doer positive
## 4024 4024 dogged positive
## 4025 4025 dogma trust
## 4026 4026 doit negative
## 4027 4027 doldrums negative
## 4028 4028 doldrums sadness
## 4029 4029 dole negative
## 4030 4030 dole sadness
## 4031 4031 doll joy
## 4032 4032 dolor negative
## 4033 4033 dolor sadness
## 4034 4034 dolphin joy
## 4035 4035 dolphin positive
## 4036 4036 dolphin surprise
## 4037 4037 dolphin trust
## 4038 4038 dominant fear
## 4039 4039 dominant negative
## 4040 4040 dominate anger
## 4041 4041 dominate fear
## 4042 4042 dominate negative
## 4043 4043 dominate positive
## 4044 4044 domination anger
## 4045 4045 domination fear
## 4046 4046 domination negative
## 4047 4047 domination sadness
## 4048 4048 dominion fear
## 4049 4049 dominion trust
## 4050 4050 don positive
## 4051 4051 don trust
## 4052 4052 donation positive
## 4053 4053 donkey disgust
## 4054 4054 donkey negative
## 4055 4055 doodle negative
## 4056 4056 doom fear
## 4057 4057 doom negative
## 4058 4058 doomed fear
## 4059 4059 doomed negative
## 4060 4060 doomed sadness
## 4061 4061 doomsday anger
## 4062 4062 doomsday anticipation
## 4063 4063 doomsday disgust
## 4064 4064 doomsday fear
## 4065 4065 doomsday negative
## 4066 4066 doomsday sadness
## 4067 4067 doubt fear
## 4068 4068 doubt negative
## 4069 4069 doubt sadness
## 4070 4070 doubt trust
## 4071 4071 doubtful negative
## 4072 4072 doubting negative
## 4073 4073 doubtless positive
## 4074 4074 doubtless trust
## 4075 4075 douche negative
## 4076 4076 dour negative
## 4077 4077 dove anticipation
## 4078 4078 dove joy
## 4079 4079 dove positive
## 4080 4080 dove trust
## 4081 4081 downfall fear
## 4082 4082 downfall negative
## 4083 4083 downfall sadness
## 4084 4084 downright trust
## 4085 4085 downy positive
## 4086 4086 drab negative
## 4087 4087 drab sadness
## 4088 4088 draft anticipation
## 4089 4089 dragon fear
## 4090 4090 drainage negative
## 4091 4091 drawback negative
## 4092 4092 dread anticipation
## 4093 4093 dread fear
## 4094 4094 dread negative
## 4095 4095 dreadful anger
## 4096 4096 dreadful anticipation
## 4097 4097 dreadful disgust
## 4098 4098 dreadful fear
## 4099 4099 dreadful negative
## 4100 4100 dreadful sadness
## 4101 4101 dreadfully disgust
## 4102 4102 dreadfully fear
## 4103 4103 dreadfully negative
## 4104 4104 dreadfully sadness
## 4105 4105 dreadfully surprise
## 4106 4106 dreary negative
## 4107 4107 dreary sadness
## 4108 4108 drinking negative
## 4109 4109 drivel disgust
## 4110 4110 drivel negative
## 4111 4111 drone negative
## 4112 4112 drool disgust
## 4113 4113 drooping negative
## 4114 4114 drought negative
## 4115 4115 drown fear
## 4116 4116 drown negative
## 4117 4117 drown sadness
## 4118 4118 drowsiness negative
## 4119 4119 drudgery negative
## 4120 4120 drugged sadness
## 4121 4121 drunken disgust
## 4122 4122 drunken negative
## 4123 4123 drunkenness negative
## 4124 4124 dubious fear
## 4125 4125 dubious negative
## 4126 4126 dubious trust
## 4127 4127 duel anger
## 4128 4128 duel anticipation
## 4129 4129 duel fear
## 4130 4130 duet positive
## 4131 4131 duke positive
## 4132 4132 dull negative
## 4133 4133 dull sadness
## 4134 4134 dumb negative
## 4135 4135 dummy negative
## 4136 4136 dumps anger
## 4137 4137 dumps negative
## 4138 4138 dumps sadness
## 4139 4139 dun negative
## 4140 4140 dung disgust
## 4141 4141 dungeon fear
## 4142 4142 dungeon negative
## 4143 4143 dupe anger
## 4144 4144 dupe negative
## 4145 4145 duplicity anger
## 4146 4146 duplicity negative
## 4147 4147 durability positive
## 4148 4148 durability trust
## 4149 4149 durable positive
## 4150 4150 durable trust
## 4151 4151 duress anger
## 4152 4152 duress disgust
## 4153 4153 duress fear
## 4154 4154 duress negative
## 4155 4155 duress sadness
## 4156 4156 dust negative
## 4157 4157 dutiful anticipation
## 4158 4158 dutiful positive
## 4159 4159 dutiful trust
## 4160 4160 dwarfed fear
## 4161 4161 dwarfed negative
## 4162 4162 dwarfed sadness
## 4163 4163 dying anger
## 4164 4164 dying disgust
## 4165 4165 dying fear
## 4166 4166 dying negative
## 4167 4167 dying sadness
## 4168 4168 dynamic surprise
## 4169 4169 dysentery disgust
## 4170 4170 dysentery negative
## 4171 4171 dysentery sadness
## 4172 4172 eager anticipation
## 4173 4173 eager joy
## 4174 4174 eager positive
## 4175 4175 eager surprise
## 4176 4176 eager trust
## 4177 4177 eagerness anticipation
## 4178 4178 eagerness joy
## 4179 4179 eagerness positive
## 4180 4180 eagerness trust
## 4181 4181 eagle trust
## 4182 4182 earl positive
## 4183 4183 earn positive
## 4184 4184 earnest positive
## 4185 4185 earnestly positive
## 4186 4186 earnestness positive
## 4187 4187 earthquake anger
## 4188 4188 earthquake fear
## 4189 4189 earthquake negative
## 4190 4190 earthquake sadness
## 4191 4191 earthquake surprise
## 4192 4192 ease positive
## 4193 4193 easement positive
## 4194 4194 easygoing positive
## 4195 4195 eat positive
## 4196 4196 eavesdropping negative
## 4197 4197 economy trust
## 4198 4198 ecstasy anticipation
## 4199 4199 ecstasy joy
## 4200 4200 ecstasy positive
## 4201 4201 ecstatic anticipation
## 4202 4202 ecstatic joy
## 4203 4203 ecstatic positive
## 4204 4204 ecstatic surprise
## 4205 4205 edict fear
## 4206 4206 edict negative
## 4207 4207 edification anticipation
## 4208 4208 edification joy
## 4209 4209 edification positive
## 4210 4210 edification trust
## 4211 4211 edition anticipation
## 4212 4212 educate positive
## 4213 4213 educated positive
## 4214 4214 educational positive
## 4215 4215 educational trust
## 4216 4216 eel fear
## 4217 4217 effective positive
## 4218 4218 effective trust
## 4219 4219 effeminate negative
## 4220 4220 efficacy positive
## 4221 4221 efficiency positive
## 4222 4222 efficient anticipation
## 4223 4223 efficient positive
## 4224 4224 efficient trust
## 4225 4225 effigy anger
## 4226 4226 effort positive
## 4227 4227 egotistical disgust
## 4228 4228 egotistical negative
## 4229 4229 egregious anger
## 4230 4230 egregious disgust
## 4231 4231 egregious negative
## 4232 4232 ejaculation anticipation
## 4233 4233 ejaculation joy
## 4234 4234 ejaculation positive
## 4235 4235 ejaculation surprise
## 4236 4236 ejaculation trust
## 4237 4237 eject negative
## 4238 4238 ejection negative
## 4239 4239 elaboration positive
## 4240 4240 elated joy
## 4241 4241 elated positive
## 4242 4242 elbow anger
## 4243 4243 elder positive
## 4244 4244 elder trust
## 4245 4245 elders positive
## 4246 4246 elders trust
## 4247 4247 elect positive
## 4248 4248 elect trust
## 4249 4249 electorate trust
## 4250 4250 electric joy
## 4251 4251 electric positive
## 4252 4252 electric surprise
## 4253 4253 electricity positive
## 4254 4254 elegance anticipation
## 4255 4255 elegance joy
## 4256 4256 elegance positive
## 4257 4257 elegance trust
## 4258 4258 elegant joy
## 4259 4259 elegant positive
## 4260 4260 elevation anticipation
## 4261 4261 elevation fear
## 4262 4262 elevation joy
## 4263 4263 elevation positive
## 4264 4264 elevation trust
## 4265 4265 elf anger
## 4266 4266 elf disgust
## 4267 4267 elf fear
## 4268 4268 eligible positive
## 4269 4269 elimination anger
## 4270 4270 elimination disgust
## 4271 4271 elimination fear
## 4272 4272 elimination negative
## 4273 4273 elimination sadness
## 4274 4274 elite anticipation
## 4275 4275 elite joy
## 4276 4276 elite positive
## 4277 4277 elite trust
## 4278 4278 eloquence positive
## 4279 4279 eloquent positive
## 4280 4280 elucidate positive
## 4281 4281 elucidate trust
## 4282 4282 elusive negative
## 4283 4283 elusive surprise
## 4284 4284 emaciated fear
## 4285 4285 emaciated negative
## 4286 4286 emaciated sadness
## 4287 4287 emancipation anticipation
## 4288 4288 emancipation joy
## 4289 4289 emancipation positive
## 4290 4290 embargo negative
## 4291 4291 embarrass negative
## 4292 4292 embarrass sadness
## 4293 4293 embarrassing negative
## 4294 4294 embarrassment fear
## 4295 4295 embarrassment negative
## 4296 4296 embarrassment sadness
## 4297 4297 embarrassment surprise
## 4298 4298 embezzlement negative
## 4299 4299 embolism fear
## 4300 4300 embolism negative
## 4301 4301 embolism sadness
## 4302 4302 embrace anticipation
## 4303 4303 embrace joy
## 4304 4304 embrace positive
## 4305 4305 embrace surprise
## 4306 4306 embrace trust
## 4307 4307 embroiled negative
## 4308 4308 emergency fear
## 4309 4309 emergency negative
## 4310 4310 emergency sadness
## 4311 4311 emergency surprise
## 4312 4312 emeritus positive
## 4313 4313 eminence positive
## 4314 4314 eminence trust
## 4315 4315 eminent positive
## 4316 4316 eminently positive
## 4317 4317 emir positive
## 4318 4318 empathy positive
## 4319 4319 emphasize trust
## 4320 4320 employ trust
## 4321 4321 empower positive
## 4322 4322 emptiness sadness
## 4323 4323 emulate positive
## 4324 4324 enable positive
## 4325 4325 enable trust
## 4326 4326 enablement positive
## 4327 4327 enablement trust
## 4328 4328 enchant anticipation
## 4329 4329 enchant joy
## 4330 4330 enchant positive
## 4331 4331 enchant surprise
## 4332 4332 enchanted joy
## 4333 4333 enchanted positive
## 4334 4334 enchanted trust
## 4335 4335 enchanting anticipation
## 4336 4336 enchanting joy
## 4337 4337 enchanting positive
## 4338 4338 enclave negative
## 4339 4339 encore positive
## 4340 4340 encourage joy
## 4341 4341 encourage positive
## 4342 4342 encourage trust
## 4343 4343 encouragement positive
## 4344 4344 encroachment fear
## 4345 4345 encroachment negative
## 4346 4346 encumbrance anger
## 4347 4347 encumbrance fear
## 4348 4348 encumbrance negative
## 4349 4349 encumbrance sadness
## 4350 4350 encyclopedia positive
## 4351 4351 encyclopedia trust
## 4352 4352 endanger anticipation
## 4353 4353 endanger fear
## 4354 4354 endanger negative
## 4355 4355 endangered fear
## 4356 4356 endangered negative
## 4357 4357 endeavor anticipation
## 4358 4358 endeavor positive
## 4359 4359 endemic disgust
## 4360 4360 endemic fear
## 4361 4361 endemic negative
## 4362 4362 endemic sadness
## 4363 4363 endless anger
## 4364 4364 endless fear
## 4365 4365 endless joy
## 4366 4366 endless negative
## 4367 4367 endless positive
## 4368 4368 endless sadness
## 4369 4369 endless trust
## 4370 4370 endocarditis fear
## 4371 4371 endocarditis sadness
## 4372 4372 endow positive
## 4373 4373 endow trust
## 4374 4374 endowed positive
## 4375 4375 endowment positive
## 4376 4376 endowment trust
## 4377 4377 endurance positive
## 4378 4378 endure positive
## 4379 4379 enema disgust
## 4380 4380 enemy anger
## 4381 4381 enemy disgust
## 4382 4382 enemy fear
## 4383 4383 enemy negative
## 4384 4384 energetic positive
## 4385 4385 enforce anger
## 4386 4386 enforce fear
## 4387 4387 enforce negative
## 4388 4388 enforce positive
## 4389 4389 enforcement negative
## 4390 4390 engaged anticipation
## 4391 4391 engaged joy
## 4392 4392 engaged positive
## 4393 4393 engaged trust
## 4394 4394 engaging joy
## 4395 4395 engaging positive
## 4396 4396 engaging trust
## 4397 4397 engulf anticipation
## 4398 4398 enhance positive
## 4399 4399 enigmatic fear
## 4400 4400 enigmatic negative
## 4401 4401 enjoy anticipation
## 4402 4402 enjoy joy
## 4403 4403 enjoy positive
## 4404 4404 enjoy trust
## 4405 4405 enjoying anticipation
## 4406 4406 enjoying joy
## 4407 4407 enjoying positive
## 4408 4408 enjoying trust
## 4409 4409 enlighten joy
## 4410 4410 enlighten positive
## 4411 4411 enlighten trust
## 4412 4412 enlightenment joy
## 4413 4413 enlightenment positive
## 4414 4414 enlightenment trust
## 4415 4415 enliven joy
## 4416 4416 enliven positive
## 4417 4417 enliven surprise
## 4418 4418 enliven trust
## 4419 4419 enmity anger
## 4420 4420 enmity fear
## 4421 4421 enmity negative
## 4422 4422 enmity sadness
## 4423 4423 enrich positive
## 4424 4424 enroll anticipation
## 4425 4425 enroll trust
## 4426 4426 ensemble positive
## 4427 4427 ensemble trust
## 4428 4428 ensign positive
## 4429 4429 enslave negative
## 4430 4430 enslaved anger
## 4431 4431 enslaved disgust
## 4432 4432 enslaved fear
## 4433 4433 enslaved negative
## 4434 4434 enslaved sadness
## 4435 4435 enslavement negative
## 4436 4436 entangled anger
## 4437 4437 entangled disgust
## 4438 4438 entangled fear
## 4439 4439 entangled negative
## 4440 4440 entangled sadness
## 4441 4441 entanglement negative
## 4442 4442 enterprising positive
## 4443 4443 entertain joy
## 4444 4444 entertain positive
## 4445 4445 entertained joy
## 4446 4446 entertained positive
## 4447 4447 entertaining anticipation
## 4448 4448 entertaining joy
## 4449 4449 entertaining positive
## 4450 4450 entertainment anticipation
## 4451 4451 entertainment joy
## 4452 4452 entertainment positive
## 4453 4453 entertainment surprise
## 4454 4454 entertainment trust
## 4455 4455 enthusiasm anticipation
## 4456 4456 enthusiasm joy
## 4457 4457 enthusiasm positive
## 4458 4458 enthusiasm surprise
## 4459 4459 enthusiast anticipation
## 4460 4460 enthusiast joy
## 4461 4461 enthusiast positive
## 4462 4462 enthusiast surprise
## 4463 4463 entrails disgust
## 4464 4464 entrails negative
## 4465 4465 entrust trust
## 4466 4466 envious negative
## 4467 4467 environ positive
## 4468 4468 ephemeris positive
## 4469 4469 epic positive
## 4470 4470 epidemic anger
## 4471 4471 epidemic anticipation
## 4472 4472 epidemic disgust
## 4473 4473 epidemic fear
## 4474 4474 epidemic negative
## 4475 4475 epidemic sadness
## 4476 4476 epidemic surprise
## 4477 4477 epilepsy negative
## 4478 4478 episcopal trust
## 4479 4479 epitaph sadness
## 4480 4480 epitome positive
## 4481 4481 equality joy
## 4482 4482 equality positive
## 4483 4483 equality trust
## 4484 4484 equally positive
## 4485 4485 equilibrium positive
## 4486 4486 equity positive
## 4487 4487 eradicate anger
## 4488 4488 eradicate negative
## 4489 4489 eradication anger
## 4490 4490 eradication disgust
## 4491 4491 eradication fear
## 4492 4492 eradication negative
## 4493 4493 erase fear
## 4494 4494 erase negative
## 4495 4495 erosion negative
## 4496 4496 erotic anticipation
## 4497 4497 erotic joy
## 4498 4498 erotic negative
## 4499 4499 erotic positive
## 4500 4500 erotic surprise
## 4501 4501 erotic trust
## 4502 4502 err negative
## 4503 4503 errand anticipation
## 4504 4504 errand positive
## 4505 4505 errand trust
## 4506 4506 errant negative
## 4507 4507 erratic negative
## 4508 4508 erratic surprise
## 4509 4509 erratum negative
## 4510 4510 erroneous negative
## 4511 4511 error negative
## 4512 4512 error sadness
## 4513 4513 erudite positive
## 4514 4514 erupt anger
## 4515 4515 erupt negative
## 4516 4516 erupt surprise
## 4517 4517 eruption anger
## 4518 4518 eruption fear
## 4519 4519 eruption negative
## 4520 4520 eruption surprise
## 4521 4521 escalate anger
## 4522 4522 escalate negative
## 4523 4523 escape anticipation
## 4524 4524 escape fear
## 4525 4525 escape negative
## 4526 4526 escape positive
## 4527 4527 escaped fear
## 4528 4528 eschew anger
## 4529 4529 eschew negative
## 4530 4530 eschew sadness
## 4531 4531 escort trust
## 4532 4532 espionage negative
## 4533 4533 esprit positive
## 4534 4534 essential positive
## 4535 4535 establish trust
## 4536 4536 established joy
## 4537 4537 established positive
## 4538 4538 esteem joy
## 4539 4539 esteem positive
## 4540 4540 esteem sadness
## 4541 4541 esteem trust
## 4542 4542 esthetic positive
## 4543 4543 estranged negative
## 4544 4544 ethereal fear
## 4545 4545 ethical positive
## 4546 4546 ethics positive
## 4547 4547 euthanasia fear
## 4548 4548 euthanasia negative
## 4549 4549 euthanasia sadness
## 4550 4550 evacuate fear
## 4551 4551 evacuate negative
## 4552 4552 evacuation negative
## 4553 4553 evade anger
## 4554 4554 evade disgust
## 4555 4555 evade fear
## 4556 4556 evade negative
## 4557 4557 evanescence sadness
## 4558 4558 evanescence surprise
## 4559 4559 evasion fear
## 4560 4560 evasion negative
## 4561 4561 evasion sadness
## 4562 4562 eventual anticipation
## 4563 4563 eventuality anticipation
## 4564 4564 eventuality fear
## 4565 4565 evergreen joy
## 4566 4566 evergreen positive
## 4567 4567 evergreen trust
## 4568 4568 everlasting positive
## 4569 4569 evict negative
## 4570 4570 evict sadness
## 4571 4571 eviction anger
## 4572 4572 eviction disgust
## 4573 4573 eviction fear
## 4574 4574 eviction negative
## 4575 4575 eviction sadness
## 4576 4576 evident positive
## 4577 4577 evident trust
## 4578 4578 evil anger
## 4579 4579 evil disgust
## 4580 4580 evil fear
## 4581 4581 evil negative
## 4582 4582 evil sadness
## 4583 4583 evolution positive
## 4584 4584 exacerbate negative
## 4585 4585 exacerbation anger
## 4586 4586 exacerbation fear
## 4587 4587 exacerbation negative
## 4588 4588 exacting negative
## 4589 4589 exaggerate anger
## 4590 4590 exaggerate negative
## 4591 4591 exaggerated negative
## 4592 4592 exalt anticipation
## 4593 4593 exalt joy
## 4594 4594 exalt positive
## 4595 4595 exalt trust
## 4596 4596 exaltation joy
## 4597 4597 exaltation positive
## 4598 4598 exaltation trust
## 4599 4599 exalted joy
## 4600 4600 exalted positive
## 4601 4601 exalted trust
## 4602 4602 examination fear
## 4603 4603 examination negative
## 4604 4604 examination surprise
## 4605 4605 exasperation anger
## 4606 4606 exasperation disgust
## 4607 4607 exasperation negative
## 4608 4608 excavation anticipation
## 4609 4609 excavation negative
## 4610 4610 excavation surprise
## 4611 4611 exceed anticipation
## 4612 4612 exceed joy
## 4613 4613 exceed positive
## 4614 4614 excel anticipation
## 4615 4615 excel joy
## 4616 4616 excel positive
## 4617 4617 excel surprise
## 4618 4618 excel trust
## 4619 4619 excellence disgust
## 4620 4620 excellence joy
## 4621 4621 excellence positive
## 4622 4622 excellence trust
## 4623 4623 excellent joy
## 4624 4624 excellent positive
## 4625 4625 excellent trust
## 4626 4626 excess negative
## 4627 4627 exchange positive
## 4628 4628 exchange trust
## 4629 4629 excise negative
## 4630 4630 excitable positive
## 4631 4631 excitation anger
## 4632 4632 excitation anticipation
## 4633 4633 excitation fear
## 4634 4634 excitation joy
## 4635 4635 excitation positive
## 4636 4636 excitation surprise
## 4637 4637 excite anger
## 4638 4638 excite anticipation
## 4639 4639 excite fear
## 4640 4640 excite joy
## 4641 4641 excite positive
## 4642 4642 excite surprise
## 4643 4643 excited anticipation
## 4644 4644 excited joy
## 4645 4645 excited positive
## 4646 4646 excited surprise
## 4647 4647 excited trust
## 4648 4648 excitement anticipation
## 4649 4649 excitement joy
## 4650 4650 excitement positive
## 4651 4651 excitement surprise
## 4652 4652 exciting anticipation
## 4653 4653 exciting joy
## 4654 4654 exciting positive
## 4655 4655 exciting surprise
## 4656 4656 exclaim surprise
## 4657 4657 excluded disgust
## 4658 4658 excluded negative
## 4659 4659 excluded sadness
## 4660 4660 excluding negative
## 4661 4661 excluding sadness
## 4662 4662 exclusion disgust
## 4663 4663 exclusion fear
## 4664 4664 exclusion negative
## 4665 4665 exclusion sadness
## 4666 4666 excrement disgust
## 4667 4667 excrement negative
## 4668 4668 excretion disgust
## 4669 4669 excruciating fear
## 4670 4670 excruciating negative
## 4671 4671 excuse negative
## 4672 4672 execution anger
## 4673 4673 execution fear
## 4674 4674 execution negative
## 4675 4675 execution sadness
## 4676 4676 execution trust
## 4677 4677 executioner anger
## 4678 4678 executioner fear
## 4679 4679 executioner negative
## 4680 4680 executioner sadness
## 4681 4681 executor trust
## 4682 4682 exemplary positive
## 4683 4683 exemption positive
## 4684 4684 exhaust negative
## 4685 4685 exhausted negative
## 4686 4686 exhausted sadness
## 4687 4687 exhaustion anticipation
## 4688 4688 exhaustion negative
## 4689 4689 exhaustion sadness
## 4690 4690 exhaustive trust
## 4691 4691 exhilaration joy
## 4692 4692 exhilaration positive
## 4693 4693 exhilaration surprise
## 4694 4694 exhort positive
## 4695 4695 exhortation positive
## 4696 4696 exigent anticipation
## 4697 4697 exigent disgust
## 4698 4698 exigent fear
## 4699 4699 exigent negative
## 4700 4700 exigent surprise
## 4701 4701 exile anger
## 4702 4702 exile fear
## 4703 4703 exile negative
## 4704 4704 exile sadness
## 4705 4705 existence positive
## 4706 4706 exorcism fear
## 4707 4707 exorcism negative
## 4708 4708 exorcism sadness
## 4709 4709 exotic positive
## 4710 4710 expatriate negative
## 4711 4711 expect anticipation
## 4712 4712 expect positive
## 4713 4713 expect surprise
## 4714 4714 expect trust
## 4715 4715 expectancy anticipation
## 4716 4716 expectant anticipation
## 4717 4717 expectation anticipation
## 4718 4718 expectation positive
## 4719 4719 expected anticipation
## 4720 4720 expecting anticipation
## 4721 4721 expedient joy
## 4722 4722 expedient positive
## 4723 4723 expedient trust
## 4724 4724 expedition anticipation
## 4725 4725 expedition positive
## 4726 4726 expel anger
## 4727 4727 expel disgust
## 4728 4728 expel fear
## 4729 4729 expel negative
## 4730 4730 expel sadness
## 4731 4731 expenditure negative
## 4732 4732 expenses negative
## 4733 4733 experienced positive
## 4734 4734 experienced trust
## 4735 4735 experiment anticipation
## 4736 4736 experiment surprise
## 4737 4737 expert positive
## 4738 4738 expert trust
## 4739 4739 expertise positive
## 4740 4740 expertise trust
## 4741 4741 expire negative
## 4742 4742 expire sadness
## 4743 4743 expired negative
## 4744 4744 explain positive
## 4745 4745 explain trust
## 4746 4746 expletive anger
## 4747 4747 expletive negative
## 4748 4748 explode anger
## 4749 4749 explode fear
## 4750 4750 explode negative
## 4751 4751 explode sadness
## 4752 4752 explode surprise
## 4753 4753 explore anticipation
## 4754 4754 explosion fear
## 4755 4755 explosion negative
## 4756 4756 explosion surprise
## 4757 4757 explosive anger
## 4758 4758 explosive anticipation
## 4759 4759 explosive fear
## 4760 4760 explosive negative
## 4761 4761 explosive surprise
## 4762 4762 expose anticipation
## 4763 4763 expose fear
## 4764 4764 exposed negative
## 4765 4765 expropriation negative
## 4766 4766 expulsion anger
## 4767 4767 expulsion disgust
## 4768 4768 expulsion fear
## 4769 4769 expulsion negative
## 4770 4770 expulsion sadness
## 4771 4771 exquisite joy
## 4772 4772 exquisite positive
## 4773 4773 exquisitely positive
## 4774 4774 extend positive
## 4775 4775 extensive positive
## 4776 4776 exterminate fear
## 4777 4777 exterminate negative
## 4778 4778 exterminate sadness
## 4779 4779 extermination anger
## 4780 4780 extermination fear
## 4781 4781 extermination negative
## 4782 4782 extinct negative
## 4783 4783 extinct sadness
## 4784 4784 extinguish anger
## 4785 4785 extinguish negative
## 4786 4786 extra positive
## 4787 4787 extrajudicial fear
## 4788 4788 extrajudicial negative
## 4789 4789 extraordinary positive
## 4790 4790 extremity negative
## 4791 4791 extricate anticipation
## 4792 4792 extricate positive
## 4793 4793 exuberance joy
## 4794 4794 exuberance positive
## 4795 4795 eyewitness trust
## 4796 4796 fabricate negative
## 4797 4797 fabrication negative
## 4798 4798 fabrication trust
## 4799 4799 facilitate positive
## 4800 4800 fact trust
## 4801 4801 facts positive
## 4802 4802 facts trust
## 4803 4803 faculty positive
## 4804 4804 faculty trust
## 4805 4805 fade negative
## 4806 4806 faeces disgust
## 4807 4807 faeces negative
## 4808 4808 failing anger
## 4809 4809 failing anticipation
## 4810 4810 failing fear
## 4811 4811 failing negative
## 4812 4812 failing sadness
## 4813 4813 failure disgust
## 4814 4814 failure fear
## 4815 4815 failure negative
## 4816 4816 failure sadness
## 4817 4817 fain anticipation
## 4818 4818 fain joy
## 4819 4819 fain positive
## 4820 4820 fain trust
## 4821 4821 fainting fear
## 4822 4822 fainting negative
## 4823 4823 fainting sadness
## 4824 4824 fainting surprise
## 4825 4825 fair positive
## 4826 4826 fairly positive
## 4827 4827 fairly trust
## 4828 4828 faith anticipation
## 4829 4829 faith joy
## 4830 4830 faith positive
## 4831 4831 faith trust
## 4832 4832 faithful positive
## 4833 4833 faithful trust
## 4834 4834 faithless negative
## 4835 4835 faithless sadness
## 4836 4836 fake negative
## 4837 4837 fall negative
## 4838 4838 fall sadness
## 4839 4839 fallacious anger
## 4840 4840 fallacious negative
## 4841 4841 fallacy negative
## 4842 4842 fallible negative
## 4843 4843 falling negative
## 4844 4844 falling sadness
## 4845 4845 fallow negative
## 4846 4846 falsehood anger
## 4847 4847 falsehood negative
## 4848 4848 falsehood trust
## 4849 4849 falsely negative
## 4850 4850 falsification anger
## 4851 4851 falsification negative
## 4852 4852 falsify disgust
## 4853 4853 falsify negative
## 4854 4854 falsity disgust
## 4855 4855 falsity negative
## 4856 4856 falter fear
## 4857 4857 falter negative
## 4858 4858 fame positive
## 4859 4859 familiar positive
## 4860 4860 familiar trust
## 4861 4861 familiarity anticipation
## 4862 4862 familiarity joy
## 4863 4863 familiarity positive
## 4864 4864 familiarity trust
## 4865 4865 famine negative
## 4866 4866 famine sadness
## 4867 4867 famous positive
## 4868 4868 famously positive
## 4869 4869 fanatic negative
## 4870 4870 fanaticism fear
## 4871 4871 fancy anticipation
## 4872 4872 fancy joy
## 4873 4873 fancy positive
## 4874 4874 fanfare anticipation
## 4875 4875 fanfare joy
## 4876 4876 fanfare positive
## 4877 4877 fanfare surprise
## 4878 4878 fang fear
## 4879 4879 fangs fear
## 4880 4880 farce negative
## 4881 4881 farcical disgust
## 4882 4882 farcical negative
## 4883 4883 farm anticipation
## 4884 4884 fascinating positive
## 4885 4885 fascination positive
## 4886 4886 fashionable positive
## 4887 4887 fasting negative
## 4888 4888 fasting sadness
## 4889 4889 fat disgust
## 4890 4890 fat negative
## 4891 4891 fat sadness
## 4892 4892 fatal anger
## 4893 4893 fatal fear
## 4894 4894 fatal negative
## 4895 4895 fatal sadness
## 4896 4896 fatality fear
## 4897 4897 fatality negative
## 4898 4898 fatality sadness
## 4899 4899 fate anticipation
## 4900 4900 fate negative
## 4901 4901 father trust
## 4902 4902 fatigue negative
## 4903 4903 fatigued negative
## 4904 4904 fatigued sadness
## 4905 4905 fatty disgust
## 4906 4906 fatty negative
## 4907 4907 fatty sadness
## 4908 4908 fault negative
## 4909 4909 fault sadness
## 4910 4910 faultless positive
## 4911 4911 faultless trust
## 4912 4912 faulty negative
## 4913 4913 favorable anticipation
## 4914 4914 favorable joy
## 4915 4915 favorable positive
## 4916 4916 favorable surprise
## 4917 4917 favorable trust
## 4918 4918 favorite joy
## 4919 4919 favorite positive
## 4920 4920 favorite trust
## 4921 4921 favoritism positive
## 4922 4922 fawn negative
## 4923 4923 fear anger
## 4924 4924 fear fear
## 4925 4925 fear negative
## 4926 4926 fearful fear
## 4927 4927 fearful negative
## 4928 4928 fearful sadness
## 4929 4929 fearfully fear
## 4930 4930 fearfully negative
## 4931 4931 fearfully sadness
## 4932 4932 fearfully surprise
## 4933 4933 fearing fear
## 4934 4934 fearing negative
## 4935 4935 fearless fear
## 4936 4936 fearless positive
## 4937 4937 feat anticipation
## 4938 4938 feat joy
## 4939 4939 feat positive
## 4940 4940 feat surprise
## 4941 4941 feature positive
## 4942 4942 febrile negative
## 4943 4943 fecal disgust
## 4944 4944 fecal negative
## 4945 4945 feces disgust
## 4946 4946 feces negative
## 4947 4947 fee anger
## 4948 4948 fee negative
## 4949 4949 feeble negative
## 4950 4950 feeble sadness
## 4951 4951 feeling anger
## 4952 4952 feeling anticipation
## 4953 4953 feeling disgust
## 4954 4954 feeling fear
## 4955 4955 feeling joy
## 4956 4956 feeling negative
## 4957 4957 feeling positive
## 4958 4958 feeling sadness
## 4959 4959 feeling surprise
## 4960 4960 feeling trust
## 4961 4961 feigned negative
## 4962 4962 felicity joy
## 4963 4963 felicity positive
## 4964 4964 fell negative
## 4965 4965 fell sadness
## 4966 4966 fellow positive
## 4967 4967 fellow trust
## 4968 4968 fellowship positive
## 4969 4969 felon fear
## 4970 4970 felon negative
## 4971 4971 felony negative
## 4972 4972 fenced anger
## 4973 4973 fender trust
## 4974 4974 ferment anticipation
## 4975 4975 ferment negative
## 4976 4976 fermentation anticipation
## 4977 4977 ferocious anger
## 4978 4978 ferocious disgust
## 4979 4979 ferocious fear
## 4980 4980 ferocious negative
## 4981 4981 ferocity anger
## 4982 4982 ferocity negative
## 4983 4983 fertile positive
## 4984 4984 fervor anger
## 4985 4985 fervor joy
## 4986 4986 fervor positive
## 4987 4987 festival anticipation
## 4988 4988 festival joy
## 4989 4989 festival positive
## 4990 4990 festival surprise
## 4991 4991 festive joy
## 4992 4992 festive positive
## 4993 4993 fete anticipation
## 4994 4994 fete joy
## 4995 4995 fete positive
## 4996 4996 fete surprise
## 4997 4997 feud anger
## 4998 4998 feud negative
## 4999 4999 feudal negative
## 5000 5000 feudalism anger
## 5001 5001 feudalism negative
## 5002 5002 feudalism sadness
## 5003 5003 fever fear
## 5004 5004 feverish negative
## 5005 5005 fiasco negative
## 5006 5006 fib anger
## 5007 5007 fib negative
## 5008 5008 fickle negative
## 5009 5009 fictitious negative
## 5010 5010 fidelity joy
## 5011 5011 fidelity positive
## 5012 5012 fidelity trust
## 5013 5013 fiend anger
## 5014 5014 fiend disgust
## 5015 5015 fiend fear
## 5016 5016 fiend negative
## 5017 5017 fierce anger
## 5018 5018 fierce disgust
## 5019 5019 fierce fear
## 5020 5020 fierce negative
## 5021 5021 fiesta anticipation
## 5022 5022 fiesta joy
## 5023 5023 fiesta positive
## 5024 5024 fiesta surprise
## 5025 5025 fiesta trust
## 5026 5026 fight anger
## 5027 5027 fight fear
## 5028 5028 fight negative
## 5029 5029 fighting anger
## 5030 5030 fighting negative
## 5031 5031 filibuster negative
## 5032 5032 fill trust
## 5033 5033 filth disgust
## 5034 5034 filth negative
## 5035 5035 filthy disgust
## 5036 5036 filthy negative
## 5037 5037 finally anticipation
## 5038 5038 finally disgust
## 5039 5039 finally joy
## 5040 5040 finally positive
## 5041 5041 finally surprise
## 5042 5042 finally trust
## 5043 5043 finery positive
## 5044 5044 finesse positive
## 5045 5045 fire fear
## 5046 5046 firearms anger
## 5047 5047 firearms fear
## 5048 5048 firearms negative
## 5049 5049 fireball positive
## 5050 5050 fireman trust
## 5051 5051 fireproof positive
## 5052 5052 firmness positive
## 5053 5053 firmness trust
## 5054 5054 firstborn anticipation
## 5055 5055 firstborn joy
## 5056 5056 firstborn positive
## 5057 5057 firstborn trust
## 5058 5058 fits anger
## 5059 5059 fits negative
## 5060 5060 fitting anticipation
## 5061 5061 fitting joy
## 5062 5062 fitting positive
## 5063 5063 fitting trust
## 5064 5064 fixed trust
## 5065 5065 fixture positive
## 5066 5066 flabby disgust
## 5067 5067 flabby negative
## 5068 5068 flaccid negative
## 5069 5069 flaccid sadness
## 5070 5070 flagging negative
## 5071 5071 flagrant anger
## 5072 5072 flagrant disgust
## 5073 5073 flagrant negative
## 5074 5074 flagship trust
## 5075 5075 flake negative
## 5076 5076 flange trust
## 5077 5077 flap negative
## 5078 5078 flattering joy
## 5079 5079 flattering positive
## 5080 5080 flatulence disgust
## 5081 5081 flatulence negative
## 5082 5082 flaunt negative
## 5083 5083 flaw negative
## 5084 5084 flaw sadness
## 5085 5085 flea disgust
## 5086 5086 flea negative
## 5087 5087 fled fear
## 5088 5088 flee fear
## 5089 5089 flee negative
## 5090 5090 fleece anger
## 5091 5091 fleece disgust
## 5092 5092 fleece negative
## 5093 5093 fleece sadness
## 5094 5094 fleet positive
## 5095 5095 flesh disgust
## 5096 5096 fleshy negative
## 5097 5097 flexibility positive
## 5098 5098 flinch anticipation
## 5099 5099 flinch disgust
## 5100 5100 flinch fear
## 5101 5101 flinch negative
## 5102 5102 flinch sadness
## 5103 5103 flinch surprise
## 5104 5104 flirt anticipation
## 5105 5105 flirt joy
## 5106 5106 flirt negative
## 5107 5107 flirt positive
## 5108 5108 flirt surprise
## 5109 5109 flirt trust
## 5110 5110 flog anger
## 5111 5111 flog disgust
## 5112 5112 flog fear
## 5113 5113 flog negative
## 5114 5114 flog sadness
## 5115 5115 flood fear
## 5116 5116 flop disgust
## 5117 5117 flop negative
## 5118 5118 flop sadness
## 5119 5119 floral positive
## 5120 5120 flounder fear
## 5121 5121 flounder negative
## 5122 5122 flounder sadness
## 5123 5123 flow positive
## 5124 5124 flowery positive
## 5125 5125 flu fear
## 5126 5126 flu negative
## 5127 5127 fluctuation anger
## 5128 5128 fluctuation fear
## 5129 5129 fluctuation negative
## 5130 5130 fluke surprise
## 5131 5131 flush positive
## 5132 5132 flying fear
## 5133 5133 flying positive
## 5134 5134 focus positive
## 5135 5135 foe anger
## 5136 5136 foe fear
## 5137 5137 foe negative
## 5138 5138 foiled negative
## 5139 5139 follower trust
## 5140 5140 folly negative
## 5141 5141 fondness joy
## 5142 5142 fondness positive
## 5143 5143 food joy
## 5144 5144 food positive
## 5145 5145 food trust
## 5146 5146 fool disgust
## 5147 5147 fool negative
## 5148 5148 foolish negative
## 5149 5149 foolishness negative
## 5150 5150 football anticipation
## 5151 5151 football joy
## 5152 5152 football positive
## 5153 5153 footing trust
## 5154 5154 forage negative
## 5155 5155 foray anger
## 5156 5156 foray negative
## 5157 5157 forbearance positive
## 5158 5158 forbid negative
## 5159 5159 forbid sadness
## 5160 5160 forbidding anger
## 5161 5161 forbidding fear
## 5162 5162 forbidding negative
## 5163 5163 force anger
## 5164 5164 force fear
## 5165 5165 force negative
## 5166 5166 forced fear
## 5167 5167 forced negative
## 5168 5168 forcibly anger
## 5169 5169 forcibly fear
## 5170 5170 forcibly negative
## 5171 5171 fore positive
## 5172 5172 forearm anger
## 5173 5173 forearm anticipation
## 5174 5174 foreboding anticipation
## 5175 5175 foreboding fear
## 5176 5176 foreboding negative
## 5177 5177 forecast anticipation
## 5178 5178 forecast trust
## 5179 5179 foreclose fear
## 5180 5180 foreclose negative
## 5181 5181 foreclose sadness
## 5182 5182 forefathers joy
## 5183 5183 forefathers positive
## 5184 5184 forefathers trust
## 5185 5185 forego negative
## 5186 5186 foregoing positive
## 5187 5187 foreign negative
## 5188 5188 foreigner fear
## 5189 5189 foreigner negative
## 5190 5190 foreman positive
## 5191 5191 forerunner positive
## 5192 5192 foresee anticipation
## 5193 5193 foresee positive
## 5194 5194 foresee surprise
## 5195 5195 foresee trust
## 5196 5196 foreseen anticipation
## 5197 5197 foreseen positive
## 5198 5198 foresight anticipation
## 5199 5199 foresight positive
## 5200 5200 foresight trust
## 5201 5201 forewarned anticipation
## 5202 5202 forewarned fear
## 5203 5203 forewarned negative
## 5204 5204 forfeit anger
## 5205 5205 forfeit negative
## 5206 5206 forfeit sadness
## 5207 5207 forfeited negative
## 5208 5208 forfeiture fear
## 5209 5209 forfeiture negative
## 5210 5210 forfeiture sadness
## 5211 5211 forge positive
## 5212 5212 forgery negative
## 5213 5213 forget negative
## 5214 5214 forgive positive
## 5215 5215 forgiven positive
## 5216 5216 forgiving positive
## 5217 5217 forgiving trust
## 5218 5218 forgotten fear
## 5219 5219 forgotten negative
## 5220 5220 forgotten sadness
## 5221 5221 forlorn negative
## 5222 5222 forlorn sadness
## 5223 5223 formality trust
## 5224 5224 formative positive
## 5225 5225 formative trust
## 5226 5226 formidable fear
## 5227 5227 forming anticipation
## 5228 5228 formless negative
## 5229 5229 formula positive
## 5230 5230 formula trust
## 5231 5231 fornication negative
## 5232 5232 forsake negative
## 5233 5233 forsake sadness
## 5234 5234 forsaken anger
## 5235 5235 forsaken negative
## 5236 5236 forsaken sadness
## 5237 5237 fort trust
## 5238 5238 forte positive
## 5239 5239 forthcoming positive
## 5240 5240 fortify positive
## 5241 5241 fortitude joy
## 5242 5242 fortitude positive
## 5243 5243 fortitude trust
## 5244 5244 fortress fear
## 5245 5245 fortress positive
## 5246 5246 fortress sadness
## 5247 5247 fortress trust
## 5248 5248 fortunate positive
## 5249 5249 fortune anticipation
## 5250 5250 fortune joy
## 5251 5251 fortune positive
## 5252 5252 fortune surprise
## 5253 5253 fortune trust
## 5254 5254 forward positive
## 5255 5255 foul anger
## 5256 5256 foul disgust
## 5257 5257 foul fear
## 5258 5258 foul negative
## 5259 5259 found joy
## 5260 5260 found positive
## 5261 5261 found trust
## 5262 5262 foundation positive
## 5263 5263 fracture negative
## 5264 5264 fragile fear
## 5265 5265 fragile negative
## 5266 5266 fragile sadness
## 5267 5267 fragrant positive
## 5268 5268 frailty negative
## 5269 5269 frailty sadness
## 5270 5270 framework trust
## 5271 5271 frank positive
## 5272 5272 frank trust
## 5273 5273 frankness positive
## 5274 5274 frankness trust
## 5275 5275 frantic anticipation
## 5276 5276 frantic disgust
## 5277 5277 frantic fear
## 5278 5278 frantic negative
## 5279 5279 frantic surprise
## 5280 5280 fraternal joy
## 5281 5281 fraternal positive
## 5282 5282 fraternal trust
## 5283 5283 fraud anger
## 5284 5284 fraud negative
## 5285 5285 fraudulent anger
## 5286 5286 fraudulent disgust
## 5287 5287 fraudulent negative
## 5288 5288 fraught fear
## 5289 5289 fraught negative
## 5290 5290 fraught sadness
## 5291 5291 fray negative
## 5292 5292 frayed negative
## 5293 5293 frayed sadness
## 5294 5294 freakish disgust
## 5295 5295 freakish fear
## 5296 5296 freakish negative
## 5297 5297 freakish surprise
## 5298 5298 freedom joy
## 5299 5299 freedom positive
## 5300 5300 freedom trust
## 5301 5301 freely joy
## 5302 5302 freely positive
## 5303 5303 freely trust
## 5304 5304 freezing negative
## 5305 5305 frenetic anger
## 5306 5306 frenetic fear
## 5307 5307 frenetic negative
## 5308 5308 frenetic surprise
## 5309 5309 frenzied anger
## 5310 5310 frenzied fear
## 5311 5311 frenzied negative
## 5312 5312 frenzy negative
## 5313 5313 fret fear
## 5314 5314 fret negative
## 5315 5315 friction anger
## 5316 5316 friend joy
## 5317 5317 friend positive
## 5318 5318 friend trust
## 5319 5319 friendliness joy
## 5320 5320 friendliness positive
## 5321 5321 friendliness trust
## 5322 5322 friendly anticipation
## 5323 5323 friendly joy
## 5324 5324 friendly positive
## 5325 5325 friendly trust
## 5326 5326 friendship joy
## 5327 5327 friendship positive
## 5328 5328 friendship trust
## 5329 5329 frigate fear
## 5330 5330 fright fear
## 5331 5331 fright negative
## 5332 5332 fright surprise
## 5333 5333 frighten fear
## 5334 5334 frighten negative
## 5335 5335 frighten sadness
## 5336 5336 frighten surprise
## 5337 5337 frightened fear
## 5338 5338 frightened negative
## 5339 5339 frightened surprise
## 5340 5340 frightful anger
## 5341 5341 frightful fear
## 5342 5342 frightful negative
## 5343 5343 frightful sadness
## 5344 5344 frigid negative
## 5345 5345 frisky anticipation
## 5346 5346 frisky joy
## 5347 5347 frisky positive
## 5348 5348 frisky surprise
## 5349 5349 frivolous negative
## 5350 5350 frolic joy
## 5351 5351 frolic positive
## 5352 5352 frostbite negative
## 5353 5353 froth negative
## 5354 5354 frown negative
## 5355 5355 frown sadness
## 5356 5356 frowning anger
## 5357 5357 frowning disgust
## 5358 5358 frowning negative
## 5359 5359 frowning sadness
## 5360 5360 frugal positive
## 5361 5361 fruitful positive
## 5362 5362 fruitless negative
## 5363 5363 fruitless sadness
## 5364 5364 frustrate anger
## 5365 5365 frustrate disgust
## 5366 5366 frustrate negative
## 5367 5367 frustrate sadness
## 5368 5368 frustrated anger
## 5369 5369 frustrated negative
## 5370 5370 frustration anger
## 5371 5371 frustration negative
## 5372 5372 fudge negative
## 5373 5373 fugitive anger
## 5374 5374 fugitive disgust
## 5375 5375 fugitive fear
## 5376 5376 fugitive negative
## 5377 5377 fugitive sadness
## 5378 5378 fugitive trust
## 5379 5379 fulfill joy
## 5380 5380 fulfill positive
## 5381 5381 fulfillment anticipation
## 5382 5382 fulfillment joy
## 5383 5383 fulfillment positive
## 5384 5384 fulfillment trust
## 5385 5385 full positive
## 5386 5386 fully positive
## 5387 5387 fully trust
## 5388 5388 fumble negative
## 5389 5389 fume negative
## 5390 5390 fuming anger
## 5391 5391 fuming negative
## 5392 5392 fun anticipation
## 5393 5393 fun joy
## 5394 5394 fun positive
## 5395 5395 fundamental positive
## 5396 5396 fundamental trust
## 5397 5397 funeral sadness
## 5398 5398 fungus disgust
## 5399 5399 fungus negative
## 5400 5400 funk negative
## 5401 5401 funk sadness
## 5402 5402 furious anger
## 5403 5403 furious disgust
## 5404 5404 furious negative
## 5405 5405 furiously anger
## 5406 5406 furnace anger
## 5407 5407 furor anger
## 5408 5408 furor negative
## 5409 5409 furrow sadness
## 5410 5410 fury anger
## 5411 5411 fury fear
## 5412 5412 fury negative
## 5413 5413 fury sadness
## 5414 5414 fuse positive
## 5415 5415 fuse trust
## 5416 5416 fusion positive
## 5417 5417 fuss anger
## 5418 5418 fuss negative
## 5419 5419 fuss sadness
## 5420 5420 fussy disgust
## 5421 5421 fussy negative
## 5422 5422 futile negative
## 5423 5423 futile sadness
## 5424 5424 futility negative
## 5425 5425 gaby disgust
## 5426 5426 gaby negative
## 5427 5427 gag disgust
## 5428 5428 gag negative
## 5429 5429 gage trust
## 5430 5430 gain anticipation
## 5431 5431 gain joy
## 5432 5432 gain positive
## 5433 5433 gaining positive
## 5434 5434 gall anger
## 5435 5435 gall disgust
## 5436 5436 gall negative
## 5437 5437 gallant positive
## 5438 5438 gallantry positive
## 5439 5439 gallows anger
## 5440 5440 gallows fear
## 5441 5441 gallows negative
## 5442 5442 gallows sadness
## 5443 5443 galore positive
## 5444 5444 gamble negative
## 5445 5445 gambler anticipation
## 5446 5446 gambler negative
## 5447 5447 gambler surprise
## 5448 5448 gambling anticipation
## 5449 5449 gambling negative
## 5450 5450 gambling surprise
## 5451 5451 gang anger
## 5452 5452 gang fear
## 5453 5453 gang negative
## 5454 5454 gaol negative
## 5455 5455 gap negative
## 5456 5456 gape surprise
## 5457 5457 garbage disgust
## 5458 5458 garbage negative
## 5459 5459 garden joy
## 5460 5460 garden positive
## 5461 5461 garish disgust
## 5462 5462 garish negative
## 5463 5463 garish surprise
## 5464 5464 garnet positive
## 5465 5465 garnish negative
## 5466 5466 garrison positive
## 5467 5467 garrison trust
## 5468 5468 gash fear
## 5469 5469 gash negative
## 5470 5470 gasp surprise
## 5471 5471 gasping fear
## 5472 5472 gasping negative
## 5473 5473 gate trust
## 5474 5474 gateway trust
## 5475 5475 gauche negative
## 5476 5476 gauging trust
## 5477 5477 gaunt negative
## 5478 5478 gawk surprise
## 5479 5479 gazette positive
## 5480 5480 gazette trust
## 5481 5481 gear positive
## 5482 5482 gelatin disgust
## 5483 5483 gem joy
## 5484 5484 gem positive
## 5485 5485 general positive
## 5486 5486 general trust
## 5487 5487 generosity anticipation
## 5488 5488 generosity joy
## 5489 5489 generosity positive
## 5490 5490 generosity surprise
## 5491 5491 generosity trust
## 5492 5492 generous joy
## 5493 5493 generous positive
## 5494 5494 generous trust
## 5495 5495 genial joy
## 5496 5496 genial positive
## 5497 5497 genius positive
## 5498 5498 gent anger
## 5499 5499 gent disgust
## 5500 5500 gent fear
## 5501 5501 gent negative
## 5502 5502 genteel positive
## 5503 5503 gentleman positive
## 5504 5504 gentleman trust
## 5505 5505 gentleness positive
## 5506 5506 gentry positive
## 5507 5507 gentry trust
## 5508 5508 genuine positive
## 5509 5509 genuine trust
## 5510 5510 geranium positive
## 5511 5511 geriatric negative
## 5512 5512 geriatric sadness
## 5513 5513 germ anticipation
## 5514 5514 germ disgust
## 5515 5515 germination anticipation
## 5516 5516 ghastly disgust
## 5517 5517 ghastly fear
## 5518 5518 ghastly negative
## 5519 5519 ghetto disgust
## 5520 5520 ghetto fear
## 5521 5521 ghetto negative
## 5522 5522 ghetto sadness
## 5523 5523 ghost fear
## 5524 5524 ghostly fear
## 5525 5525 ghostly negative
## 5526 5526 giant fear
## 5527 5527 gibberish anger
## 5528 5528 gibberish negative
## 5529 5529 gift anticipation
## 5530 5530 gift joy
## 5531 5531 gift positive
## 5532 5532 gift surprise
## 5533 5533 gifted positive
## 5534 5534 gigantic positive
## 5535 5535 giggle joy
## 5536 5536 giggle positive
## 5537 5537 girder positive
## 5538 5538 girder trust
## 5539 5539 giving positive
## 5540 5540 glacial negative
## 5541 5541 glad anticipation
## 5542 5542 glad joy
## 5543 5543 glad positive
## 5544 5544 gladiator fear
## 5545 5545 gladness joy
## 5546 5546 gladness positive
## 5547 5547 glare anger
## 5548 5548 glare fear
## 5549 5549 glare negative
## 5550 5550 glaring anger
## 5551 5551 glaring negative
## 5552 5552 glee joy
## 5553 5553 glee positive
## 5554 5554 glib negative
## 5555 5555 glide anticipation
## 5556 5556 glide joy
## 5557 5557 glide positive
## 5558 5558 glimmer anticipation
## 5559 5559 glimmer joy
## 5560 5560 glimmer positive
## 5561 5561 glimmer surprise
## 5562 5562 glitter disgust
## 5563 5563 glitter joy
## 5564 5564 glittering positive
## 5565 5565 gloom negative
## 5566 5566 gloom sadness
## 5567 5567 gloomy negative
## 5568 5568 gloomy sadness
## 5569 5569 glorification joy
## 5570 5570 glorification positive
## 5571 5571 glorify anticipation
## 5572 5572 glorify joy
## 5573 5573 glorify positive
## 5574 5574 glorify surprise
## 5575 5575 glorify trust
## 5576 5576 glory anticipation
## 5577 5577 glory joy
## 5578 5578 glory positive
## 5579 5579 glory trust
## 5580 5580 gloss positive
## 5581 5581 glow anticipation
## 5582 5582 glow joy
## 5583 5583 glow positive
## 5584 5584 glow trust
## 5585 5585 glowing positive
## 5586 5586 glum negative
## 5587 5587 glum sadness
## 5588 5588 glut disgust
## 5589 5589 glut negative
## 5590 5590 gluttony disgust
## 5591 5591 gluttony negative
## 5592 5592 gnome anger
## 5593 5593 gnome disgust
## 5594 5594 gnome fear
## 5595 5595 gnome negative
## 5596 5596 gob disgust
## 5597 5597 goblin disgust
## 5598 5598 goblin fear
## 5599 5599 goblin negative
## 5600 5600 god anticipation
## 5601 5601 god fear
## 5602 5602 god joy
## 5603 5603 god positive
## 5604 5604 god trust
## 5605 5605 godless anger
## 5606 5606 godless negative
## 5607 5607 godly joy
## 5608 5608 godly positive
## 5609 5609 godly trust
## 5610 5610 godsend joy
## 5611 5611 godsend positive
## 5612 5612 godsend surprise
## 5613 5613 gold positive
## 5614 5614 gonorrhea anger
## 5615 5615 gonorrhea disgust
## 5616 5616 gonorrhea fear
## 5617 5617 gonorrhea negative
## 5618 5618 gonorrhea sadness
## 5619 5619 goo disgust
## 5620 5620 goo negative
## 5621 5621 good anticipation
## 5622 5622 good joy
## 5623 5623 good positive
## 5624 5624 good surprise
## 5625 5625 good trust
## 5626 5626 goodly positive
## 5627 5627 goodness anticipation
## 5628 5628 goodness joy
## 5629 5629 goodness positive
## 5630 5630 goodness surprise
## 5631 5631 goodness trust
## 5632 5632 goods positive
## 5633 5633 goodwill positive
## 5634 5634 gore anger
## 5635 5635 gore disgust
## 5636 5636 gore fear
## 5637 5637 gore negative
## 5638 5638 gore sadness
## 5639 5639 gorge disgust
## 5640 5640 gorge negative
## 5641 5641 gorgeous joy
## 5642 5642 gorgeous positive
## 5643 5643 gorilla negative
## 5644 5644 gory anger
## 5645 5645 gory disgust
## 5646 5646 gory fear
## 5647 5647 gory negative
## 5648 5648 gory sadness
## 5649 5649 gospel positive
## 5650 5650 gospel trust
## 5651 5651 gossip negative
## 5652 5652 gouge negative
## 5653 5653 gout negative
## 5654 5654 govern positive
## 5655 5655 govern trust
## 5656 5656 governess trust
## 5657 5657 government fear
## 5658 5658 government negative
## 5659 5659 governor trust
## 5660 5660 grab anger
## 5661 5661 grab negative
## 5662 5662 grace positive
## 5663 5663 graceful positive
## 5664 5664 gracious positive
## 5665 5665 graciously positive
## 5666 5666 gradual anticipation
## 5667 5667 graduation anticipation
## 5668 5668 graduation fear
## 5669 5669 graduation joy
## 5670 5670 graduation positive
## 5671 5671 graduation surprise
## 5672 5672 graduation trust
## 5673 5673 grammar trust
## 5674 5674 grandchildren anticipation
## 5675 5675 grandchildren joy
## 5676 5676 grandchildren positive
## 5677 5677 grandchildren trust
## 5678 5678 grandeur positive
## 5679 5679 grandfather trust
## 5680 5680 grandmother positive
## 5681 5681 grant anticipation
## 5682 5682 grant joy
## 5683 5683 grant positive
## 5684 5684 grant trust
## 5685 5685 granted positive
## 5686 5686 grasping anticipation
## 5687 5687 grasping negative
## 5688 5688 grate negative
## 5689 5689 grated anger
## 5690 5690 grated negative
## 5691 5691 grateful positive
## 5692 5692 gratify joy
## 5693 5693 gratify positive
## 5694 5694 gratify surprise
## 5695 5695 grating anger
## 5696 5696 grating disgust
## 5697 5697 grating negative
## 5698 5698 gratitude joy
## 5699 5699 gratitude positive
## 5700 5700 gratuitous disgust
## 5701 5701 grave fear
## 5702 5702 grave negative
## 5703 5703 grave sadness
## 5704 5704 gravitate anticipation
## 5705 5705 gray disgust
## 5706 5706 gray sadness
## 5707 5707 greasy disgust
## 5708 5708 greater positive
## 5709 5709 greatness joy
## 5710 5710 greatness positive
## 5711 5711 greatness surprise
## 5712 5712 greatness trust
## 5713 5713 greed anger
## 5714 5714 greed disgust
## 5715 5715 greed negative
## 5716 5716 greedy disgust
## 5717 5717 greedy negative
## 5718 5718 green joy
## 5719 5719 green positive
## 5720 5720 green trust
## 5721 5721 greeting positive
## 5722 5722 greeting surprise
## 5723 5723 gregarious positive
## 5724 5724 grenade fear
## 5725 5725 grenade negative
## 5726 5726 grief negative
## 5727 5727 grief sadness
## 5728 5728 grievance anger
## 5729 5729 grievance disgust
## 5730 5730 grievance negative
## 5731 5731 grievance sadness
## 5732 5732 grieve fear
## 5733 5733 grieve negative
## 5734 5734 grieve sadness
## 5735 5735 grievous anger
## 5736 5736 grievous fear
## 5737 5737 grievous negative
## 5738 5738 grievous sadness
## 5739 5739 grim anger
## 5740 5740 grim anticipation
## 5741 5741 grim disgust
## 5742 5742 grim fear
## 5743 5743 grim negative
## 5744 5744 grim sadness
## 5745 5745 grime disgust
## 5746 5746 grime negative
## 5747 5747 grimy disgust
## 5748 5748 grimy negative
## 5749 5749 grin anticipation
## 5750 5750 grin joy
## 5751 5751 grin positive
## 5752 5752 grin surprise
## 5753 5753 grin trust
## 5754 5754 grinding negative
## 5755 5755 grisly disgust
## 5756 5756 grisly fear
## 5757 5757 grisly negative
## 5758 5758 grist positive
## 5759 5759 grit positive
## 5760 5760 grit trust
## 5761 5761 grizzly fear
## 5762 5762 grizzly negative
## 5763 5763 groan disgust
## 5764 5764 groan negative
## 5765 5765 groan sadness
## 5766 5766 grope anger
## 5767 5767 grope disgust
## 5768 5768 grope fear
## 5769 5769 grope negative
## 5770 5770 gross disgust
## 5771 5771 gross negative
## 5772 5772 grotesque disgust
## 5773 5773 grotesque negative
## 5774 5774 ground trust
## 5775 5775 grounded fear
## 5776 5776 grounded negative
## 5777 5777 grounded sadness
## 5778 5778 groundless negative
## 5779 5779 groundwork positive
## 5780 5780 grow anticipation
## 5781 5781 grow joy
## 5782 5782 grow positive
## 5783 5783 grow trust
## 5784 5784 growl anger
## 5785 5785 growl fear
## 5786 5786 growl negative
## 5787 5787 growling anger
## 5788 5788 growling disgust
## 5789 5789 growling fear
## 5790 5790 growling negative
## 5791 5791 growth positive
## 5792 5792 grudge anger
## 5793 5793 grudge negative
## 5794 5794 grudgingly negative
## 5795 5795 gruesome disgust
## 5796 5796 gruesome negative
## 5797 5797 gruff anger
## 5798 5798 gruff disgust
## 5799 5799 gruff negative
## 5800 5800 grumble anger
## 5801 5801 grumble disgust
## 5802 5802 grumble negative
## 5803 5803 grumpy anger
## 5804 5804 grumpy disgust
## 5805 5805 grumpy negative
## 5806 5806 grumpy sadness
## 5807 5807 guarantee positive
## 5808 5808 guarantee trust
## 5809 5809 guard fear
## 5810 5810 guard positive
## 5811 5811 guard trust
## 5812 5812 guarded trust
## 5813 5813 guardian positive
## 5814 5814 guardian trust
## 5815 5815 guardianship positive
## 5816 5816 guardianship trust
## 5817 5817 gubernatorial positive
## 5818 5818 gubernatorial trust
## 5819 5819 guerilla fear
## 5820 5820 guerilla negative
## 5821 5821 guess surprise
## 5822 5822 guidance positive
## 5823 5823 guidance trust
## 5824 5824 guide positive
## 5825 5825 guide trust
## 5826 5826 guidebook positive
## 5827 5827 guidebook trust
## 5828 5828 guile negative
## 5829 5829 guillotine anger
## 5830 5830 guillotine anticipation
## 5831 5831 guillotine disgust
## 5832 5832 guillotine fear
## 5833 5833 guillotine negative
## 5834 5834 guillotine sadness
## 5835 5835 guilt disgust
## 5836 5836 guilt negative
## 5837 5837 guilt sadness
## 5838 5838 guilty anger
## 5839 5839 guilty negative
## 5840 5840 guilty sadness
## 5841 5841 guise negative
## 5842 5842 gull negative
## 5843 5843 gullible negative
## 5844 5844 gullible sadness
## 5845 5845 gullible trust
## 5846 5846 gulp fear
## 5847 5847 gulp surprise
## 5848 5848 gun anger
## 5849 5849 gun fear
## 5850 5850 gun negative
## 5851 5851 gunpowder fear
## 5852 5852 guru positive
## 5853 5853 guru trust
## 5854 5854 gush disgust
## 5855 5855 gush joy
## 5856 5856 gush negative
## 5857 5857 gusset trust
## 5858 5858 gut disgust
## 5859 5859 guts positive
## 5860 5860 gutter disgust
## 5861 5861 guzzling negative
## 5862 5862 gymnast positive
## 5863 5863 gypsy negative
## 5864 5864 habitat positive
## 5865 5865 habitual anticipation
## 5866 5866 hag disgust
## 5867 5867 hag fear
## 5868 5868 hag negative
## 5869 5869 haggard negative
## 5870 5870 haggard sadness
## 5871 5871 hail negative
## 5872 5872 hail positive
## 5873 5873 hail trust
## 5874 5874 hairy disgust
## 5875 5875 hairy negative
## 5876 5876 hale positive
## 5877 5877 halfway negative
## 5878 5878 hallucination fear
## 5879 5879 hallucination negative
## 5880 5880 halter anger
## 5881 5881 halter fear
## 5882 5882 halter negative
## 5883 5883 halter sadness
## 5884 5884 halting fear
## 5885 5885 halting negative
## 5886 5886 halting sadness
## 5887 5887 hamper negative
## 5888 5888 hamstring anger
## 5889 5889 hamstring negative
## 5890 5890 hamstring sadness
## 5891 5891 handbook trust
## 5892 5892 handicap negative
## 5893 5893 handicap sadness
## 5894 5894 handy positive
## 5895 5895 hanging anger
## 5896 5896 hanging disgust
## 5897 5897 hanging fear
## 5898 5898 hanging negative
## 5899 5899 hanging sadness
## 5900 5900 hangman fear
## 5901 5901 hangman negative
## 5902 5902 hankering anticipation
## 5903 5903 hap anticipation
## 5904 5904 hap surprise
## 5905 5905 happen anticipation
## 5906 5906 happily joy
## 5907 5907 happily positive
## 5908 5908 happiness anticipation
## 5909 5909 happiness joy
## 5910 5910 happiness positive
## 5911 5911 happy anticipation
## 5912 5912 happy joy
## 5913 5913 happy positive
## 5914 5914 happy trust
## 5915 5915 harass anger
## 5916 5916 harass disgust
## 5917 5917 harass negative
## 5918 5918 harassing anger
## 5919 5919 harbinger anger
## 5920 5920 harbinger anticipation
## 5921 5921 harbinger fear
## 5922 5922 harbinger negative
## 5923 5923 harbor trust
## 5924 5924 hardened anger
## 5925 5925 hardened disgust
## 5926 5926 hardened fear
## 5927 5927 hardened negative
## 5928 5928 hardness negative
## 5929 5929 hardship negative
## 5930 5930 hardship sadness
## 5931 5931 hardy joy
## 5932 5932 hardy positive
## 5933 5933 hardy trust
## 5934 5934 harlot disgust
## 5935 5935 harlot negative
## 5936 5936 harm fear
## 5937 5937 harm negative
## 5938 5938 harmful anger
## 5939 5939 harmful disgust
## 5940 5940 harmful fear
## 5941 5941 harmful negative
## 5942 5942 harmful sadness
## 5943 5943 harmoniously joy
## 5944 5944 harmoniously positive
## 5945 5945 harmoniously trust
## 5946 5946 harmony joy
## 5947 5947 harmony positive
## 5948 5948 harmony trust
## 5949 5949 harrowing fear
## 5950 5950 harrowing negative
## 5951 5951 harry anger
## 5952 5952 harry negative
## 5953 5953 harry sadness
## 5954 5954 harshness anger
## 5955 5955 harshness fear
## 5956 5956 harshness negative
## 5957 5957 harvest anticipation
## 5958 5958 harvest joy
## 5959 5959 harvest positive
## 5960 5960 hash negative
## 5961 5961 hashish negative
## 5962 5962 haste anticipation
## 5963 5963 hasty negative
## 5964 5964 hate anger
## 5965 5965 hate disgust
## 5966 5966 hate fear
## 5967 5967 hate negative
## 5968 5968 hate sadness
## 5969 5969 hateful anger
## 5970 5970 hateful disgust
## 5971 5971 hateful fear
## 5972 5972 hateful negative
## 5973 5973 hateful sadness
## 5974 5974 hating anger
## 5975 5975 hating negative
## 5976 5976 hatred anger
## 5977 5977 hatred disgust
## 5978 5978 hatred fear
## 5979 5979 hatred negative
## 5980 5980 hatred sadness
## 5981 5981 haughty anger
## 5982 5982 haughty negative
## 5983 5983 haunt fear
## 5984 5984 haunt negative
## 5985 5985 haunted fear
## 5986 5986 haunted negative
## 5987 5987 haunted sadness
## 5988 5988 haven positive
## 5989 5989 haven trust
## 5990 5990 havoc anger
## 5991 5991 havoc fear
## 5992 5992 havoc negative
## 5993 5993 hawk fear
## 5994 5994 hazard fear
## 5995 5995 hazard negative
## 5996 5996 hazardous fear
## 5997 5997 hazardous negative
## 5998 5998 haze fear
## 5999 5999 haze negative
## 6000 6000 headache negative
## 6001 6001 headlight anticipation
## 6002 6002 headway positive
## 6003 6003 heady negative
## 6004 6004 heal joy
## 6005 6005 heal positive
## 6006 6006 heal trust
## 6007 6007 healing anticipation
## 6008 6008 healing joy
## 6009 6009 healing positive
## 6010 6010 healing trust
## 6011 6011 healthful joy
## 6012 6012 healthful positive
## 6013 6013 healthy positive
## 6014 6014 hearing fear
## 6015 6015 hearing negative
## 6016 6016 hearsay negative
## 6017 6017 hearse fear
## 6018 6018 hearse negative
## 6019 6019 hearse sadness
## 6020 6020 heartache negative
## 6021 6021 heartache sadness
## 6022 6022 heartburn negative
## 6023 6023 heartfelt joy
## 6024 6024 heartfelt positive
## 6025 6025 heartfelt sadness
## 6026 6026 heartfelt trust
## 6027 6027 hearth positive
## 6028 6028 heartily joy
## 6029 6029 heartily positive
## 6030 6030 heartless negative
## 6031 6031 heartless sadness
## 6032 6032 heartworm disgust
## 6033 6033 heathen fear
## 6034 6034 heathen negative
## 6035 6035 heavenly anticipation
## 6036 6036 heavenly joy
## 6037 6037 heavenly positive
## 6038 6038 heavenly trust
## 6039 6039 heavens joy
## 6040 6040 heavens positive
## 6041 6041 heavens trust
## 6042 6042 heavily negative
## 6043 6043 hedonism joy
## 6044 6044 hedonism negative
## 6045 6045 hedonism positive
## 6046 6046 heel negative
## 6047 6047 heft anticipation
## 6048 6048 heft fear
## 6049 6049 heft positive
## 6050 6050 heighten fear
## 6051 6051 heighten negative
## 6052 6052 heinous negative
## 6053 6053 hell anger
## 6054 6054 hell disgust
## 6055 6055 hell fear
## 6056 6056 hell negative
## 6057 6057 hell sadness
## 6058 6058 hellish anger
## 6059 6059 hellish disgust
## 6060 6060 hellish fear
## 6061 6061 hellish negative
## 6062 6062 hellish sadness
## 6063 6063 helmet fear
## 6064 6064 helmet positive
## 6065 6065 helper positive
## 6066 6066 helper trust
## 6067 6067 helpful joy
## 6068 6068 helpful positive
## 6069 6069 helpful trust
## 6070 6070 helpless fear
## 6071 6071 helpless negative
## 6072 6072 helpless sadness
## 6073 6073 helplessness fear
## 6074 6074 helplessness negative
## 6075 6075 helplessness sadness
## 6076 6076 hemorrhage disgust
## 6077 6077 hemorrhage fear
## 6078 6078 hemorrhage negative
## 6079 6079 hemorrhage sadness
## 6080 6080 hemorrhoids negative
## 6081 6081 herbal positive
## 6082 6082 heresy negative
## 6083 6083 heretic disgust
## 6084 6084 heretic negative
## 6085 6085 heritage trust
## 6086 6086 hermaphrodite negative
## 6087 6087 hermaphrodite surprise
## 6088 6088 hermit sadness
## 6089 6089 hermit trust
## 6090 6090 hero anticipation
## 6091 6091 hero joy
## 6092 6092 hero positive
## 6093 6093 hero surprise
## 6094 6094 hero trust
## 6095 6095 heroic joy
## 6096 6096 heroic positive
## 6097 6097 heroic surprise
## 6098 6098 heroic trust
## 6099 6099 heroics positive
## 6100 6100 heroin negative
## 6101 6101 heroine positive
## 6102 6102 heroine trust
## 6103 6103 heroism anticipation
## 6104 6104 heroism joy
## 6105 6105 heroism positive
## 6106 6106 heroism surprise
## 6107 6107 heroism trust
## 6108 6108 herpes disgust
## 6109 6109 herpes negative
## 6110 6110 herpesvirus disgust
## 6111 6111 herpesvirus negative
## 6112 6112 hesitation fear
## 6113 6113 hesitation negative
## 6114 6114 heyday anticipation
## 6115 6115 heyday joy
## 6116 6116 heyday positive
## 6117 6117 heyday trust
## 6118 6118 hidden negative
## 6119 6119 hide fear
## 6120 6120 hideous disgust
## 6121 6121 hideous fear
## 6122 6122 hideous negative
## 6123 6123 hideous sadness
## 6124 6124 hiding fear
## 6125 6125 highest anticipation
## 6126 6126 highest fear
## 6127 6127 highest joy
## 6128 6128 highest negative
## 6129 6129 highest positive
## 6130 6130 highest surprise
## 6131 6131 hilarious joy
## 6132 6132 hilarious positive
## 6133 6133 hilarious surprise
## 6134 6134 hilarity joy
## 6135 6135 hilarity positive
## 6136 6136 hindering negative
## 6137 6137 hindering sadness
## 6138 6138 hindrance negative
## 6139 6139 hippie negative
## 6140 6140 hire anticipation
## 6141 6141 hire joy
## 6142 6142 hire positive
## 6143 6143 hire trust
## 6144 6144 hiss anger
## 6145 6145 hiss fear
## 6146 6146 hiss negative
## 6147 6147 hissing negative
## 6148 6148 hit anger
## 6149 6149 hit negative
## 6150 6150 hitherto negative
## 6151 6151 hive negative
## 6152 6152 hoarse negative
## 6153 6153 hoary negative
## 6154 6154 hoary sadness
## 6155 6155 hoax anger
## 6156 6156 hoax disgust
## 6157 6157 hoax negative
## 6158 6158 hoax sadness
## 6159 6159 hoax surprise
## 6160 6160 hobby joy
## 6161 6161 hobby positive
## 6162 6162 hobo negative
## 6163 6163 hobo sadness
## 6164 6164 hog disgust
## 6165 6165 hog negative
## 6166 6166 holiday anticipation
## 6167 6167 holiday joy
## 6168 6168 holiday positive
## 6169 6169 holiness anticipation
## 6170 6170 holiness fear
## 6171 6171 holiness joy
## 6172 6172 holiness positive
## 6173 6173 holiness surprise
## 6174 6174 holiness trust
## 6175 6175 hollow negative
## 6176 6176 hollow sadness
## 6177 6177 holocaust anger
## 6178 6178 holocaust disgust
## 6179 6179 holocaust fear
## 6180 6180 holocaust negative
## 6181 6181 holocaust sadness
## 6182 6182 holy positive
## 6183 6183 homage positive
## 6184 6184 homeless anger
## 6185 6185 homeless anticipation
## 6186 6186 homeless disgust
## 6187 6187 homeless fear
## 6188 6188 homeless negative
## 6189 6189 homeless sadness
## 6190 6190 homesick negative
## 6191 6191 homesick sadness
## 6192 6192 homework fear
## 6193 6193 homicidal anger
## 6194 6194 homicidal fear
## 6195 6195 homicidal negative
## 6196 6196 homicide anger
## 6197 6197 homicide disgust
## 6198 6198 homicide fear
## 6199 6199 homicide negative
## 6200 6200 homicide sadness
## 6201 6201 homology positive
## 6202 6202 honest anger
## 6203 6203 honest disgust
## 6204 6204 honest fear
## 6205 6205 honest joy
## 6206 6206 honest positive
## 6207 6207 honest sadness
## 6208 6208 honest trust
## 6209 6209 honesty positive
## 6210 6210 honesty trust
## 6211 6211 honey positive
## 6212 6212 honeymoon anticipation
## 6213 6213 honeymoon joy
## 6214 6214 honeymoon positive
## 6215 6215 honeymoon surprise
## 6216 6216 honeymoon trust
## 6217 6217 honor positive
## 6218 6218 honor trust
## 6219 6219 honorable positive
## 6220 6220 honorable trust
## 6221 6221 hood anger
## 6222 6222 hood disgust
## 6223 6223 hood fear
## 6224 6224 hood negative
## 6225 6225 hooded fear
## 6226 6226 hooked negative
## 6227 6227 hoot anger
## 6228 6228 hoot disgust
## 6229 6229 hoot negative
## 6230 6230 hope anticipation
## 6231 6231 hope joy
## 6232 6232 hope positive
## 6233 6233 hope surprise
## 6234 6234 hope trust
## 6235 6235 hopeful anticipation
## 6236 6236 hopeful joy
## 6237 6237 hopeful positive
## 6238 6238 hopeful surprise
## 6239 6239 hopeful trust
## 6240 6240 hopeless fear
## 6241 6241 hopeless negative
## 6242 6242 hopeless sadness
## 6243 6243 hopelessness anger
## 6244 6244 hopelessness disgust
## 6245 6245 hopelessness fear
## 6246 6246 hopelessness negative
## 6247 6247 hopelessness sadness
## 6248 6248 horde negative
## 6249 6249 horde surprise
## 6250 6250 horizon anticipation
## 6251 6251 horizon positive
## 6252 6252 horoscope anticipation
## 6253 6253 horrible anger
## 6254 6254 horrible disgust
## 6255 6255 horrible fear
## 6256 6256 horrible negative
## 6257 6257 horribly negative
## 6258 6258 horrid anger
## 6259 6259 horrid disgust
## 6260 6260 horrid fear
## 6261 6261 horrid negative
## 6262 6262 horrid sadness
## 6263 6263 horrific anger
## 6264 6264 horrific disgust
## 6265 6265 horrific fear
## 6266 6266 horrific negative
## 6267 6267 horrific sadness
## 6268 6268 horrified fear
## 6269 6269 horrified negative
## 6270 6270 horrifying disgust
## 6271 6271 horrifying fear
## 6272 6272 horrifying negative
## 6273 6273 horrifying sadness
## 6274 6274 horror anger
## 6275 6275 horror disgust
## 6276 6276 horror fear
## 6277 6277 horror negative
## 6278 6278 horror sadness
## 6279 6279 horror surprise
## 6280 6280 horrors fear
## 6281 6281 horrors negative
## 6282 6282 horrors sadness
## 6283 6283 horse trust
## 6284 6284 hospice sadness
## 6285 6285 hospital fear
## 6286 6286 hospital sadness
## 6287 6287 hospital trust
## 6288 6288 hospitality positive
## 6289 6289 hostage anger
## 6290 6290 hostage fear
## 6291 6291 hostage negative
## 6292 6292 hostile anger
## 6293 6293 hostile disgust
## 6294 6294 hostile fear
## 6295 6295 hostile negative
## 6296 6296 hostilities anger
## 6297 6297 hostilities fear
## 6298 6298 hostilities negative
## 6299 6299 hostility anger
## 6300 6300 hostility disgust
## 6301 6301 hostility negative
## 6302 6302 hot anger
## 6303 6303 household positive
## 6304 6304 housekeeping positive
## 6305 6305 howl anger
## 6306 6306 howl disgust
## 6307 6307 howl fear
## 6308 6308 howl negative
## 6309 6309 howl sadness
## 6310 6310 howl surprise
## 6311 6311 huff anger
## 6312 6312 huff disgust
## 6313 6313 huff negative
## 6314 6314 hug joy
## 6315 6315 hug positive
## 6316 6316 hug trust
## 6317 6317 hulk disgust
## 6318 6318 humane positive
## 6319 6319 humanitarian anticipation
## 6320 6320 humanitarian joy
## 6321 6321 humanitarian positive
## 6322 6322 humanitarian surprise
## 6323 6323 humanitarian trust
## 6324 6324 humanity joy
## 6325 6325 humanity positive
## 6326 6326 humanity trust
## 6327 6327 humble disgust
## 6328 6328 humble negative
## 6329 6329 humble positive
## 6330 6330 humble sadness
## 6331 6331 humbled positive
## 6332 6332 humbled sadness
## 6333 6333 humbly positive
## 6334 6334 humbug anger
## 6335 6335 humbug disgust
## 6336 6336 humbug negative
## 6337 6337 humbug sadness
## 6338 6338 humiliate anger
## 6339 6339 humiliate negative
## 6340 6340 humiliate sadness
## 6341 6341 humiliating disgust
## 6342 6342 humiliating negative
## 6343 6343 humiliation disgust
## 6344 6344 humiliation negative
## 6345 6345 humiliation sadness
## 6346 6346 humility positive
## 6347 6347 humility trust
## 6348 6348 humorist positive
## 6349 6349 humorous joy
## 6350 6350 humorous positive
## 6351 6351 hunch negative
## 6352 6352 hungry anticipation
## 6353 6353 hungry negative
## 6354 6354 hunter anticipation
## 6355 6355 hunter fear
## 6356 6356 hunter negative
## 6357 6357 hunter sadness
## 6358 6358 hunting anger
## 6359 6359 hunting anticipation
## 6360 6360 hunting fear
## 6361 6361 hunting negative
## 6362 6362 hurrah joy
## 6363 6363 hurrah positive
## 6364 6364 hurricane fear
## 6365 6365 hurricane negative
## 6366 6366 hurried anticipation
## 6367 6367 hurried negative
## 6368 6368 hurry anticipation
## 6369 6369 hurt anger
## 6370 6370 hurt fear
## 6371 6371 hurt negative
## 6372 6372 hurt sadness
## 6373 6373 hurtful anger
## 6374 6374 hurtful disgust
## 6375 6375 hurtful fear
## 6376 6376 hurtful negative
## 6377 6377 hurtful sadness
## 6378 6378 hurting anger
## 6379 6379 hurting fear
## 6380 6380 hurting negative
## 6381 6381 hurting sadness
## 6382 6382 husbandry positive
## 6383 6383 husbandry trust
## 6384 6384 hush positive
## 6385 6385 hustler negative
## 6386 6386 hut positive
## 6387 6387 hut sadness
## 6388 6388 hydra fear
## 6389 6389 hydra negative
## 6390 6390 hydrocephalus disgust
## 6391 6391 hydrocephalus fear
## 6392 6392 hydrocephalus negative
## 6393 6393 hydrocephalus sadness
## 6394 6394 hygienic positive
## 6395 6395 hymn anticipation
## 6396 6396 hymn joy
## 6397 6397 hymn positive
## 6398 6398 hymn sadness
## 6399 6399 hymn trust
## 6400 6400 hype anticipation
## 6401 6401 hype negative
## 6402 6402 hyperbole negative
## 6403 6403 hypertrophy disgust
## 6404 6404 hypertrophy fear
## 6405 6405 hypertrophy surprise
## 6406 6406 hypocrisy negative
## 6407 6407 hypocrite disgust
## 6408 6408 hypocrite negative
## 6409 6409 hypocritical disgust
## 6410 6410 hypocritical negative
## 6411 6411 hypothesis anticipation
## 6412 6412 hypothesis surprise
## 6413 6413 hysteria fear
## 6414 6414 hysteria negative
## 6415 6415 hysterical anger
## 6416 6416 hysterical fear
## 6417 6417 hysterical negative
## 6418 6418 idealism positive
## 6419 6419 idiocy anger
## 6420 6420 idiocy disgust
## 6421 6421 idiocy negative
## 6422 6422 idiocy sadness
## 6423 6423 idiot disgust
## 6424 6424 idiot negative
## 6425 6425 idiotic anger
## 6426 6426 idiotic disgust
## 6427 6427 idiotic negative
## 6428 6428 idler negative
## 6429 6429 idol positive
## 6430 6430 idolatry disgust
## 6431 6431 idolatry fear
## 6432 6432 idolatry negative
## 6433 6433 ignorance negative
## 6434 6434 ignorant disgust
## 6435 6435 ignorant negative
## 6436 6436 ignore negative
## 6437 6437 ill anger
## 6438 6438 ill disgust
## 6439 6439 ill fear
## 6440 6440 ill negative
## 6441 6441 ill sadness
## 6442 6442 illegal anger
## 6443 6443 illegal disgust
## 6444 6444 illegal fear
## 6445 6445 illegal negative
## 6446 6446 illegal sadness
## 6447 6447 illegality anger
## 6448 6448 illegality disgust
## 6449 6449 illegality fear
## 6450 6450 illegality negative
## 6451 6451 illegible negative
## 6452 6452 illegitimate anger
## 6453 6453 illegitimate disgust
## 6454 6454 illegitimate fear
## 6455 6455 illegitimate negative
## 6456 6456 illegitimate sadness
## 6457 6457 illegitimate surprise
## 6458 6458 illicit anger
## 6459 6459 illicit disgust
## 6460 6460 illicit fear
## 6461 6461 illicit negative
## 6462 6462 illiterate disgust
## 6463 6463 illiterate negative
## 6464 6464 illness fear
## 6465 6465 illness negative
## 6466 6466 illness sadness
## 6467 6467 illogical negative
## 6468 6468 illuminate anticipation
## 6469 6469 illuminate joy
## 6470 6470 illuminate positive
## 6471 6471 illuminate surprise
## 6472 6472 illumination joy
## 6473 6473 illumination positive
## 6474 6474 illumination surprise
## 6475 6475 illumination trust
## 6476 6476 illusion negative
## 6477 6477 illusion surprise
## 6478 6478 illustrate positive
## 6479 6479 illustrious positive
## 6480 6480 imaginative positive
## 6481 6481 imitated negative
## 6482 6482 imitation negative
## 6483 6483 immaculate joy
## 6484 6484 immaculate positive
## 6485 6485 immaculate trust
## 6486 6486 immature anticipation
## 6487 6487 immature negative
## 6488 6488 immaturity anger
## 6489 6489 immaturity anticipation
## 6490 6490 immaturity negative
## 6491 6491 immediacy surprise
## 6492 6492 immediately anticipation
## 6493 6493 immediately negative
## 6494 6494 immediately positive
## 6495 6495 immense positive
## 6496 6496 immerse anticipation
## 6497 6497 immerse fear
## 6498 6498 immerse joy
## 6499 6499 immerse positive
## 6500 6500 immerse surprise
## 6501 6501 immerse trust
## 6502 6502 immigrant fear
## 6503 6503 imminent anticipation
## 6504 6504 imminent fear
## 6505 6505 immoral anger
## 6506 6506 immoral disgust
## 6507 6507 immoral fear
## 6508 6508 immoral negative
## 6509 6509 immoral sadness
## 6510 6510 immorality anger
## 6511 6511 immorality disgust
## 6512 6512 immorality negative
## 6513 6513 immortal positive
## 6514 6514 immortality anticipation
## 6515 6515 immovable negative
## 6516 6516 immovable positive
## 6517 6517 immovable trust
## 6518 6518 immunization trust
## 6519 6519 impair negative
## 6520 6520 impairment negative
## 6521 6521 impart positive
## 6522 6522 impart trust
## 6523 6523 impartial positive
## 6524 6524 impartial trust
## 6525 6525 impartiality positive
## 6526 6526 impartiality trust
## 6527 6527 impassable negative
## 6528 6528 impatience negative
## 6529 6529 impatient anticipation
## 6530 6530 impatient negative
## 6531 6531 impeach disgust
## 6532 6532 impeach fear
## 6533 6533 impeach negative
## 6534 6534 impeachment negative
## 6535 6535 impeccable positive
## 6536 6536 impeccable trust
## 6537 6537 impede negative
## 6538 6538 impending anticipation
## 6539 6539 impending fear
## 6540 6540 impenetrable trust
## 6541 6541 imperfection negative
## 6542 6542 imperfectly negative
## 6543 6543 impermeable anger
## 6544 6544 impermeable fear
## 6545 6545 impersonate negative
## 6546 6546 impersonation negative
## 6547 6547 impervious positive
## 6548 6548 implacable negative
## 6549 6549 implicate anger
## 6550 6550 implicate negative
## 6551 6551 impolite disgust
## 6552 6552 impolite negative
## 6553 6553 importance anticipation
## 6554 6554 importance positive
## 6555 6555 important positive
## 6556 6556 important trust
## 6557 6557 imposition negative
## 6558 6558 impossible negative
## 6559 6559 impossible sadness
## 6560 6560 impotence anger
## 6561 6561 impotence fear
## 6562 6562 impotence negative
## 6563 6563 impotence sadness
## 6564 6564 impotent negative
## 6565 6565 impound negative
## 6566 6566 impracticable negative
## 6567 6567 impress positive
## 6568 6568 impression positive
## 6569 6569 impressionable trust
## 6570 6570 imprison negative
## 6571 6571 imprisoned anger
## 6572 6572 imprisoned disgust
## 6573 6573 imprisoned fear
## 6574 6574 imprisoned negative
## 6575 6575 imprisoned sadness
## 6576 6576 imprisonment anger
## 6577 6577 imprisonment disgust
## 6578 6578 imprisonment fear
## 6579 6579 imprisonment negative
## 6580 6580 imprisonment sadness
## 6581 6581 impropriety negative
## 6582 6582 improve anticipation
## 6583 6583 improve joy
## 6584 6584 improve positive
## 6585 6585 improve trust
## 6586 6586 improved positive
## 6587 6587 improvement joy
## 6588 6588 improvement positive
## 6589 6589 improvement trust
## 6590 6590 improving positive
## 6591 6591 improvisation surprise
## 6592 6592 improvise anticipation
## 6593 6593 improvise positive
## 6594 6594 improvise surprise
## 6595 6595 imprudent negative
## 6596 6596 imprudent sadness
## 6597 6597 impure disgust
## 6598 6598 impure negative
## 6599 6599 impurity disgust
## 6600 6600 impurity negative
## 6601 6601 imputation negative
## 6602 6602 inability negative
## 6603 6603 inability sadness
## 6604 6604 inaccessible negative
## 6605 6605 inaccurate negative
## 6606 6606 inaction negative
## 6607 6607 inactivity negative
## 6608 6608 inadequacy negative
## 6609 6609 inadequate negative
## 6610 6610 inadequate sadness
## 6611 6611 inadmissible anger
## 6612 6612 inadmissible disgust
## 6613 6613 inadmissible negative
## 6614 6614 inalienable positive
## 6615 6615 inane negative
## 6616 6616 inapplicable negative
## 6617 6617 inappropriate anger
## 6618 6618 inappropriate disgust
## 6619 6619 inappropriate negative
## 6620 6620 inappropriate sadness
## 6621 6621 inattention anger
## 6622 6622 inattention negative
## 6623 6623 inaudible negative
## 6624 6624 inaugural anticipation
## 6625 6625 inauguration anticipation
## 6626 6626 inauguration joy
## 6627 6627 inauguration positive
## 6628 6628 inauguration trust
## 6629 6629 incalculable negative
## 6630 6630 incapacity negative
## 6631 6631 incarceration anger
## 6632 6632 incarceration disgust
## 6633 6633 incarceration fear
## 6634 6634 incarceration negative
## 6635 6635 incarceration sadness
## 6636 6636 incase anger
## 6637 6637 incase disgust
## 6638 6638 incase fear
## 6639 6639 incase negative
## 6640 6640 incase sadness
## 6641 6641 incendiary anger
## 6642 6642 incendiary fear
## 6643 6643 incendiary negative
## 6644 6644 incendiary surprise
## 6645 6645 incense anger
## 6646 6646 incense negative
## 6647 6647 incessant negative
## 6648 6648 incest anger
## 6649 6649 incest disgust
## 6650 6650 incest fear
## 6651 6651 incest negative
## 6652 6652 incest sadness
## 6653 6653 incestuous disgust
## 6654 6654 incestuous negative
## 6655 6655 incident surprise
## 6656 6656 incineration negative
## 6657 6657 incisive positive
## 6658 6658 incite anger
## 6659 6659 incite anticipation
## 6660 6660 incite fear
## 6661 6661 incite negative
## 6662 6662 inclement negative
## 6663 6663 incline trust
## 6664 6664 include positive
## 6665 6665 included positive
## 6666 6666 including positive
## 6667 6667 inclusion trust
## 6668 6668 inclusive positive
## 6669 6669 incoherent negative
## 6670 6670 income anticipation
## 6671 6671 income joy
## 6672 6672 income negative
## 6673 6673 income positive
## 6674 6674 income sadness
## 6675 6675 income trust
## 6676 6676 incompatible anger
## 6677 6677 incompatible disgust
## 6678 6678 incompatible negative
## 6679 6679 incompatible sadness
## 6680 6680 incompetence negative
## 6681 6681 incompetent anger
## 6682 6682 incompetent negative
## 6683 6683 incompetent sadness
## 6684 6684 incompleteness negative
## 6685 6685 incomprehensible negative
## 6686 6686 incongruous anger
## 6687 6687 incongruous negative
## 6688 6688 inconsequential negative
## 6689 6689 inconsequential sadness
## 6690 6690 inconsiderate anger
## 6691 6691 inconsiderate disgust
## 6692 6692 inconsiderate negative
## 6693 6693 inconsiderate sadness
## 6694 6694 inconsistency negative
## 6695 6695 incontinence surprise
## 6696 6696 inconvenient anger
## 6697 6697 inconvenient disgust
## 6698 6698 inconvenient negative
## 6699 6699 inconvenient sadness
## 6700 6700 incorrect negative
## 6701 6701 increase positive
## 6702 6702 incredulous anger
## 6703 6703 incredulous disgust
## 6704 6704 incredulous negative
## 6705 6705 incrimination fear
## 6706 6706 incrimination negative
## 6707 6707 incrimination sadness
## 6708 6708 incubus disgust
## 6709 6709 incubus fear
## 6710 6710 incubus negative
## 6711 6711 incur negative
## 6712 6712 incurable anger
## 6713 6713 incurable disgust
## 6714 6714 incurable fear
## 6715 6715 incurable negative
## 6716 6716 incurable sadness
## 6717 6717 incursion fear
## 6718 6718 incursion negative
## 6719 6719 indecency anger
## 6720 6720 indecency disgust
## 6721 6721 indecent disgust
## 6722 6722 indecent negative
## 6723 6723 indecision negative
## 6724 6724 indecisive negative
## 6725 6725 indefensible fear
## 6726 6726 indefensible negative
## 6727 6727 indelible positive
## 6728 6728 indelible trust
## 6729 6729 indemnify negative
## 6730 6730 indemnity positive
## 6731 6731 indemnity trust
## 6732 6732 indent trust
## 6733 6733 indenture anger
## 6734 6734 indenture negative
## 6735 6735 independence anticipation
## 6736 6736 independence joy
## 6737 6737 independence positive
## 6738 6738 independence surprise
## 6739 6739 independence trust
## 6740 6740 indestructible positive
## 6741 6741 indestructible trust
## 6742 6742 indeterminate negative
## 6743 6743 indict anger
## 6744 6744 indict fear
## 6745 6745 indict negative
## 6746 6746 indictment fear
## 6747 6747 indictment negative
## 6748 6748 indifference anger
## 6749 6749 indifference disgust
## 6750 6750 indifference fear
## 6751 6751 indifference negative
## 6752 6752 indifference sadness
## 6753 6753 indigent negative
## 6754 6754 indigent sadness
## 6755 6755 indignant anger
## 6756 6756 indignant negative
## 6757 6757 indignation anger
## 6758 6758 indignation disgust
## 6759 6759 indignation negative
## 6760 6760 indistinct negative
## 6761 6761 individuality positive
## 6762 6762 indivisible trust
## 6763 6763 indoctrination anger
## 6764 6764 indoctrination fear
## 6765 6765 indoctrination negative
## 6766 6766 indolent negative
## 6767 6767 indomitable fear
## 6768 6768 indomitable positive
## 6769 6769 ineffable positive
## 6770 6770 ineffective negative
## 6771 6771 ineffectual disgust
## 6772 6772 ineffectual negative
## 6773 6773 inefficiency disgust
## 6774 6774 inefficiency negative
## 6775 6775 inefficiency sadness
## 6776 6776 inefficient negative
## 6777 6777 inefficient sadness
## 6778 6778 inept anger
## 6779 6779 inept disgust
## 6780 6780 inept negative
## 6781 6781 ineptitude disgust
## 6782 6782 ineptitude fear
## 6783 6783 ineptitude negative
## 6784 6784 ineptitude sadness
## 6785 6785 inequality anger
## 6786 6786 inequality fear
## 6787 6787 inequality negative
## 6788 6788 inequality sadness
## 6789 6789 inequitable negative
## 6790 6790 inert negative
## 6791 6791 inexcusable anger
## 6792 6792 inexcusable disgust
## 6793 6793 inexcusable negative
## 6794 6794 inexcusable sadness
## 6795 6795 inexpensive positive
## 6796 6796 inexperience negative
## 6797 6797 inexperienced negative
## 6798 6798 inexplicable negative
## 6799 6799 inexplicable surprise
## 6800 6800 infallibility trust
## 6801 6801 infallible positive
## 6802 6802 infamous anger
## 6803 6803 infamous disgust
## 6804 6804 infamous fear
## 6805 6805 infamous negative
## 6806 6806 infamy negative
## 6807 6807 infamy sadness
## 6808 6808 infant anticipation
## 6809 6809 infant fear
## 6810 6810 infant joy
## 6811 6811 infant positive
## 6812 6812 infant surprise
## 6813 6813 infanticide anger
## 6814 6814 infanticide anticipation
## 6815 6815 infanticide disgust
## 6816 6816 infanticide fear
## 6817 6817 infanticide negative
## 6818 6818 infanticide sadness
## 6819 6819 infantile anger
## 6820 6820 infantile disgust
## 6821 6821 infantile negative
## 6822 6822 infarct fear
## 6823 6823 infarct negative
## 6824 6824 infarct surprise
## 6825 6825 infect disgust
## 6826 6826 infect negative
## 6827 6827 infection fear
## 6828 6828 infection negative
## 6829 6829 infectious disgust
## 6830 6830 infectious fear
## 6831 6831 infectious negative
## 6832 6832 infectious sadness
## 6833 6833 inferior negative
## 6834 6834 inferior sadness
## 6835 6835 inferiority negative
## 6836 6836 inferno anger
## 6837 6837 inferno fear
## 6838 6838 inferno negative
## 6839 6839 infertility negative
## 6840 6840 infertility sadness
## 6841 6841 infestation disgust
## 6842 6842 infestation fear
## 6843 6843 infestation negative
## 6844 6844 infidel anger
## 6845 6845 infidel disgust
## 6846 6846 infidel fear
## 6847 6847 infidel negative
## 6848 6848 infidelity anger
## 6849 6849 infidelity disgust
## 6850 6850 infidelity fear
## 6851 6851 infidelity negative
## 6852 6852 infidelity sadness
## 6853 6853 infiltration negative
## 6854 6854 infiltration positive
## 6855 6855 infinite positive
## 6856 6856 infinity anticipation
## 6857 6857 infinity joy
## 6858 6858 infinity positive
## 6859 6859 infinity trust
## 6860 6860 infirm negative
## 6861 6861 infirmity fear
## 6862 6862 infirmity negative
## 6863 6863 inflammation negative
## 6864 6864 inflated negative
## 6865 6865 inflation fear
## 6866 6866 inflation negative
## 6867 6867 inflict anger
## 6868 6868 inflict fear
## 6869 6869 inflict negative
## 6870 6870 inflict sadness
## 6871 6871 infliction fear
## 6872 6872 infliction negative
## 6873 6873 infliction sadness
## 6874 6874 influence negative
## 6875 6875 influence positive
## 6876 6876 influential positive
## 6877 6877 influential trust
## 6878 6878 influenza negative
## 6879 6879 inform trust
## 6880 6880 information positive
## 6881 6881 informer negative
## 6882 6882 infraction anger
## 6883 6883 infraction negative
## 6884 6884 infrequent surprise
## 6885 6885 infrequently negative
## 6886 6886 infringement negative
## 6887 6887 ingenious positive
## 6888 6888 inheritance anticipation
## 6889 6889 inheritance joy
## 6890 6890 inheritance positive
## 6891 6891 inheritance surprise
## 6892 6892 inheritance trust
## 6893 6893 inhibit anger
## 6894 6894 inhibit disgust
## 6895 6895 inhibit negative
## 6896 6896 inhibit sadness
## 6897 6897 inhospitable negative
## 6898 6898 inhospitable sadness
## 6899 6899 inhuman anger
## 6900 6900 inhuman disgust
## 6901 6901 inhuman fear
## 6902 6902 inhuman negative
## 6903 6903 inhuman sadness
## 6904 6904 inhumanity negative
## 6905 6905 inhumanity sadness
## 6906 6906 inimical anger
## 6907 6907 inimical negative
## 6908 6908 inimical sadness
## 6909 6909 inimitable positive
## 6910 6910 inimitable trust
## 6911 6911 iniquity disgust
## 6912 6912 iniquity negative
## 6913 6913 injection fear
## 6914 6914 injunction negative
## 6915 6915 injure anger
## 6916 6916 injure fear
## 6917 6917 injure negative
## 6918 6918 injure sadness
## 6919 6919 injured fear
## 6920 6920 injured negative
## 6921 6921 injured sadness
## 6922 6922 injurious anger
## 6923 6923 injurious fear
## 6924 6924 injurious negative
## 6925 6925 injurious sadness
## 6926 6926 injury anger
## 6927 6927 injury fear
## 6928 6928 injury negative
## 6929 6929 injury sadness
## 6930 6930 injustice anger
## 6931 6931 injustice negative
## 6932 6932 inmate disgust
## 6933 6933 inmate fear
## 6934 6934 inmate negative
## 6935 6935 innocence positive
## 6936 6936 innocent positive
## 6937 6937 innocent trust
## 6938 6938 innocently positive
## 6939 6939 innocuous positive
## 6940 6940 innovate positive
## 6941 6941 innovation positive
## 6942 6942 inoculation anticipation
## 6943 6943 inoculation trust
## 6944 6944 inoperative anger
## 6945 6945 inoperative negative
## 6946 6946 inquirer positive
## 6947 6947 inquiry anticipation
## 6948 6948 inquiry positive
## 6949 6949 inquisitive positive
## 6950 6950 insane anger
## 6951 6951 insane fear
## 6952 6952 insane negative
## 6953 6953 insanity anger
## 6954 6954 insanity disgust
## 6955 6955 insanity fear
## 6956 6956 insanity negative
## 6957 6957 insanity sadness
## 6958 6958 insecure anger
## 6959 6959 insecure fear
## 6960 6960 insecure negative
## 6961 6961 insecure sadness
## 6962 6962 insecurity fear
## 6963 6963 insecurity negative
## 6964 6964 inseparable joy
## 6965 6965 inseparable positive
## 6966 6966 inseparable trust
## 6967 6967 insidious anger
## 6968 6968 insidious disgust
## 6969 6969 insidious fear
## 6970 6970 insidious negative
## 6971 6971 insignia positive
## 6972 6972 insignificance negative
## 6973 6973 insignificant anger
## 6974 6974 insignificant negative
## 6975 6975 insignificant sadness
## 6976 6976 insipid negative
## 6977 6977 insolent negative
## 6978 6978 insolvency fear
## 6979 6979 insolvency negative
## 6980 6980 insolvency sadness
## 6981 6981 insolvency surprise
## 6982 6982 insolvent fear
## 6983 6983 insolvent negative
## 6984 6984 insolvent sadness
## 6985 6985 insolvent trust
## 6986 6986 inspector positive
## 6987 6987 inspiration anticipation
## 6988 6988 inspiration joy
## 6989 6989 inspiration positive
## 6990 6990 inspire anticipation
## 6991 6991 inspire joy
## 6992 6992 inspire positive
## 6993 6993 inspire trust
## 6994 6994 inspired joy
## 6995 6995 inspired positive
## 6996 6996 inspired surprise
## 6997 6997 inspired trust
## 6998 6998 instability disgust
## 6999 6999 instability fear
## 7000 7000 instability negative
## 7001 7001 install anticipation
## 7002 7002 instigate negative
## 7003 7003 instigation negative
## 7004 7004 instinctive anger
## 7005 7005 instinctive disgust
## 7006 7006 instinctive fear
## 7007 7007 instinctive positive
## 7008 7008 institute trust
## 7009 7009 instruct positive
## 7010 7010 instruct trust
## 7011 7011 instruction positive
## 7012 7012 instruction trust
## 7013 7013 instructions anticipation
## 7014 7014 instructions trust
## 7015 7015 instructor anticipation
## 7016 7016 instructor positive
## 7017 7017 instructor trust
## 7018 7018 instrumental positive
## 7019 7019 insufficiency anger
## 7020 7020 insufficiency negative
## 7021 7021 insufficient negative
## 7022 7022 insufficiently negative
## 7023 7023 insulation trust
## 7024 7024 insult anger
## 7025 7025 insult disgust
## 7026 7026 insult negative
## 7027 7027 insult sadness
## 7028 7028 insult surprise
## 7029 7029 insulting anger
## 7030 7030 insulting disgust
## 7031 7031 insulting fear
## 7032 7032 insulting negative
## 7033 7033 insulting sadness
## 7034 7034 insure positive
## 7035 7035 insure trust
## 7036 7036 insurgent negative
## 7037 7037 insurmountable fear
## 7038 7038 insurmountable negative
## 7039 7039 insurmountable sadness
## 7040 7040 insurrection anger
## 7041 7041 insurrection negative
## 7042 7042 intact positive
## 7043 7043 intact trust
## 7044 7044 integrity positive
## 7045 7045 integrity trust
## 7046 7046 intellect positive
## 7047 7047 intellectual positive
## 7048 7048 intelligence fear
## 7049 7049 intelligence joy
## 7050 7050 intelligence positive
## 7051 7051 intelligence trust
## 7052 7052 intelligent positive
## 7053 7053 intelligent trust
## 7054 7054 intend trust
## 7055 7055 intended anticipation
## 7056 7056 intended positive
## 7057 7057 intense anger
## 7058 7058 intense disgust
## 7059 7059 intense fear
## 7060 7060 intense joy
## 7061 7061 intense negative
## 7062 7062 intense positive
## 7063 7063 intense surprise
## 7064 7064 intense trust
## 7065 7065 inter negative
## 7066 7066 inter sadness
## 7067 7067 intercede fear
## 7068 7068 intercede sadness
## 7069 7069 intercession trust
## 7070 7070 intercourse positive
## 7071 7071 interdiction negative
## 7072 7072 interest positive
## 7073 7073 interested disgust
## 7074 7074 interested positive
## 7075 7075 interested sadness
## 7076 7076 interesting positive
## 7077 7077 interference negative
## 7078 7078 interim anticipation
## 7079 7079 interior disgust
## 7080 7080 interior positive
## 7081 7081 interior trust
## 7082 7082 interlocutory positive
## 7083 7083 interlude positive
## 7084 7084 interment negative
## 7085 7085 interment sadness
## 7086 7086 interminable anger
## 7087 7087 interminable anticipation
## 7088 7088 interminable negative
## 7089 7089 interminable positive
## 7090 7090 intermission anticipation
## 7091 7091 interrogate fear
## 7092 7092 interrogation fear
## 7093 7093 interrupt anger
## 7094 7094 interrupt negative
## 7095 7095 interrupt surprise
## 7096 7096 interrupted negative
## 7097 7097 interrupted sadness
## 7098 7098 intervention negative
## 7099 7099 intervention positive
## 7100 7100 intervention sadness
## 7101 7101 interviewer fear
## 7102 7102 intestate negative
## 7103 7103 intestinal disgust
## 7104 7104 intimate anticipation
## 7105 7105 intimate joy
## 7106 7106 intimate positive
## 7107 7107 intimate trust
## 7108 7108 intimately anticipation
## 7109 7109 intimately fear
## 7110 7110 intimately joy
## 7111 7111 intimidate fear
## 7112 7112 intimidate negative
## 7113 7113 intimidation anger
## 7114 7114 intimidation fear
## 7115 7115 intimidation negative
## 7116 7116 intolerable anger
## 7117 7117 intolerable negative
## 7118 7118 intolerance anger
## 7119 7119 intolerance disgust
## 7120 7120 intolerance fear
## 7121 7121 intolerance negative
## 7122 7122 intolerant anger
## 7123 7123 intolerant disgust
## 7124 7124 intolerant fear
## 7125 7125 intolerant negative
## 7126 7126 intolerant sadness
## 7127 7127 intonation positive
## 7128 7128 intoxicated disgust
## 7129 7129 intoxicated negative
## 7130 7130 intractable anger
## 7131 7131 intractable negative
## 7132 7132 intrepid positive
## 7133 7133 intrigue anticipation
## 7134 7134 intrigue fear
## 7135 7135 intrigue negative
## 7136 7136 intrigue surprise
## 7137 7137 intruder anger
## 7138 7138 intruder fear
## 7139 7139 intruder negative
## 7140 7140 intruder surprise
## 7141 7141 intrusion fear
## 7142 7142 intrusion negative
## 7143 7143 intrusive anger
## 7144 7144 intrusive disgust
## 7145 7145 intrusive fear
## 7146 7146 intrusive negative
## 7147 7147 intrusive surprise
## 7148 7148 intuition positive
## 7149 7149 intuition trust
## 7150 7150 intuitive positive
## 7151 7151 intuitively anticipation
## 7152 7152 invade anger
## 7153 7153 invade fear
## 7154 7154 invade negative
## 7155 7155 invade sadness
## 7156 7156 invade surprise
## 7157 7157 invader anger
## 7158 7158 invader fear
## 7159 7159 invader negative
## 7160 7160 invader sadness
## 7161 7161 invalid sadness
## 7162 7162 invalidate negative
## 7163 7163 invalidation negative
## 7164 7164 invalidity negative
## 7165 7165 invariably positive
## 7166 7166 invasion anger
## 7167 7167 invasion negative
## 7168 7168 inventive positive
## 7169 7169 inventor positive
## 7170 7170 investigate positive
## 7171 7171 investigation anticipation
## 7172 7172 invigorate positive
## 7173 7173 invitation anticipation
## 7174 7174 invitation positive
## 7175 7175 invite anticipation
## 7176 7176 invite joy
## 7177 7177 invite positive
## 7178 7178 invite surprise
## 7179 7179 invite trust
## 7180 7180 inviting anticipation
## 7181 7181 inviting joy
## 7182 7182 inviting positive
## 7183 7183 inviting surprise
## 7184 7184 inviting trust
## 7185 7185 invocation anticipation
## 7186 7186 invocation trust
## 7187 7187 invoke anticipation
## 7188 7188 involuntary negative
## 7189 7189 involution anger
## 7190 7190 involution negative
## 7191 7191 involvement anger
## 7192 7192 irate anger
## 7193 7193 irate negative
## 7194 7194 ire anger
## 7195 7195 ire negative
## 7196 7196 iris fear
## 7197 7197 iron positive
## 7198 7198 iron trust
## 7199 7199 irons negative
## 7200 7200 irrational disgust
## 7201 7201 irrational fear
## 7202 7202 irrational negative
## 7203 7203 irrationality negative
## 7204 7204 irreconcilable anger
## 7205 7205 irreconcilable fear
## 7206 7206 irreconcilable negative
## 7207 7207 irreconcilable sadness
## 7208 7208 irreducible positive
## 7209 7209 irrefutable positive
## 7210 7210 irrefutable trust
## 7211 7211 irregular negative
## 7212 7212 irregularity negative
## 7213 7213 irrelevant negative
## 7214 7214 irreparable fear
## 7215 7215 irreparable negative
## 7216 7216 irreparable sadness
## 7217 7217 irresponsible negative
## 7218 7218 irreverent negative
## 7219 7219 irrevocable negative
## 7220 7220 irritability anger
## 7221 7221 irritability negative
## 7222 7222 irritable anger
## 7223 7223 irritable negative
## 7224 7224 irritating anger
## 7225 7225 irritating disgust
## 7226 7226 irritating negative
## 7227 7227 irritation anger
## 7228 7228 irritation disgust
## 7229 7229 irritation negative
## 7230 7230 irritation sadness
## 7231 7231 isolate sadness
## 7232 7232 isolated fear
## 7233 7233 isolated negative
## 7234 7234 isolated sadness
## 7235 7235 isolation negative
## 7236 7236 isolation sadness
## 7237 7237 jab anger
## 7238 7238 jabber negative
## 7239 7239 jackpot anticipation
## 7240 7240 jackpot joy
## 7241 7241 jackpot positive
## 7242 7242 jackpot surprise
## 7243 7243 jail fear
## 7244 7244 jail negative
## 7245 7245 jail sadness
## 7246 7246 jam positive
## 7247 7247 janitor disgust
## 7248 7248 jargon negative
## 7249 7249 jarring fear
## 7250 7250 jarring negative
## 7251 7251 jarring sadness
## 7252 7252 jaundice fear
## 7253 7253 jaundice negative
## 7254 7254 jaws fear
## 7255 7255 jealous anger
## 7256 7256 jealous disgust
## 7257 7257 jealous negative
## 7258 7258 jealousy anger
## 7259 7259 jealousy disgust
## 7260 7260 jealousy fear
## 7261 7261 jealousy negative
## 7262 7262 jealousy sadness
## 7263 7263 jeopardize anger
## 7264 7264 jeopardize fear
## 7265 7265 jeopardize negative
## 7266 7266 jeopardy anticipation
## 7267 7267 jeopardy fear
## 7268 7268 jeopardy negative
## 7269 7269 jerk anger
## 7270 7270 jerk surprise
## 7271 7271 jest joy
## 7272 7272 jest positive
## 7273 7273 jest surprise
## 7274 7274 job positive
## 7275 7275 john disgust
## 7276 7276 john negative
## 7277 7277 join positive
## 7278 7278 joined positive
## 7279 7279 joke negative
## 7280 7280 joker joy
## 7281 7281 joker positive
## 7282 7282 joker surprise
## 7283 7283 joking positive
## 7284 7284 jolt surprise
## 7285 7285 jornada negative
## 7286 7286 journalism trust
## 7287 7287 journalist positive
## 7288 7288 journey anticipation
## 7289 7289 journey fear
## 7290 7290 journey joy
## 7291 7291 journey positive
## 7292 7292 journeyman trust
## 7293 7293 jovial joy
## 7294 7294 jovial positive
## 7295 7295 joy joy
## 7296 7296 joy positive
## 7297 7297 joyful joy
## 7298 7298 joyful positive
## 7299 7299 joyful trust
## 7300 7300 joyous joy
## 7301 7301 joyous positive
## 7302 7302 jubilant joy
## 7303 7303 jubilant positive
## 7304 7304 jubilant surprise
## 7305 7305 jubilant trust
## 7306 7306 jubilee joy
## 7307 7307 jubilee positive
## 7308 7308 jubilee surprise
## 7309 7309 judgment surprise
## 7310 7310 judicial anticipation
## 7311 7311 judicial positive
## 7312 7312 judicial trust
## 7313 7313 judiciary anticipation
## 7314 7314 judiciary trust
## 7315 7315 judicious positive
## 7316 7316 judicious trust
## 7317 7317 jumble negative
## 7318 7318 jump joy
## 7319 7319 jump positive
## 7320 7320 jungle fear
## 7321 7321 junk negative
## 7322 7322 junta negative
## 7323 7323 jurisprudence sadness
## 7324 7324 jurist trust
## 7325 7325 jury trust
## 7326 7326 justice positive
## 7327 7327 justice trust
## 7328 7328 justifiable positive
## 7329 7329 justifiable trust
## 7330 7330 justification positive
## 7331 7331 juvenile negative
## 7332 7332 keepsake positive
## 7333 7333 ken positive
## 7334 7334 kennel sadness
## 7335 7335 kern negative
## 7336 7336 kerosene fear
## 7337 7337 keynote positive
## 7338 7338 keystone positive
## 7339 7339 khan fear
## 7340 7340 khan trust
## 7341 7341 kick anger
## 7342 7342 kick negative
## 7343 7343 kicking anger
## 7344 7344 kidnap anger
## 7345 7345 kidnap fear
## 7346 7346 kidnap negative
## 7347 7347 kidnap sadness
## 7348 7348 kidnap surprise
## 7349 7349 kill fear
## 7350 7350 kill negative
## 7351 7351 kill sadness
## 7352 7352 killing anger
## 7353 7353 killing fear
## 7354 7354 killing negative
## 7355 7355 killing sadness
## 7356 7356 kind joy
## 7357 7357 kind positive
## 7358 7358 kind trust
## 7359 7359 kindness positive
## 7360 7360 kindred anticipation
## 7361 7361 kindred joy
## 7362 7362 kindred positive
## 7363 7363 kindred trust
## 7364 7364 king positive
## 7365 7365 kiss anticipation
## 7366 7366 kiss joy
## 7367 7367 kiss positive
## 7368 7368 kiss surprise
## 7369 7369 kite disgust
## 7370 7370 kite negative
## 7371 7371 kitten joy
## 7372 7372 kitten positive
## 7373 7373 kitten trust
## 7374 7374 knack positive
## 7375 7375 knell fear
## 7376 7376 knell negative
## 7377 7377 knell sadness
## 7378 7378 knickers trust
## 7379 7379 knight positive
## 7380 7380 knotted negative
## 7381 7381 knowing positive
## 7382 7382 knowledge positive
## 7383 7383 kudos joy
## 7384 7384 kudos positive
## 7385 7385 label trust
## 7386 7386 labor anticipation
## 7387 7387 labor joy
## 7388 7388 labor positive
## 7389 7389 labor surprise
## 7390 7390 labor trust
## 7391 7391 labored negative
## 7392 7392 labored sadness
## 7393 7393 laborious negative
## 7394 7394 labyrinth anticipation
## 7395 7395 labyrinth negative
## 7396 7396 lace anger
## 7397 7397 lace fear
## 7398 7398 lace negative
## 7399 7399 lace positive
## 7400 7400 lace sadness
## 7401 7401 lace trust
## 7402 7402 lack negative
## 7403 7403 lacking negative
## 7404 7404 lackluster negative
## 7405 7405 laden negative
## 7406 7406 lag negative
## 7407 7407 lagging anger
## 7408 7408 lagging anticipation
## 7409 7409 lagging disgust
## 7410 7410 lagging negative
## 7411 7411 lagging sadness
## 7412 7412 lair negative
## 7413 7413 lamb joy
## 7414 7414 lamb positive
## 7415 7415 lamb trust
## 7416 7416 lament disgust
## 7417 7417 lament fear
## 7418 7418 lament negative
## 7419 7419 lament sadness
## 7420 7420 lamenting sadness
## 7421 7421 land positive
## 7422 7422 landed positive
## 7423 7423 landmark trust
## 7424 7424 landslide fear
## 7425 7425 landslide negative
## 7426 7426 landslide sadness
## 7427 7427 languid negative
## 7428 7428 languish negative
## 7429 7429 languishing fear
## 7430 7430 languishing negative
## 7431 7431 languishing sadness
## 7432 7432 lapse negative
## 7433 7433 larceny disgust
## 7434 7434 larceny negative
## 7435 7435 larger disgust
## 7436 7436 larger surprise
## 7437 7437 larger trust
## 7438 7438 laser positive
## 7439 7439 laser trust
## 7440 7440 lash anger
## 7441 7441 lash fear
## 7442 7442 lash negative
## 7443 7443 late negative
## 7444 7444 late sadness
## 7445 7445 lateness negative
## 7446 7446 latent anger
## 7447 7447 latent anticipation
## 7448 7448 latent disgust
## 7449 7449 latent negative
## 7450 7450 latent surprise
## 7451 7451 latrines disgust
## 7452 7452 latrines negative
## 7453 7453 laudable positive
## 7454 7454 laugh joy
## 7455 7455 laugh positive
## 7456 7456 laugh surprise
## 7457 7457 laughable disgust
## 7458 7458 laughable negative
## 7459 7459 laughing joy
## 7460 7460 laughing positive
## 7461 7461 laughter anticipation
## 7462 7462 laughter joy
## 7463 7463 laughter positive
## 7464 7464 laughter surprise
## 7465 7465 launch anticipation
## 7466 7466 launch positive
## 7467 7467 laureate positive
## 7468 7468 laureate trust
## 7469 7469 laurel positive
## 7470 7470 laurels joy
## 7471 7471 laurels positive
## 7472 7472 lava anger
## 7473 7473 lava fear
## 7474 7474 lava negative
## 7475 7475 lavatory disgust
## 7476 7476 lavish positive
## 7477 7477 law trust
## 7478 7478 lawful positive
## 7479 7479 lawful trust
## 7480 7480 lawlessness anger
## 7481 7481 lawlessness fear
## 7482 7482 lawlessness negative
## 7483 7483 lawsuit anger
## 7484 7484 lawsuit disgust
## 7485 7485 lawsuit fear
## 7486 7486 lawsuit negative
## 7487 7487 lawsuit sadness
## 7488 7488 lawsuit surprise
## 7489 7489 lawyer anger
## 7490 7490 lawyer disgust
## 7491 7491 lawyer fear
## 7492 7492 lawyer negative
## 7493 7493 lax negative
## 7494 7494 lax sadness
## 7495 7495 laxative disgust
## 7496 7496 laxative fear
## 7497 7497 laxative negative
## 7498 7498 lazy negative
## 7499 7499 lead positive
## 7500 7500 leader positive
## 7501 7501 leader trust
## 7502 7502 leading trust
## 7503 7503 league positive
## 7504 7504 leak negative
## 7505 7505 leakage negative
## 7506 7506 leaky negative
## 7507 7507 leaning trust
## 7508 7508 learn positive
## 7509 7509 learning positive
## 7510 7510 leave negative
## 7511 7511 leave sadness
## 7512 7512 leave surprise
## 7513 7513 lecturer positive
## 7514 7514 leech negative
## 7515 7515 leeches disgust
## 7516 7516 leeches fear
## 7517 7517 leeches negative
## 7518 7518 leer disgust
## 7519 7519 leer negative
## 7520 7520 leery surprise
## 7521 7521 leeway positive
## 7522 7522 legal positive
## 7523 7523 legal trust
## 7524 7524 legalized anger
## 7525 7525 legalized fear
## 7526 7526 legalized joy
## 7527 7527 legalized positive
## 7528 7528 legalized trust
## 7529 7529 legendary positive
## 7530 7530 legibility positive
## 7531 7531 legible positive
## 7532 7532 legislator trust
## 7533 7533 legislature trust
## 7534 7534 legitimacy trust
## 7535 7535 leisure anticipation
## 7536 7536 leisure joy
## 7537 7537 leisure positive
## 7538 7538 leisure surprise
## 7539 7539 leisure trust
## 7540 7540 leisurely positive
## 7541 7541 lemma positive
## 7542 7542 lemon disgust
## 7543 7543 lemon negative
## 7544 7544 lender trust
## 7545 7545 lenient positive
## 7546 7546 leprosy disgust
## 7547 7547 leprosy fear
## 7548 7548 leprosy negative
## 7549 7549 leprosy sadness
## 7550 7550 lessen anticipation
## 7551 7551 lessen negative
## 7552 7552 lesser disgust
## 7553 7553 lesser negative
## 7554 7554 lesson anticipation
## 7555 7555 lesson positive
## 7556 7556 lesson trust
## 7557 7557 lethal disgust
## 7558 7558 lethal fear
## 7559 7559 lethal negative
## 7560 7560 lethal sadness
## 7561 7561 lethargy negative
## 7562 7562 lethargy sadness
## 7563 7563 letter anticipation
## 7564 7564 lettered anticipation
## 7565 7565 lettered positive
## 7566 7566 lettered trust
## 7567 7567 leukemia anger
## 7568 7568 leukemia fear
## 7569 7569 leukemia negative
## 7570 7570 leukemia sadness
## 7571 7571 levee trust
## 7572 7572 level positive
## 7573 7573 level trust
## 7574 7574 leverage positive
## 7575 7575 levy negative
## 7576 7576 lewd disgust
## 7577 7577 lewd negative
## 7578 7578 liaison negative
## 7579 7579 liar disgust
## 7580 7580 liar negative
## 7581 7581 libel anger
## 7582 7582 libel fear
## 7583 7583 libel negative
## 7584 7584 libel trust
## 7585 7585 liberal negative
## 7586 7586 liberal positive
## 7587 7587 liberate anger
## 7588 7588 liberate anticipation
## 7589 7589 liberate joy
## 7590 7590 liberate positive
## 7591 7591 liberate surprise
## 7592 7592 liberate trust
## 7593 7593 liberation anticipation
## 7594 7594 liberation joy
## 7595 7595 liberation positive
## 7596 7596 liberation surprise
## 7597 7597 liberty anticipation
## 7598 7598 liberty joy
## 7599 7599 liberty positive
## 7600 7600 liberty surprise
## 7601 7601 liberty trust
## 7602 7602 library positive
## 7603 7603 lick disgust
## 7604 7604 lick negative
## 7605 7605 lie anger
## 7606 7606 lie disgust
## 7607 7607 lie negative
## 7608 7608 lie sadness
## 7609 7609 lieutenant trust
## 7610 7610 lifeblood positive
## 7611 7611 lifeless fear
## 7612 7612 lifeless negative
## 7613 7613 lifeless sadness
## 7614 7614 lighthouse positive
## 7615 7615 lightning anger
## 7616 7616 lightning fear
## 7617 7617 lightning surprise
## 7618 7618 liking joy
## 7619 7619 liking positive
## 7620 7620 liking trust
## 7621 7621 limited anger
## 7622 7622 limited negative
## 7623 7623 limited sadness
## 7624 7624 limp negative
## 7625 7625 lines fear
## 7626 7626 linger anticipation
## 7627 7627 linguist positive
## 7628 7628 linguist trust
## 7629 7629 lint negative
## 7630 7630 lion fear
## 7631 7631 lion positive
## 7632 7632 liquor anger
## 7633 7633 liquor joy
## 7634 7634 liquor negative
## 7635 7635 liquor sadness
## 7636 7636 listless negative
## 7637 7637 listless sadness
## 7638 7638 lithe positive
## 7639 7639 litigant negative
## 7640 7640 litigate anger
## 7641 7641 litigate anticipation
## 7642 7642 litigate disgust
## 7643 7643 litigate fear
## 7644 7644 litigate negative
## 7645 7645 litigate sadness
## 7646 7646 litigation negative
## 7647 7647 litigious anger
## 7648 7648 litigious disgust
## 7649 7649 litigious negative
## 7650 7650 litter negative
## 7651 7651 livid anger
## 7652 7652 livid disgust
## 7653 7653 livid negative
## 7654 7654 loaf negative
## 7655 7655 loafer negative
## 7656 7656 loath anger
## 7657 7657 loath negative
## 7658 7658 loathe anger
## 7659 7659 loathe disgust
## 7660 7660 loathe negative
## 7661 7661 loathing disgust
## 7662 7662 loathing negative
## 7663 7663 loathsome anger
## 7664 7664 loathsome disgust
## 7665 7665 loathsome negative
## 7666 7666 lobbyist negative
## 7667 7667 localize anticipation
## 7668 7668 lockup fear
## 7669 7669 lockup negative
## 7670 7670 lockup sadness
## 7671 7671 locust fear
## 7672 7672 locust negative
## 7673 7673 lodging trust
## 7674 7674 lofty negative
## 7675 7675 logical positive
## 7676 7676 lone sadness
## 7677 7677 loneliness fear
## 7678 7678 loneliness negative
## 7679 7679 loneliness sadness
## 7680 7680 lonely anger
## 7681 7681 lonely disgust
## 7682 7682 lonely fear
## 7683 7683 lonely negative
## 7684 7684 lonely sadness
## 7685 7685 lonesome negative
## 7686 7686 lonesome sadness
## 7687 7687 long anticipation
## 7688 7688 longevity positive
## 7689 7689 longing anticipation
## 7690 7690 longing sadness
## 7691 7691 loo disgust
## 7692 7692 loo negative
## 7693 7693 loom anticipation
## 7694 7694 loom fear
## 7695 7695 loom negative
## 7696 7696 loon disgust
## 7697 7697 loon negative
## 7698 7698 loony negative
## 7699 7699 loot negative
## 7700 7700 lord disgust
## 7701 7701 lord negative
## 7702 7702 lord positive
## 7703 7703 lord trust
## 7704 7704 lordship positive
## 7705 7705 lose anger
## 7706 7706 lose disgust
## 7707 7707 lose fear
## 7708 7708 lose negative
## 7709 7709 lose sadness
## 7710 7710 lose surprise
## 7711 7711 losing anger
## 7712 7712 losing negative
## 7713 7713 losing sadness
## 7714 7714 loss anger
## 7715 7715 loss fear
## 7716 7716 loss negative
## 7717 7717 loss sadness
## 7718 7718 lost negative
## 7719 7719 lost sadness
## 7720 7720 lottery anticipation
## 7721 7721 loudness anger
## 7722 7722 loudness negative
## 7723 7723 lounge negative
## 7724 7724 louse disgust
## 7725 7725 louse negative
## 7726 7726 lovable joy
## 7727 7727 lovable positive
## 7728 7728 lovable trust
## 7729 7729 love joy
## 7730 7730 love positive
## 7731 7731 lovely anticipation
## 7732 7732 lovely joy
## 7733 7733 lovely positive
## 7734 7734 lovely sadness
## 7735 7735 lovely surprise
## 7736 7736 lovely trust
## 7737 7737 lovemaking joy
## 7738 7738 lovemaking positive
## 7739 7739 lovemaking trust
## 7740 7740 lover anticipation
## 7741 7741 lover joy
## 7742 7742 lover positive
## 7743 7743 lover trust
## 7744 7744 loving joy
## 7745 7745 loving positive
## 7746 7746 loving trust
## 7747 7747 lower negative
## 7748 7748 lower sadness
## 7749 7749 lowering negative
## 7750 7750 lowest negative
## 7751 7751 lowest sadness
## 7752 7752 lowlands negative
## 7753 7753 lowly negative
## 7754 7754 lowly sadness
## 7755 7755 loyal fear
## 7756 7756 loyal joy
## 7757 7757 loyal positive
## 7758 7758 loyal surprise
## 7759 7759 loyal trust
## 7760 7760 loyalty positive
## 7761 7761 loyalty trust
## 7762 7762 luck anticipation
## 7763 7763 luck joy
## 7764 7764 luck positive
## 7765 7765 luck surprise
## 7766 7766 lucky joy
## 7767 7767 lucky positive
## 7768 7768 lucky surprise
## 7769 7769 ludicrous negative
## 7770 7770 lull anticipation
## 7771 7771 lumbering negative
## 7772 7772 lump negative
## 7773 7773 lumpy disgust
## 7774 7774 lumpy negative
## 7775 7775 lunacy anger
## 7776 7776 lunacy disgust
## 7777 7777 lunacy fear
## 7778 7778 lunacy negative
## 7779 7779 lunacy sadness
## 7780 7780 lunatic anger
## 7781 7781 lunatic disgust
## 7782 7782 lunatic fear
## 7783 7783 lunatic negative
## 7784 7784 lunge surprise
## 7785 7785 lurch negative
## 7786 7786 lure negative
## 7787 7787 lurid disgust
## 7788 7788 lurid negative
## 7789 7789 lurk negative
## 7790 7790 lurking negative
## 7791 7791 luscious anticipation
## 7792 7792 luscious joy
## 7793 7793 luscious positive
## 7794 7794 lush disgust
## 7795 7795 lush negative
## 7796 7796 lush sadness
## 7797 7797 lust anticipation
## 7798 7798 lust negative
## 7799 7799 luster joy
## 7800 7800 luster positive
## 7801 7801 lustful negative
## 7802 7802 lustrous positive
## 7803 7803 lusty disgust
## 7804 7804 luxuriant positive
## 7805 7805 luxurious joy
## 7806 7806 luxurious positive
## 7807 7807 luxury joy
## 7808 7808 luxury positive
## 7809 7809 lying anger
## 7810 7810 lying disgust
## 7811 7811 lying negative
## 7812 7812 lynch anger
## 7813 7813 lynch disgust
## 7814 7814 lynch fear
## 7815 7815 lynch negative
## 7816 7816 lynch sadness
## 7817 7817 lyre joy
## 7818 7818 lyre positive
## 7819 7819 lyrical joy
## 7820 7820 lyrical positive
## 7821 7821 mace fear
## 7822 7822 mace negative
## 7823 7823 machine trust
## 7824 7824 mad anger
## 7825 7825 mad disgust
## 7826 7826 mad fear
## 7827 7827 mad negative
## 7828 7828 mad sadness
## 7829 7829 madden anger
## 7830 7830 madden fear
## 7831 7831 madden negative
## 7832 7832 madman anger
## 7833 7833 madman fear
## 7834 7834 madman negative
## 7835 7835 madness anger
## 7836 7836 madness fear
## 7837 7837 madness negative
## 7838 7838 mafia fear
## 7839 7839 mafia negative
## 7840 7840 mage fear
## 7841 7841 maggot disgust
## 7842 7842 maggot negative
## 7843 7843 magical anticipation
## 7844 7844 magical joy
## 7845 7845 magical positive
## 7846 7846 magical surprise
## 7847 7847 magician surprise
## 7848 7848 magnet positive
## 7849 7849 magnet trust
## 7850 7850 magnetism positive
## 7851 7851 magnetite positive
## 7852 7852 magnificence anticipation
## 7853 7853 magnificence joy
## 7854 7854 magnificence positive
## 7855 7855 magnificence trust
## 7856 7856 magnificent anticipation
## 7857 7857 magnificent joy
## 7858 7858 magnificent positive
## 7859 7859 magnificent surprise
## 7860 7860 magnificent trust
## 7861 7861 maiden positive
## 7862 7862 mail anticipation
## 7863 7863 main positive
## 7864 7864 mainstay positive
## 7865 7865 mainstay trust
## 7866 7866 maintenance trust
## 7867 7867 majestic anticipation
## 7868 7868 majestic joy
## 7869 7869 majestic positive
## 7870 7870 majestic surprise
## 7871 7871 majestic trust
## 7872 7872 majesty positive
## 7873 7873 majesty trust
## 7874 7874 major positive
## 7875 7875 majority joy
## 7876 7876 majority positive
## 7877 7877 majority trust
## 7878 7878 makeshift negative
## 7879 7879 malady negative
## 7880 7880 malaise negative
## 7881 7881 malaise sadness
## 7882 7882 malaria disgust
## 7883 7883 malaria fear
## 7884 7884 malaria negative
## 7885 7885 malaria sadness
## 7886 7886 malevolent anger
## 7887 7887 malevolent disgust
## 7888 7888 malevolent fear
## 7889 7889 malevolent negative
## 7890 7890 malevolent sadness
## 7891 7891 malfeasance disgust
## 7892 7892 malfeasance negative
## 7893 7893 malformation negative
## 7894 7894 malice anger
## 7895 7895 malice fear
## 7896 7896 malice negative
## 7897 7897 malicious anger
## 7898 7898 malicious disgust
## 7899 7899 malicious fear
## 7900 7900 malicious negative
## 7901 7901 malicious sadness
## 7902 7902 malign anger
## 7903 7903 malign disgust
## 7904 7904 malign negative
## 7905 7905 malignancy fear
## 7906 7906 malignancy negative
## 7907 7907 malignancy sadness
## 7908 7908 malignant anger
## 7909 7909 malignant fear
## 7910 7910 malignant negative
## 7911 7911 malpractice anger
## 7912 7912 malpractice negative
## 7913 7913 mamma trust
## 7914 7914 manage positive
## 7915 7915 manage trust
## 7916 7916 management positive
## 7917 7917 management trust
## 7918 7918 mandamus fear
## 7919 7919 mandamus negative
## 7920 7920 mandarin positive
## 7921 7921 mandarin trust
## 7922 7922 mange disgust
## 7923 7923 mange fear
## 7924 7924 mange negative
## 7925 7925 mangle anger
## 7926 7926 mangle disgust
## 7927 7927 mangle fear
## 7928 7928 mangle negative
## 7929 7929 mangle sadness
## 7930 7930 manhood positive
## 7931 7931 mania negative
## 7932 7932 maniac anger
## 7933 7933 maniac fear
## 7934 7934 maniac negative
## 7935 7935 maniacal negative
## 7936 7936 manifestation fear
## 7937 7937 manifested positive
## 7938 7938 manipulate negative
## 7939 7939 manipulation anger
## 7940 7940 manipulation fear
## 7941 7941 manipulation negative
## 7942 7942 manly positive
## 7943 7943 manna positive
## 7944 7944 mannered positive
## 7945 7945 manners positive
## 7946 7946 manslaughter anger
## 7947 7947 manslaughter disgust
## 7948 7948 manslaughter fear
## 7949 7949 manslaughter negative
## 7950 7950 manslaughter sadness
## 7951 7951 manslaughter surprise
## 7952 7952 manual trust
## 7953 7953 manufacturer positive
## 7954 7954 manure disgust
## 7955 7955 manure negative
## 7956 7956 mar negative
## 7957 7957 march positive
## 7958 7958 margin negative
## 7959 7959 margin sadness
## 7960 7960 marine trust
## 7961 7961 marked positive
## 7962 7962 marketable positive
## 7963 7963 maroon negative
## 7964 7964 marquis positive
## 7965 7965 marriage anticipation
## 7966 7966 marriage joy
## 7967 7967 marriage positive
## 7968 7968 marriage trust
## 7969 7969 marrow joy
## 7970 7970 marrow positive
## 7971 7971 marrow trust
## 7972 7972 marry anticipation
## 7973 7973 marry fear
## 7974 7974 marry joy
## 7975 7975 marry positive
## 7976 7976 marry surprise
## 7977 7977 marry trust
## 7978 7978 marshal positive
## 7979 7979 marshal trust
## 7980 7980 martial anger
## 7981 7981 martingale negative
## 7982 7982 martyr fear
## 7983 7983 martyr negative
## 7984 7984 martyr sadness
## 7985 7985 martyrdom fear
## 7986 7986 martyrdom negative
## 7987 7987 martyrdom sadness
## 7988 7988 marvel positive
## 7989 7989 marvel surprise
## 7990 7990 marvelous joy
## 7991 7991 marvelous positive
## 7992 7992 marvelously joy
## 7993 7993 marvelously positive
## 7994 7994 masculine positive
## 7995 7995 masochism anger
## 7996 7996 masochism disgust
## 7997 7997 masochism fear
## 7998 7998 masochism negative
## 7999 7999 massacre anger
## 8000 8000 massacre disgust
## 8001 8001 massacre fear
## 8002 8002 massacre negative
## 8003 8003 massacre sadness
## 8004 8004 massage joy
## 8005 8005 massage positive
## 8006 8006 master positive
## 8007 8007 masterpiece joy
## 8008 8008 masterpiece positive
## 8009 8009 mastery anger
## 8010 8010 mastery joy
## 8011 8011 mastery positive
## 8012 8012 mastery trust
## 8013 8013 matchmaker anticipation
## 8014 8014 mate positive
## 8015 8015 mate trust
## 8016 8016 materialism negative
## 8017 8017 materialist disgust
## 8018 8018 materialist negative
## 8019 8019 maternal anticipation
## 8020 8020 maternal negative
## 8021 8021 maternal positive
## 8022 8022 mathematical trust
## 8023 8023 matrimony anticipation
## 8024 8024 matrimony joy
## 8025 8025 matrimony positive
## 8026 8026 matrimony trust
## 8027 8027 matron positive
## 8028 8028 matron trust
## 8029 8029 mausoleum sadness
## 8030 8030 maxim trust
## 8031 8031 maximum positive
## 8032 8032 mayor positive
## 8033 8033 meadow positive
## 8034 8034 meandering negative
## 8035 8035 meaningless negative
## 8036 8036 meaningless sadness
## 8037 8037 measles disgust
## 8038 8038 measles fear
## 8039 8039 measles negative
## 8040 8040 measles sadness
## 8041 8041 measure trust
## 8042 8042 measured positive
## 8043 8043 measured trust
## 8044 8044 medal anticipation
## 8045 8045 medal joy
## 8046 8046 medal positive
## 8047 8047 medal surprise
## 8048 8048 medal trust
## 8049 8049 meddle anger
## 8050 8050 meddle negative
## 8051 8051 mediate anticipation
## 8052 8052 mediate positive
## 8053 8053 mediate trust
## 8054 8054 mediation positive
## 8055 8055 mediator anticipation
## 8056 8056 mediator positive
## 8057 8057 mediator trust
## 8058 8058 medical anticipation
## 8059 8059 medical fear
## 8060 8060 medical positive
## 8061 8061 medical trust
## 8062 8062 mediocre negative
## 8063 8063 mediocrity negative
## 8064 8064 meditate anticipation
## 8065 8065 meditate joy
## 8066 8066 meditate positive
## 8067 8067 meditate trust
## 8068 8068 mediterranean positive
## 8069 8069 medley positive
## 8070 8070 meek sadness
## 8071 8071 melancholic negative
## 8072 8072 melancholic sadness
## 8073 8073 melancholy negative
## 8074 8074 melancholy sadness
## 8075 8075 melee fear
## 8076 8076 melee negative
## 8077 8077 melodrama anger
## 8078 8078 melodrama negative
## 8079 8079 melodrama sadness
## 8080 8080 meltdown negative
## 8081 8081 meltdown sadness
## 8082 8082 memento positive
## 8083 8083 memorable joy
## 8084 8084 memorable positive
## 8085 8085 memorable surprise
## 8086 8086 memorable trust
## 8087 8087 memorials sadness
## 8088 8088 menace anger
## 8089 8089 menace fear
## 8090 8090 menace negative
## 8091 8091 menacing anger
## 8092 8092 menacing fear
## 8093 8093 menacing negative
## 8094 8094 mending positive
## 8095 8095 menial negative
## 8096 8096 menses positive
## 8097 8097 mentor positive
## 8098 8098 mentor trust
## 8099 8099 mercenary fear
## 8100 8100 mercenary negative
## 8101 8101 merchant trust
## 8102 8102 merciful positive
## 8103 8103 merciless fear
## 8104 8104 merciless negative
## 8105 8105 mercy positive
## 8106 8106 merge anticipation
## 8107 8107 merge positive
## 8108 8108 merit positive
## 8109 8109 merit trust
## 8110 8110 meritorious joy
## 8111 8111 meritorious positive
## 8112 8112 meritorious trust
## 8113 8113 merriment joy
## 8114 8114 merriment positive
## 8115 8115 merriment surprise
## 8116 8116 merry joy
## 8117 8117 merry positive
## 8118 8118 mess disgust
## 8119 8119 mess negative
## 8120 8120 messenger trust
## 8121 8121 messy disgust
## 8122 8122 messy negative
## 8123 8123 metastasis negative
## 8124 8124 methanol negative
## 8125 8125 metropolitan positive
## 8126 8126 mettle positive
## 8127 8127 microscope trust
## 8128 8128 microscopy positive
## 8129 8129 midwife anticipation
## 8130 8130 midwife joy
## 8131 8131 midwife negative
## 8132 8132 midwife positive
## 8133 8133 midwife trust
## 8134 8134 midwifery positive
## 8135 8135 mighty anger
## 8136 8136 mighty fear
## 8137 8137 mighty joy
## 8138 8138 mighty positive
## 8139 8139 mighty trust
## 8140 8140 mildew disgust
## 8141 8141 mildew negative
## 8142 8142 military fear
## 8143 8143 militia anger
## 8144 8144 militia fear
## 8145 8145 militia negative
## 8146 8146 militia sadness
## 8147 8147 mill anticipation
## 8148 8148 mime positive
## 8149 8149 mimicry negative
## 8150 8150 mimicry surprise
## 8151 8151 mindful positive
## 8152 8152 mindfulness positive
## 8153 8153 minimize negative
## 8154 8154 minimum negative
## 8155 8155 ministry joy
## 8156 8156 ministry positive
## 8157 8157 ministry trust
## 8158 8158 minority negative
## 8159 8159 miracle anticipation
## 8160 8160 miracle joy
## 8161 8161 miracle positive
## 8162 8162 miracle surprise
## 8163 8163 miracle trust
## 8164 8164 miraculous joy
## 8165 8165 miraculous positive
## 8166 8166 miraculous surprise
## 8167 8167 mire disgust
## 8168 8168 mire negative
## 8169 8169 mirth joy
## 8170 8170 mirth positive
## 8171 8171 misbehavior anger
## 8172 8172 misbehavior disgust
## 8173 8173 misbehavior negative
## 8174 8174 misbehavior surprise
## 8175 8175 miscarriage fear
## 8176 8176 miscarriage negative
## 8177 8177 miscarriage sadness
## 8178 8178 mischief negative
## 8179 8179 mischievous negative
## 8180 8180 misconception anger
## 8181 8181 misconception disgust
## 8182 8182 misconception fear
## 8183 8183 misconception negative
## 8184 8184 misconduct disgust
## 8185 8185 misconduct negative
## 8186 8186 miserable anger
## 8187 8187 miserable disgust
## 8188 8188 miserable negative
## 8189 8189 miserable sadness
## 8190 8190 miserably negative
## 8191 8191 miserably sadness
## 8192 8192 misery anger
## 8193 8193 misery disgust
## 8194 8194 misery fear
## 8195 8195 misery negative
## 8196 8196 misery sadness
## 8197 8197 misfortune fear
## 8198 8198 misfortune negative
## 8199 8199 misfortune sadness
## 8200 8200 misguided disgust
## 8201 8201 misguided negative
## 8202 8202 mishap disgust
## 8203 8203 mishap fear
## 8204 8204 mishap negative
## 8205 8205 mishap sadness
## 8206 8206 mishap surprise
## 8207 8207 misinterpretation negative
## 8208 8208 mislead anger
## 8209 8209 mislead fear
## 8210 8210 mislead negative
## 8211 8211 mislead trust
## 8212 8212 misleading anger
## 8213 8213 misleading disgust
## 8214 8214 misleading negative
## 8215 8215 mismanagement negative
## 8216 8216 mismatch negative
## 8217 8217 misnomer negative
## 8218 8218 misplace anger
## 8219 8219 misplace disgust
## 8220 8220 misplace negative
## 8221 8221 misplaced negative
## 8222 8222 misrepresent negative
## 8223 8223 misrepresentation negative
## 8224 8224 misrepresentation sadness
## 8225 8225 misrepresented anger
## 8226 8226 misrepresented negative
## 8227 8227 missile fear
## 8228 8228 missing fear
## 8229 8229 missing negative
## 8230 8230 missing sadness
## 8231 8231 missionary positive
## 8232 8232 misstatement anger
## 8233 8233 misstatement disgust
## 8234 8234 misstatement negative
## 8235 8235 mistake negative
## 8236 8236 mistake sadness
## 8237 8237 mistaken fear
## 8238 8238 mistaken negative
## 8239 8239 mistress anger
## 8240 8240 mistress disgust
## 8241 8241 mistress negative
## 8242 8242 mistrust disgust
## 8243 8243 mistrust fear
## 8244 8244 mistrust negative
## 8245 8245 misunderstand negative
## 8246 8246 misunderstanding anger
## 8247 8247 misunderstanding negative
## 8248 8248 misunderstanding sadness
## 8249 8249 misuse negative
## 8250 8250 mite disgust
## 8251 8251 mite negative
## 8252 8252 moan fear
## 8253 8253 moan sadness
## 8254 8254 moat trust
## 8255 8255 mob anger
## 8256 8256 mob fear
## 8257 8257 mob negative
## 8258 8258 mobile anticipation
## 8259 8259 mockery disgust
## 8260 8260 mockery negative
## 8261 8261 mocking anger
## 8262 8262 mocking disgust
## 8263 8263 mocking negative
## 8264 8264 mocking sadness
## 8265 8265 model positive
## 8266 8266 moderate positive
## 8267 8267 moderator positive
## 8268 8268 moderator trust
## 8269 8269 modest positive
## 8270 8270 modest trust
## 8271 8271 modesty positive
## 8272 8272 modify surprise
## 8273 8273 molestation anger
## 8274 8274 molestation disgust
## 8275 8275 molestation fear
## 8276 8276 molestation negative
## 8277 8277 molestation sadness
## 8278 8278 momentum anticipation
## 8279 8279 momentum positive
## 8280 8280 monetary anticipation
## 8281 8281 monetary positive
## 8282 8282 money anger
## 8283 8283 money anticipation
## 8284 8284 money joy
## 8285 8285 money positive
## 8286 8286 money surprise
## 8287 8287 money trust
## 8288 8288 monk positive
## 8289 8289 monk trust
## 8290 8290 monochrome disgust
## 8291 8291 monochrome negative
## 8292 8292 monogamy trust
## 8293 8293 monopolist negative
## 8294 8294 monsoon negative
## 8295 8295 monsoon sadness
## 8296 8296 monster fear
## 8297 8297 monster negative
## 8298 8298 monstrosity anger
## 8299 8299 monstrosity disgust
## 8300 8300 monstrosity fear
## 8301 8301 monstrosity negative
## 8302 8302 monstrosity surprise
## 8303 8303 monument positive
## 8304 8304 moody anger
## 8305 8305 moody negative
## 8306 8306 moody sadness
## 8307 8307 moorings trust
## 8308 8308 moot negative
## 8309 8309 moral anger
## 8310 8310 moral positive
## 8311 8311 moral trust
## 8312 8312 morality positive
## 8313 8313 morality trust
## 8314 8314 morals anger
## 8315 8315 morals anticipation
## 8316 8316 morals disgust
## 8317 8317 morals joy
## 8318 8318 morals positive
## 8319 8319 morals surprise
## 8320 8320 morals trust
## 8321 8321 morbid negative
## 8322 8322 morbid sadness
## 8323 8323 morbidity anger
## 8324 8324 morbidity disgust
## 8325 8325 morbidity fear
## 8326 8326 morbidity negative
## 8327 8327 morbidity sadness
## 8328 8328 morgue disgust
## 8329 8329 morgue fear
## 8330 8330 morgue negative
## 8331 8331 morgue sadness
## 8332 8332 moribund negative
## 8333 8333 moribund sadness
## 8334 8334 morn anticipation
## 8335 8335 moron negative
## 8336 8336 moronic negative
## 8337 8337 morrow anticipation
## 8338 8338 morsel negative
## 8339 8339 mortal negative
## 8340 8340 mortality anger
## 8341 8341 mortality fear
## 8342 8342 mortality negative
## 8343 8343 mortality sadness
## 8344 8344 mortar positive
## 8345 8345 mortgage fear
## 8346 8346 mortgagee trust
## 8347 8347 mortgagor fear
## 8348 8348 mortification anticipation
## 8349 8349 mortification disgust
## 8350 8350 mortification fear
## 8351 8351 mortification negative
## 8352 8352 mortification sadness
## 8353 8353 mortuary fear
## 8354 8354 mortuary negative
## 8355 8355 mortuary sadness
## 8356 8356 mosquito anger
## 8357 8357 mosquito disgust
## 8358 8358 mosquito negative
## 8359 8359 mother anticipation
## 8360 8360 mother joy
## 8361 8361 mother negative
## 8362 8362 mother positive
## 8363 8363 mother sadness
## 8364 8364 mother trust
## 8365 8365 motherhood joy
## 8366 8366 motherhood positive
## 8367 8367 motherhood trust
## 8368 8368 motion anticipation
## 8369 8369 motive positive
## 8370 8370 mountain anticipation
## 8371 8371 mourn negative
## 8372 8372 mourn sadness
## 8373 8373 mournful anger
## 8374 8374 mournful fear
## 8375 8375 mournful negative
## 8376 8376 mournful sadness
## 8377 8377 mourning negative
## 8378 8378 mourning sadness
## 8379 8379 mouth surprise
## 8380 8380 mouthful disgust
## 8381 8381 movable positive
## 8382 8382 mover positive
## 8383 8383 muck disgust
## 8384 8384 muck negative
## 8385 8385 mucous disgust
## 8386 8386 mucus disgust
## 8387 8387 mud negative
## 8388 8388 muddle negative
## 8389 8389 muddled negative
## 8390 8390 muddy disgust
## 8391 8391 muddy negative
## 8392 8392 muff anger
## 8393 8393 muff disgust
## 8394 8394 muff negative
## 8395 8395 mug anger
## 8396 8396 mug fear
## 8397 8397 mug negative
## 8398 8398 mug positive
## 8399 8399 mug sadness
## 8400 8400 mule anger
## 8401 8401 mule negative
## 8402 8402 mule trust
## 8403 8403 mum fear
## 8404 8404 mum negative
## 8405 8405 mumble negative
## 8406 8406 mumps negative
## 8407 8407 murder anger
## 8408 8408 murder disgust
## 8409 8409 murder fear
## 8410 8410 murder negative
## 8411 8411 murder sadness
## 8412 8412 murder surprise
## 8413 8413 murderer anger
## 8414 8414 murderer disgust
## 8415 8415 murderer fear
## 8416 8416 murderer negative
## 8417 8417 murderer sadness
## 8418 8418 murderous anger
## 8419 8419 murderous disgust
## 8420 8420 murderous fear
## 8421 8421 murderous negative
## 8422 8422 murderous sadness
## 8423 8423 murderous surprise
## 8424 8424 murky disgust
## 8425 8425 murky negative
## 8426 8426 murky sadness
## 8427 8427 muscular positive
## 8428 8428 music joy
## 8429 8429 music positive
## 8430 8430 music sadness
## 8431 8431 musical anger
## 8432 8432 musical anticipation
## 8433 8433 musical joy
## 8434 8434 musical positive
## 8435 8435 musical sadness
## 8436 8436 musical surprise
## 8437 8437 musical trust
## 8438 8438 musket fear
## 8439 8439 muss negative
## 8440 8440 musty disgust
## 8441 8441 musty negative
## 8442 8442 mutable anticipation
## 8443 8443 mutable positive
## 8444 8444 mutant negative
## 8445 8445 mutilated disgust
## 8446 8446 mutilated negative
## 8447 8447 mutilation anger
## 8448 8448 mutilation disgust
## 8449 8449 mutilation fear
## 8450 8450 mutilation negative
## 8451 8451 mutilation sadness
## 8452 8452 mutiny anger
## 8453 8453 mutiny disgust
## 8454 8454 mutiny fear
## 8455 8455 mutiny negative
## 8456 8456 mutiny surprise
## 8457 8457 mutter anger
## 8458 8458 mutter negative
## 8459 8459 mutual positive
## 8460 8460 muzzle fear
## 8461 8461 muzzle negative
## 8462 8462 myopia anger
## 8463 8463 myopia negative
## 8464 8464 myopia sadness
## 8465 8465 mysterious anticipation
## 8466 8466 mysterious fear
## 8467 8467 mysterious surprise
## 8468 8468 mystery anticipation
## 8469 8469 mystery surprise
## 8470 8470 mystic surprise
## 8471 8471 nab negative
## 8472 8472 nab surprise
## 8473 8473 nadir negative
## 8474 8474 nag anger
## 8475 8475 nag negative
## 8476 8476 naive negative
## 8477 8477 nameless disgust
## 8478 8478 nameless negative
## 8479 8479 nap joy
## 8480 8480 nap positive
## 8481 8481 napkin sadness
## 8482 8482 nappy disgust
## 8483 8483 nappy negative
## 8484 8484 narcotic negative
## 8485 8485 nascent anticipation
## 8486 8486 nasty anger
## 8487 8487 nasty disgust
## 8488 8488 nasty fear
## 8489 8489 nasty negative
## 8490 8490 nasty sadness
## 8491 8491 nation trust
## 8492 8492 naturalist positive
## 8493 8493 naughty negative
## 8494 8494 nausea disgust
## 8495 8495 nausea negative
## 8496 8496 nauseous disgust
## 8497 8497 nauseous negative
## 8498 8498 nauseous sadness
## 8499 8499 navigable anticipation
## 8500 8500 navigable positive
## 8501 8501 navigator anticipation
## 8502 8502 navigator trust
## 8503 8503 nay negative
## 8504 8504 neatly positive
## 8505 8505 necessity sadness
## 8506 8506 nectar positive
## 8507 8507 needful negative
## 8508 8508 needle positive
## 8509 8509 needy negative
## 8510 8510 nefarious disgust
## 8511 8511 nefarious fear
## 8512 8512 nefarious negative
## 8513 8513 nefarious sadness
## 8514 8514 nefarious surprise
## 8515 8515 negation anger
## 8516 8516 negation negative
## 8517 8517 negative negative
## 8518 8518 negative sadness
## 8519 8519 neglect negative
## 8520 8520 neglected anger
## 8521 8521 neglected disgust
## 8522 8522 neglected negative
## 8523 8523 neglected sadness
## 8524 8524 neglecting negative
## 8525 8525 negligence negative
## 8526 8526 negligent negative
## 8527 8527 negligently negative
## 8528 8528 negotiate positive
## 8529 8529 negotiate trust
## 8530 8530 negotiator positive
## 8531 8531 negro negative
## 8532 8532 negro sadness
## 8533 8533 neighbor anticipation
## 8534 8534 neighbor positive
## 8535 8535 neighbor trust
## 8536 8536 neighborhood anticipation
## 8537 8537 nepotism anger
## 8538 8538 nepotism disgust
## 8539 8539 nepotism negative
## 8540 8540 nepotism sadness
## 8541 8541 nerve positive
## 8542 8542 nervous anticipation
## 8543 8543 nervous fear
## 8544 8544 nervous negative
## 8545 8545 nervousness fear
## 8546 8546 nest trust
## 8547 8547 nestle positive
## 8548 8548 nestle trust
## 8549 8549 nether anger
## 8550 8550 nether fear
## 8551 8551 nether negative
## 8552 8552 nether sadness
## 8553 8553 nettle anger
## 8554 8554 nettle disgust
## 8555 8555 nettle negative
## 8556 8556 network anticipation
## 8557 8557 neuralgia fear
## 8558 8558 neuralgia negative
## 8559 8559 neuralgia sadness
## 8560 8560 neurosis fear
## 8561 8561 neurosis negative
## 8562 8562 neurosis sadness
## 8563 8563 neurotic disgust
## 8564 8564 neurotic fear
## 8565 8565 neurotic negative
## 8566 8566 neutral anticipation
## 8567 8567 neutral trust
## 8568 8568 neutrality trust
## 8569 8569 newcomer fear
## 8570 8570 newcomer surprise
## 8571 8571 nicotine disgust
## 8572 8572 nicotine negative
## 8573 8573 nightmare fear
## 8574 8574 nightmare negative
## 8575 8575 nihilism negative
## 8576 8576 nobility anticipation
## 8577 8577 nobility positive
## 8578 8578 nobility trust
## 8579 8579 noble positive
## 8580 8580 noble trust
## 8581 8581 nobleman positive
## 8582 8582 nobleman trust
## 8583 8583 noise negative
## 8584 8584 noisy anger
## 8585 8585 noisy negative
## 8586 8586 nomination positive
## 8587 8587 noncompliance anger
## 8588 8588 noncompliance anticipation
## 8589 8589 noncompliance fear
## 8590 8590 noncompliance negative
## 8591 8591 noncompliance sadness
## 8592 8592 nonsense negative
## 8593 8593 nonsensical negative
## 8594 8594 nonsensical sadness
## 8595 8595 noose negative
## 8596 8596 noose sadness
## 8597 8597 normality positive
## 8598 8598 nose disgust
## 8599 8599 notable joy
## 8600 8600 notable positive
## 8601 8601 notable trust
## 8602 8602 notables positive
## 8603 8603 notary trust
## 8604 8604 noted positive
## 8605 8605 nothingness negative
## 8606 8606 nothingness sadness
## 8607 8607 notification anticipation
## 8608 8608 notion positive
## 8609 8609 notoriety anger
## 8610 8610 notoriety disgust
## 8611 8611 notoriety fear
## 8612 8612 notoriety negative
## 8613 8613 notoriety positive
## 8614 8614 nourishment positive
## 8615 8615 noxious disgust
## 8616 8616 noxious fear
## 8617 8617 noxious negative
## 8618 8618 nuisance anger
## 8619 8619 nuisance negative
## 8620 8620 nul negative
## 8621 8621 nullify negative
## 8622 8622 nullify surprise
## 8623 8623 numb negative
## 8624 8624 numbers positive
## 8625 8625 numbness negative
## 8626 8626 numbness sadness
## 8627 8627 nun negative
## 8628 8628 nun trust
## 8629 8629 nurse positive
## 8630 8630 nurse trust
## 8631 8631 nursery joy
## 8632 8632 nursery positive
## 8633 8633 nursery trust
## 8634 8634 nurture anger
## 8635 8635 nurture anticipation
## 8636 8636 nurture disgust
## 8637 8637 nurture fear
## 8638 8638 nurture joy
## 8639 8639 nurture positive
## 8640 8640 nurture trust
## 8641 8641 nutritious positive
## 8642 8642 nutritious sadness
## 8643 8643 oaf negative
## 8644 8644 oak positive
## 8645 8645 oasis anticipation
## 8646 8646 oasis joy
## 8647 8647 oasis positive
## 8648 8648 oasis trust
## 8649 8649 oath positive
## 8650 8650 oath trust
## 8651 8651 obedience positive
## 8652 8652 obedience trust
## 8653 8653 obese disgust
## 8654 8654 obese negative
## 8655 8655 obesity disgust
## 8656 8656 obesity negative
## 8657 8657 obesity sadness
## 8658 8658 obey fear
## 8659 8659 obey trust
## 8660 8660 obi disgust
## 8661 8661 obi fear
## 8662 8662 obi negative
## 8663 8663 obit negative
## 8664 8664 obit sadness
## 8665 8665 obit surprise
## 8666 8666 obituary negative
## 8667 8667 obituary sadness
## 8668 8668 objection anger
## 8669 8669 objection negative
## 8670 8670 objectionable negative
## 8671 8671 objective anticipation
## 8672 8672 objective positive
## 8673 8673 objective trust
## 8674 8674 oblige negative
## 8675 8675 oblige trust
## 8676 8676 obliging anger
## 8677 8677 obliging anticipation
## 8678 8678 obliging disgust
## 8679 8679 obliging fear
## 8680 8680 obliging joy
## 8681 8681 obliging positive
## 8682 8682 obliging surprise
## 8683 8683 obliging trust
## 8684 8684 obligor fear
## 8685 8685 obligor negative
## 8686 8686 obliterate anger
## 8687 8687 obliterate fear
## 8688 8688 obliterate negative
## 8689 8689 obliterate sadness
## 8690 8690 obliterated anger
## 8691 8691 obliterated fear
## 8692 8692 obliterated negative
## 8693 8693 obliteration fear
## 8694 8694 obliteration negative
## 8695 8695 obliteration sadness
## 8696 8696 oblivion anger
## 8697 8697 oblivion fear
## 8698 8698 oblivion negative
## 8699 8699 obnoxious anger
## 8700 8700 obnoxious disgust
## 8701 8701 obnoxious negative
## 8702 8702 obnoxious sadness
## 8703 8703 obscene disgust
## 8704 8704 obscene negative
## 8705 8705 obscenity anger
## 8706 8706 obscenity disgust
## 8707 8707 obscenity negative
## 8708 8708 obscurity negative
## 8709 8709 observant positive
## 8710 8710 obstacle anger
## 8711 8711 obstacle fear
## 8712 8712 obstacle negative
## 8713 8713 obstacle sadness
## 8714 8714 obstetrician trust
## 8715 8715 obstinate negative
## 8716 8716 obstruct anger
## 8717 8717 obstruct fear
## 8718 8718 obstruct negative
## 8719 8719 obstruction negative
## 8720 8720 obstructive anger
## 8721 8721 obstructive negative
## 8722 8722 obtainable joy
## 8723 8723 obtainable positive
## 8724 8724 obtuse negative
## 8725 8725 obvious positive
## 8726 8726 obvious trust
## 8727 8727 occasional surprise
## 8728 8728 occult disgust
## 8729 8729 occult fear
## 8730 8730 occult negative
## 8731 8731 occupant positive
## 8732 8732 occupant trust
## 8733 8733 occupation positive
## 8734 8734 occupy positive
## 8735 8735 octopus disgust
## 8736 8736 oddity disgust
## 8737 8737 oddity negative
## 8738 8738 oddity sadness
## 8739 8739 oddity surprise
## 8740 8740 odious anger
## 8741 8741 odious disgust
## 8742 8742 odious fear
## 8743 8743 odious negative
## 8744 8744 odor negative
## 8745 8745 offend anger
## 8746 8746 offend disgust
## 8747 8747 offend negative
## 8748 8748 offended anger
## 8749 8749 offended negative
## 8750 8750 offended sadness
## 8751 8751 offender anger
## 8752 8752 offender disgust
## 8753 8753 offender fear
## 8754 8754 offender negative
## 8755 8755 offender sadness
## 8756 8756 offense anger
## 8757 8757 offense disgust
## 8758 8758 offense fear
## 8759 8759 offense negative
## 8760 8760 offense sadness
## 8761 8761 offensive anger
## 8762 8762 offensive disgust
## 8763 8763 offensive negative
## 8764 8764 offer positive
## 8765 8765 offering trust
## 8766 8766 offhand negative
## 8767 8767 officer positive
## 8768 8768 officer trust
## 8769 8769 official trust
## 8770 8770 offing negative
## 8771 8771 offset anticipation
## 8772 8772 offset positive
## 8773 8773 offshoot positive
## 8774 8774 ogre disgust
## 8775 8775 ogre fear
## 8776 8776 ogre negative
## 8777 8777 olfactory anticipation
## 8778 8778 olfactory negative
## 8779 8779 oligarchy negative
## 8780 8780 omen anticipation
## 8781 8781 omen fear
## 8782 8782 omen negative
## 8783 8783 ominous anticipation
## 8784 8784 ominous fear
## 8785 8785 ominous negative
## 8786 8786 omit negative
## 8787 8787 omnipotence fear
## 8788 8788 omnipotence negative
## 8789 8789 omnipotence positive
## 8790 8790 omniscient positive
## 8791 8791 omniscient trust
## 8792 8792 onerous anger
## 8793 8793 onerous negative
## 8794 8794 onerous sadness
## 8795 8795 ongoing anticipation
## 8796 8796 onset anticipation
## 8797 8797 onus negative
## 8798 8798 ooze disgust
## 8799 8799 ooze negative
## 8800 8800 opaque negative
## 8801 8801 openness positive
## 8802 8802 opera anger
## 8803 8803 opera anticipation
## 8804 8804 opera fear
## 8805 8805 opera joy
## 8806 8806 opera positive
## 8807 8807 opera sadness
## 8808 8808 opera surprise
## 8809 8809 opera trust
## 8810 8810 operatic negative
## 8811 8811 operation fear
## 8812 8812 operation trust
## 8813 8813 opiate negative
## 8814 8814 opinionated anger
## 8815 8815 opinionated negative
## 8816 8816 opium anger
## 8817 8817 opium disgust
## 8818 8818 opium fear
## 8819 8819 opium negative
## 8820 8820 opium sadness
## 8821 8821 opponent anger
## 8822 8822 opponent anticipation
## 8823 8823 opponent disgust
## 8824 8824 opponent fear
## 8825 8825 opponent negative
## 8826 8826 opportune joy
## 8827 8827 opportune positive
## 8828 8828 opportunity anticipation
## 8829 8829 opportunity positive
## 8830 8830 oppose negative
## 8831 8831 opposed anger
## 8832 8832 opposed fear
## 8833 8833 opposed negative
## 8834 8834 opposition anger
## 8835 8835 opposition negative
## 8836 8836 oppress anger
## 8837 8837 oppress disgust
## 8838 8838 oppress fear
## 8839 8839 oppress negative
## 8840 8840 oppress sadness
## 8841 8841 oppression anger
## 8842 8842 oppression disgust
## 8843 8843 oppression fear
## 8844 8844 oppression negative
## 8845 8845 oppression sadness
## 8846 8846 oppressive anger
## 8847 8847 oppressive disgust
## 8848 8848 oppressive fear
## 8849 8849 oppressive negative
## 8850 8850 oppressive sadness
## 8851 8851 oppressor anger
## 8852 8852 oppressor fear
## 8853 8853 oppressor negative
## 8854 8854 oppressor sadness
## 8855 8855 optimism anticipation
## 8856 8856 optimism joy
## 8857 8857 optimism positive
## 8858 8858 optimism surprise
## 8859 8859 optimism trust
## 8860 8860 optimist positive
## 8861 8861 optimist trust
## 8862 8862 option positive
## 8863 8863 optional positive
## 8864 8864 oracle anticipation
## 8865 8865 oracle positive
## 8866 8866 oracle trust
## 8867 8867 oration positive
## 8868 8868 orc anger
## 8869 8869 orc disgust
## 8870 8870 orc fear
## 8871 8871 orc negative
## 8872 8872 orchestra anger
## 8873 8873 orchestra joy
## 8874 8874 orchestra positive
## 8875 8875 orchestra sadness
## 8876 8876 orchestra trust
## 8877 8877 ordained positive
## 8878 8878 ordained trust
## 8879 8879 ordeal anger
## 8880 8880 ordeal negative
## 8881 8881 ordeal surprise
## 8882 8882 orderly positive
## 8883 8883 ordinance trust
## 8884 8884 ordination anticipation
## 8885 8885 ordination joy
## 8886 8886 ordination positive
## 8887 8887 ordination trust
## 8888 8888 ordnance fear
## 8889 8889 ordnance negative
## 8890 8890 organ anticipation
## 8891 8891 organ joy
## 8892 8892 organic positive
## 8893 8893 organization anticipation
## 8894 8894 organization joy
## 8895 8895 organization positive
## 8896 8896 organization surprise
## 8897 8897 organization trust
## 8898 8898 organize positive
## 8899 8899 organized positive
## 8900 8900 orgasm anticipation
## 8901 8901 orgasm joy
## 8902 8902 orgasm positive
## 8903 8903 originality positive
## 8904 8904 originality surprise
## 8905 8905 ornamented positive
## 8906 8906 ornate positive
## 8907 8907 orphan fear
## 8908 8908 orphan negative
## 8909 8909 orphan sadness
## 8910 8910 orthodoxy trust
## 8911 8911 ostensibly negative
## 8912 8912 oust anger
## 8913 8913 oust negative
## 8914 8914 oust sadness
## 8915 8915 outburst anger
## 8916 8916 outburst fear
## 8917 8917 outburst joy
## 8918 8918 outburst negative
## 8919 8919 outburst positive
## 8920 8920 outburst sadness
## 8921 8921 outburst surprise
## 8922 8922 outcast disgust
## 8923 8923 outcast fear
## 8924 8924 outcast negative
## 8925 8925 outcast sadness
## 8926 8926 outcome positive
## 8927 8927 outcry anger
## 8928 8928 outcry fear
## 8929 8929 outcry negative
## 8930 8930 outcry surprise
## 8931 8931 outdo anticipation
## 8932 8932 outdo positive
## 8933 8933 outhouse disgust
## 8934 8934 outhouse negative
## 8935 8935 outlandish negative
## 8936 8936 outlaw negative
## 8937 8937 outpost fear
## 8938 8938 outrage anger
## 8939 8939 outrage disgust
## 8940 8940 outrage negative
## 8941 8941 outrageous surprise
## 8942 8942 outsider fear
## 8943 8943 outstanding joy
## 8944 8944 outstanding negative
## 8945 8945 outstanding positive
## 8946 8946 outward positive
## 8947 8947 ovation negative
## 8948 8948 ovation sadness
## 8949 8949 overbearing anger
## 8950 8950 overbearing negative
## 8951 8951 overburden negative
## 8952 8952 overcast negative
## 8953 8953 overdo negative
## 8954 8954 overdose negative
## 8955 8955 overdue anticipation
## 8956 8956 overdue negative
## 8957 8957 overdue sadness
## 8958 8958 overdue surprise
## 8959 8959 overestimate surprise
## 8960 8960 overestimated negative
## 8961 8961 overflow negative
## 8962 8962 overgrown negative
## 8963 8963 overjoyed joy
## 8964 8964 overjoyed positive
## 8965 8965 overload negative
## 8966 8966 overload sadness
## 8967 8967 overpaid negative
## 8968 8968 overpower negative
## 8969 8969 overpowering anger
## 8970 8970 overpowering fear
## 8971 8971 overpowering negative
## 8972 8972 overpriced anger
## 8973 8973 overpriced disgust
## 8974 8974 overpriced negative
## 8975 8975 overpriced sadness
## 8976 8976 oversight negative
## 8977 8977 overt fear
## 8978 8978 overthrow anticipation
## 8979 8979 overthrow fear
## 8980 8980 overthrow negative
## 8981 8981 overture anticipation
## 8982 8982 overturn negative
## 8983 8983 overwhelm negative
## 8984 8984 overwhelmed negative
## 8985 8985 overwhelmed sadness
## 8986 8986 overwhelming positive
## 8987 8987 owing anger
## 8988 8988 owing anticipation
## 8989 8989 owing disgust
## 8990 8990 owing fear
## 8991 8991 owing negative
## 8992 8992 owing sadness
## 8993 8993 owing trust
## 8994 8994 ownership positive
## 8995 8995 oxidation negative
## 8996 8996 pacific positive
## 8997 8997 pact trust
## 8998 8998 pad positive
## 8999 8999 padding positive
## 9000 9000 paddle anticipation
## 9001 9001 paddle positive
## 9002 9002 pain fear
## 9003 9003 pain negative
## 9004 9004 pain sadness
## 9005 9005 pained fear
## 9006 9006 pained negative
## 9007 9007 pained sadness
## 9008 9008 painful anger
## 9009 9009 painful disgust
## 9010 9010 painful fear
## 9011 9011 painful negative
## 9012 9012 painful sadness
## 9013 9013 painfully negative
## 9014 9014 painfully sadness
## 9015 9015 pains negative
## 9016 9016 palatable positive
## 9017 9017 palliative positive
## 9018 9018 palpable surprise
## 9019 9019 palsy disgust
## 9020 9020 palsy fear
## 9021 9021 palsy negative
## 9022 9022 palsy sadness
## 9023 9023 panacea positive
## 9024 9024 panache positive
## 9025 9025 pandemic fear
## 9026 9026 pandemic negative
## 9027 9027 pandemic sadness
## 9028 9028 pang negative
## 9029 9029 pang surprise
## 9030 9030 panic fear
## 9031 9031 panic negative
## 9032 9032 panier positive
## 9033 9033 paprika positive
## 9034 9034 parachute fear
## 9035 9035 parade anticipation
## 9036 9036 parade fear
## 9037 9037 parade joy
## 9038 9038 parade negative
## 9039 9039 parade positive
## 9040 9040 parade surprise
## 9041 9041 paragon anticipation
## 9042 9042 paragon joy
## 9043 9043 paragon positive
## 9044 9044 paragon trust
## 9045 9045 paralysis anger
## 9046 9046 paralysis anticipation
## 9047 9047 paralysis fear
## 9048 9048 paralysis negative
## 9049 9049 paralysis sadness
## 9050 9050 paramount positive
## 9051 9051 paranoia fear
## 9052 9052 paranoia negative
## 9053 9053 paraphrase negative
## 9054 9054 paraphrase positive
## 9055 9055 parasite disgust
## 9056 9056 parasite fear
## 9057 9057 parasite negative
## 9058 9058 pardon positive
## 9059 9059 pare anger
## 9060 9060 pare anticipation
## 9061 9061 pare fear
## 9062 9062 pare negative
## 9063 9063 pare sadness
## 9064 9064 parentage positive
## 9065 9065 parietal positive
## 9066 9066 parietal trust
## 9067 9067 parish trust
## 9068 9068 parliament trust
## 9069 9069 parole anticipation
## 9070 9070 parrot disgust
## 9071 9071 parrot negative
## 9072 9072 parsimonious negative
## 9073 9073 partake positive
## 9074 9074 partake trust
## 9075 9075 participation positive
## 9076 9076 parting sadness
## 9077 9077 partisan negative
## 9078 9078 partner positive
## 9079 9079 partnership positive
## 9080 9080 partnership trust
## 9081 9081 passe negative
## 9082 9082 passenger anticipation
## 9083 9083 passion anticipation
## 9084 9084 passion joy
## 9085 9085 passion positive
## 9086 9086 passion trust
## 9087 9087 passionate anticipation
## 9088 9088 passionate joy
## 9089 9089 passionate positive
## 9090 9090 passionate trust
## 9091 9091 passive negative
## 9092 9092 passivity negative
## 9093 9093 pastime positive
## 9094 9094 pastor joy
## 9095 9095 pastor positive
## 9096 9096 pastor trust
## 9097 9097 pastry joy
## 9098 9098 pastry positive
## 9099 9099 pasture positive
## 9100 9100 patch negative
## 9101 9101 patent positive
## 9102 9102 pathetic disgust
## 9103 9103 pathetic negative
## 9104 9104 pathetic sadness
## 9105 9105 patience anticipation
## 9106 9106 patience positive
## 9107 9107 patience trust
## 9108 9108 patient anticipation
## 9109 9109 patient positive
## 9110 9110 patriarchal positive
## 9111 9111 patriarchal trust
## 9112 9112 patrol trust
## 9113 9113 patron positive
## 9114 9114 patron trust
## 9115 9115 patronage positive
## 9116 9116 patronage trust
## 9117 9117 patronizing negative
## 9118 9118 patter anger
## 9119 9119 patter negative
## 9120 9120 paucity anger
## 9121 9121 paucity disgust
## 9122 9122 paucity negative
## 9123 9123 paucity sadness
## 9124 9124 pauper negative
## 9125 9125 pauper sadness
## 9126 9126 pavement trust
## 9127 9127 pawn negative
## 9128 9128 pawn trust
## 9129 9129 pay anticipation
## 9130 9130 pay joy
## 9131 9131 pay positive
## 9132 9132 pay trust
## 9133 9133 payback anger
## 9134 9134 payback negative
## 9135 9135 payment negative
## 9136 9136 peace anticipation
## 9137 9137 peace joy
## 9138 9138 peace positive
## 9139 9139 peace trust
## 9140 9140 peaceable positive
## 9141 9141 peaceful anticipation
## 9142 9142 peaceful joy
## 9143 9143 peaceful positive
## 9144 9144 peaceful surprise
## 9145 9145 peaceful trust
## 9146 9146 peacock positive
## 9147 9147 peck positive
## 9148 9148 peculiarities negative
## 9149 9149 peculiarity positive
## 9150 9150 pedestrian negative
## 9151 9151 pedigree positive
## 9152 9152 pedigree trust
## 9153 9153 peerless positive
## 9154 9154 penal fear
## 9155 9155 penal negative
## 9156 9156 penal sadness
## 9157 9157 penalty anger
## 9158 9158 penalty fear
## 9159 9159 penalty negative
## 9160 9160 penalty sadness
## 9161 9161 penance fear
## 9162 9162 penance sadness
## 9163 9163 penchant positive
## 9164 9164 penetration anger
## 9165 9165 penetration fear
## 9166 9166 penetration negative
## 9167 9167 penitentiary anger
## 9168 9168 penitentiary negative
## 9169 9169 pensive sadness
## 9170 9170 perceive positive
## 9171 9171 perceive trust
## 9172 9172 perceptible positive
## 9173 9173 perchance surprise
## 9174 9174 perdition anger
## 9175 9175 perdition disgust
## 9176 9176 perdition fear
## 9177 9177 perdition negative
## 9178 9178 perdition sadness
## 9179 9179 perennial positive
## 9180 9180 perennial trust
## 9181 9181 perfect anticipation
## 9182 9182 perfect joy
## 9183 9183 perfect positive
## 9184 9184 perfect trust
## 9185 9185 perfection anticipation
## 9186 9186 perfection joy
## 9187 9187 perfection positive
## 9188 9188 perfection surprise
## 9189 9189 perfection trust
## 9190 9190 performer positive
## 9191 9191 peri surprise
## 9192 9192 peril anticipation
## 9193 9193 peril fear
## 9194 9194 peril negative
## 9195 9195 peril sadness
## 9196 9196 perilous anticipation
## 9197 9197 perilous fear
## 9198 9198 perilous negative
## 9199 9199 perilous sadness
## 9200 9200 periodicity trust
## 9201 9201 perish fear
## 9202 9202 perish negative
## 9203 9203 perish sadness
## 9204 9204 perishable negative
## 9205 9205 perished negative
## 9206 9206 perished sadness
## 9207 9207 perishing fear
## 9208 9208 perishing negative
## 9209 9209 perishing sadness
## 9210 9210 perjury fear
## 9211 9211 perjury negative
## 9212 9212 perjury surprise
## 9213 9213 perk positive
## 9214 9214 permission positive
## 9215 9215 pernicious anger
## 9216 9216 pernicious fear
## 9217 9217 pernicious negative
## 9218 9218 pernicious sadness
## 9219 9219 perpetrator anger
## 9220 9220 perpetrator disgust
## 9221 9221 perpetrator fear
## 9222 9222 perpetrator negative
## 9223 9223 perpetrator sadness
## 9224 9224 perpetuate anticipation
## 9225 9225 perpetuation negative
## 9226 9226 perpetuity anticipation
## 9227 9227 perpetuity positive
## 9228 9228 perpetuity trust
## 9229 9229 perplexed negative
## 9230 9230 perplexity negative
## 9231 9231 perplexity sadness
## 9232 9232 persecute anger
## 9233 9233 persecute fear
## 9234 9234 persecute negative
## 9235 9235 persecution anger
## 9236 9236 persecution disgust
## 9237 9237 persecution fear
## 9238 9238 persecution negative
## 9239 9239 persecution sadness
## 9240 9240 persistence positive
## 9241 9241 persistent positive
## 9242 9242 personable positive
## 9243 9243 personal trust
## 9244 9244 perspiration disgust
## 9245 9245 persuade trust
## 9246 9246 pertinent positive
## 9247 9247 pertinent trust
## 9248 9248 perturbation fear
## 9249 9249 perturbation negative
## 9250 9250 pertussis negative
## 9251 9251 perverse anger
## 9252 9252 perverse disgust
## 9253 9253 perverse fear
## 9254 9254 perverse negative
## 9255 9255 perversion anger
## 9256 9256 perversion disgust
## 9257 9257 perversion negative
## 9258 9258 perversion sadness
## 9259 9259 pervert anger
## 9260 9260 pervert disgust
## 9261 9261 pervert negative
## 9262 9262 perverted disgust
## 9263 9263 perverted negative
## 9264 9264 pessimism anger
## 9265 9265 pessimism fear
## 9266 9266 pessimism negative
## 9267 9267 pessimism sadness
## 9268 9268 pessimist fear
## 9269 9269 pessimist negative
## 9270 9270 pessimist sadness
## 9271 9271 pest anger
## 9272 9272 pest disgust
## 9273 9273 pest fear
## 9274 9274 pest negative
## 9275 9275 pestilence disgust
## 9276 9276 pestilence fear
## 9277 9277 pestilence negative
## 9278 9278 pet negative
## 9279 9279 petroleum disgust
## 9280 9280 petroleum negative
## 9281 9281 petroleum positive
## 9282 9282 petty negative
## 9283 9283 phalanx fear
## 9284 9284 phalanx trust
## 9285 9285 phantom fear
## 9286 9286 phantom negative
## 9287 9287 pharmaceutical positive
## 9288 9288 philanthropic trust
## 9289 9289 philanthropist positive
## 9290 9290 philanthropist trust
## 9291 9291 philanthropy positive
## 9292 9292 philosopher positive
## 9293 9293 philosopher trust
## 9294 9294 phlegm disgust
## 9295 9295 phlegm negative
## 9296 9296 phoenix positive
## 9297 9297 phonetic positive
## 9298 9298 phony anger
## 9299 9299 phony disgust
## 9300 9300 phony negative
## 9301 9301 physician positive
## 9302 9302 physician trust
## 9303 9303 physicist trust
## 9304 9304 physics positive
## 9305 9305 physiology positive
## 9306 9306 physique positive
## 9307 9307 pick positive
## 9308 9308 picket anger
## 9309 9309 picket anticipation
## 9310 9310 picket fear
## 9311 9311 picket negative
## 9312 9312 picketing anger
## 9313 9313 picketing negative
## 9314 9314 pickle negative
## 9315 9315 picnic anticipation
## 9316 9316 picnic joy
## 9317 9317 picnic positive
## 9318 9318 picnic surprise
## 9319 9319 picnic trust
## 9320 9320 picturesque joy
## 9321 9321 picturesque positive
## 9322 9322 piety positive
## 9323 9323 pig disgust
## 9324 9324 pig negative
## 9325 9325 pigeon negative
## 9326 9326 piles disgust
## 9327 9327 piles negative
## 9328 9328 pill positive
## 9329 9329 pill trust
## 9330 9330 pillage anger
## 9331 9331 pillage disgust
## 9332 9332 pillage fear
## 9333 9333 pillage negative
## 9334 9334 pillow positive
## 9335 9335 pilot positive
## 9336 9336 pilot trust
## 9337 9337 pimp negative
## 9338 9338 pimple disgust
## 9339 9339 pimple negative
## 9340 9340 pine negative
## 9341 9341 pine sadness
## 9342 9342 pinion fear
## 9343 9343 pinion negative
## 9344 9344 pinnacle positive
## 9345 9345 pioneer positive
## 9346 9346 pious disgust
## 9347 9347 pious positive
## 9348 9348 pious sadness
## 9349 9349 pious trust
## 9350 9350 pique anger
## 9351 9351 pique negative
## 9352 9352 piracy negative
## 9353 9353 pirate anger
## 9354 9354 pirate negative
## 9355 9355 pistol negative
## 9356 9356 pitfall anger
## 9357 9357 pitfall disgust
## 9358 9358 pitfall fear
## 9359 9359 pitfall negative
## 9360 9360 pitfall sadness
## 9361 9361 pitfall surprise
## 9362 9362 pith positive
## 9363 9363 pith trust
## 9364 9364 pity sadness
## 9365 9365 pivot positive
## 9366 9366 pivot trust
## 9367 9367 placard surprise
## 9368 9368 placid positive
## 9369 9369 plagiarism disgust
## 9370 9370 plagiarism negative
## 9371 9371 plague disgust
## 9372 9372 plague fear
## 9373 9373 plague negative
## 9374 9374 plague sadness
## 9375 9375 plaintiff negative
## 9376 9376 plaintive sadness
## 9377 9377 plan anticipation
## 9378 9378 planning anticipation
## 9379 9379 planning positive
## 9380 9380 planning trust
## 9381 9381 plated negative
## 9382 9382 player negative
## 9383 9383 playful anger
## 9384 9384 playful joy
## 9385 9385 playful positive
## 9386 9386 playful surprise
## 9387 9387 playful trust
## 9388 9388 playground anticipation
## 9389 9389 playground joy
## 9390 9390 playground positive
## 9391 9391 playground surprise
## 9392 9392 playground trust
## 9393 9393 playhouse joy
## 9394 9394 playhouse positive
## 9395 9395 plea anticipation
## 9396 9396 plea fear
## 9397 9397 plea negative
## 9398 9398 plea sadness
## 9399 9399 pleasant anticipation
## 9400 9400 pleasant joy
## 9401 9401 pleasant positive
## 9402 9402 pleasant surprise
## 9403 9403 pleasant trust
## 9404 9404 pleased joy
## 9405 9405 pleased positive
## 9406 9406 pleasurable joy
## 9407 9407 pleasurable positive
## 9408 9408 pledge joy
## 9409 9409 pledge positive
## 9410 9410 pledge trust
## 9411 9411 plenary positive
## 9412 9412 plentiful positive
## 9413 9413 plexus positive
## 9414 9414 plight anticipation
## 9415 9415 plight disgust
## 9416 9416 plight fear
## 9417 9417 plight negative
## 9418 9418 plight sadness
## 9419 9419 plodding negative
## 9420 9420 plum positive
## 9421 9421 plumb positive
## 9422 9422 plummet fear
## 9423 9423 plummet negative
## 9424 9424 plump anticipation
## 9425 9425 plunder anger
## 9426 9426 plunder disgust
## 9427 9427 plunder fear
## 9428 9428 plunder negative
## 9429 9429 plunder sadness
## 9430 9430 plunder surprise
## 9431 9431 plunge fear
## 9432 9432 plunge negative
## 9433 9433 plush positive
## 9434 9434 ply positive
## 9435 9435 pneumonia fear
## 9436 9436 pneumonia negative
## 9437 9437 poaching anger
## 9438 9438 poaching disgust
## 9439 9439 poaching fear
## 9440 9440 poaching negative
## 9441 9441 poaching sadness
## 9442 9442 pointedly positive
## 9443 9443 pointless negative
## 9444 9444 pointless sadness
## 9445 9445 poison anger
## 9446 9446 poison disgust
## 9447 9447 poison fear
## 9448 9448 poison negative
## 9449 9449 poison sadness
## 9450 9450 poisoned anger
## 9451 9451 poisoned disgust
## 9452 9452 poisoned fear
## 9453 9453 poisoned negative
## 9454 9454 poisoned sadness
## 9455 9455 poisoning disgust
## 9456 9456 poisoning negative
## 9457 9457 poisonous anger
## 9458 9458 poisonous disgust
## 9459 9459 poisonous fear
## 9460 9460 poisonous negative
## 9461 9461 poisonous sadness
## 9462 9462 poke anticipation
## 9463 9463 poke negative
## 9464 9464 polarity surprise
## 9465 9465 polemic anger
## 9466 9466 polemic disgust
## 9467 9467 polemic negative
## 9468 9468 police fear
## 9469 9469 police positive
## 9470 9470 police trust
## 9471 9471 policeman fear
## 9472 9472 policeman positive
## 9473 9473 policeman trust
## 9474 9474 policy trust
## 9475 9475 polio fear
## 9476 9476 polio negative
## 9477 9477 polio sadness
## 9478 9478 polish positive
## 9479 9479 polished positive
## 9480 9480 polite positive
## 9481 9481 politeness positive
## 9482 9482 politic disgust
## 9483 9483 politic positive
## 9484 9484 politics anger
## 9485 9485 poll trust
## 9486 9486 pollute disgust
## 9487 9487 pollute negative
## 9488 9488 pollution disgust
## 9489 9489 pollution negative
## 9490 9490 polygamy disgust
## 9491 9491 polygamy negative
## 9492 9492 pomp negative
## 9493 9493 pompous disgust
## 9494 9494 pompous negative
## 9495 9495 ponderous negative
## 9496 9496 pontiff trust
## 9497 9497 pool positive
## 9498 9498 poorly negative
## 9499 9499 pop negative
## 9500 9500 pop surprise
## 9501 9501 pope positive
## 9502 9502 popularity positive
## 9503 9503 popularized positive
## 9504 9504 population positive
## 9505 9505 porcupine negative
## 9506 9506 porn disgust
## 9507 9507 porn negative
## 9508 9508 porno negative
## 9509 9509 pornographic negative
## 9510 9510 pornography disgust
## 9511 9511 pornography negative
## 9512 9512 portable positive
## 9513 9513 pose negative
## 9514 9514 posse fear
## 9515 9515 possess anticipation
## 9516 9516 possess joy
## 9517 9517 possess positive
## 9518 9518 possess trust
## 9519 9519 possessed anger
## 9520 9520 possessed disgust
## 9521 9521 possessed fear
## 9522 9522 possessed negative
## 9523 9523 possession anger
## 9524 9524 possession disgust
## 9525 9525 possession fear
## 9526 9526 possession negative
## 9527 9527 possibility anticipation
## 9528 9528 posthumous negative
## 9529 9529 posthumous sadness
## 9530 9530 postponement negative
## 9531 9531 postponement surprise
## 9532 9532 potable positive
## 9533 9533 potency positive
## 9534 9534 pound anger
## 9535 9535 pound negative
## 9536 9536 poverty anger
## 9537 9537 poverty disgust
## 9538 9538 poverty fear
## 9539 9539 poverty negative
## 9540 9540 poverty sadness
## 9541 9541 pow anger
## 9542 9542 powerful anger
## 9543 9543 powerful anticipation
## 9544 9544 powerful disgust
## 9545 9545 powerful fear
## 9546 9546 powerful joy
## 9547 9547 powerful positive
## 9548 9548 powerful trust
## 9549 9549 powerfully fear
## 9550 9550 powerfully positive
## 9551 9551 powerless anger
## 9552 9552 powerless disgust
## 9553 9553 powerless fear
## 9554 9554 powerless negative
## 9555 9555 powerless sadness
## 9556 9556 practice positive
## 9557 9557 practiced joy
## 9558 9558 practiced positive
## 9559 9559 practiced surprise
## 9560 9560 practiced trust
## 9561 9561 practise anticipation
## 9562 9562 practise positive
## 9563 9563 praise joy
## 9564 9564 praise positive
## 9565 9565 praise trust
## 9566 9566 praised joy
## 9567 9567 praised positive
## 9568 9568 praiseworthy anticipation
## 9569 9569 praiseworthy joy
## 9570 9570 praiseworthy positive
## 9571 9571 praiseworthy surprise
## 9572 9572 praiseworthy trust
## 9573 9573 prank negative
## 9574 9574 prank surprise
## 9575 9575 pray anticipation
## 9576 9576 pray fear
## 9577 9577 pray joy
## 9578 9578 pray positive
## 9579 9579 pray surprise
## 9580 9580 pray trust
## 9581 9581 precarious anticipation
## 9582 9582 precarious fear
## 9583 9583 precarious negative
## 9584 9584 precarious sadness
## 9585 9585 precaution positive
## 9586 9586 precede positive
## 9587 9587 precedence positive
## 9588 9588 precedence trust
## 9589 9589 precinct negative
## 9590 9590 precious anticipation
## 9591 9591 precious joy
## 9592 9592 precious positive
## 9593 9593 precious surprise
## 9594 9594 precipice fear
## 9595 9595 precise positive
## 9596 9596 precision positive
## 9597 9597 preclude anger
## 9598 9598 precursor anticipation
## 9599 9599 predatory negative
## 9600 9600 predicament fear
## 9601 9601 predicament negative
## 9602 9602 predict anticipation
## 9603 9603 prediction anticipation
## 9604 9604 predilection anticipation
## 9605 9605 predilection positive
## 9606 9606 predispose anticipation
## 9607 9607 predominant positive
## 9608 9608 predominant trust
## 9609 9609 preeminent positive
## 9610 9610 prefer positive
## 9611 9611 prefer trust
## 9612 9612 pregnancy disgust
## 9613 9613 pregnancy negative
## 9614 9614 prejudice anger
## 9615 9615 prejudice negative
## 9616 9616 prejudiced disgust
## 9617 9617 prejudiced fear
## 9618 9618 prejudiced negative
## 9619 9619 prejudicial anger
## 9620 9620 prejudicial negative
## 9621 9621 preliminary anticipation
## 9622 9622 premature surprise
## 9623 9623 premeditated fear
## 9624 9624 premeditated negative
## 9625 9625 premier positive
## 9626 9626 premises positive
## 9627 9627 preparation anticipation
## 9628 9628 preparatory anticipation
## 9629 9629 prepare anticipation
## 9630 9630 prepare positive
## 9631 9631 prepared anticipation
## 9632 9632 prepared positive
## 9633 9633 prepared trust
## 9634 9634 preparedness anticipation
## 9635 9635 preponderance trust
## 9636 9636 prerequisite anticipation
## 9637 9637 prescient anticipation
## 9638 9638 prescient positive
## 9639 9639 presence positive
## 9640 9640 present anticipation
## 9641 9641 present joy
## 9642 9642 present positive
## 9643 9643 present surprise
## 9644 9644 present trust
## 9645 9645 presentable positive
## 9646 9646 presentment negative
## 9647 9647 presentment positive
## 9648 9648 preservative anticipation
## 9649 9649 preservative joy
## 9650 9650 preservative positive
## 9651 9651 preservative surprise
## 9652 9652 preservative trust
## 9653 9653 preserve positive
## 9654 9654 president positive
## 9655 9655 president trust
## 9656 9656 pressure negative
## 9657 9657 prestige joy
## 9658 9658 prestige positive
## 9659 9659 prestige trust
## 9660 9660 presto joy
## 9661 9661 presto positive
## 9662 9662 presto surprise
## 9663 9663 presumption trust
## 9664 9664 presumptuous anger
## 9665 9665 presumptuous disgust
## 9666 9666 presumptuous negative
## 9667 9667 pretend negative
## 9668 9668 pretended negative
## 9669 9669 pretending anger
## 9670 9670 pretending negative
## 9671 9671 pretense negative
## 9672 9672 pretensions negative
## 9673 9673 pretty anticipation
## 9674 9674 pretty joy
## 9675 9675 pretty positive
## 9676 9676 pretty trust
## 9677 9677 prevail anticipation
## 9678 9678 prevail joy
## 9679 9679 prevail positive
## 9680 9680 prevalent trust
## 9681 9681 prevent fear
## 9682 9682 prevention anticipation
## 9683 9683 prevention positive
## 9684 9684 preventive negative
## 9685 9685 prey fear
## 9686 9686 prey negative
## 9687 9687 priceless positive
## 9688 9688 prick anger
## 9689 9689 prick disgust
## 9690 9690 prick fear
## 9691 9691 prick negative
## 9692 9692 prick surprise
## 9693 9693 prickly negative
## 9694 9694 pride joy
## 9695 9695 pride positive
## 9696 9696 priest positive
## 9697 9697 priest trust
## 9698 9698 priesthood anticipation
## 9699 9699 priesthood joy
## 9700 9700 priesthood positive
## 9701 9701 priesthood sadness
## 9702 9702 priesthood trust
## 9703 9703 priestly positive
## 9704 9704 primacy positive
## 9705 9705 primary positive
## 9706 9706 prime positive
## 9707 9707 primer positive
## 9708 9708 primer trust
## 9709 9709 prince positive
## 9710 9710 princely anticipation
## 9711 9711 princely joy
## 9712 9712 princely positive
## 9713 9713 princely surprise
## 9714 9714 princely trust
## 9715 9715 princess positive
## 9716 9716 principal positive
## 9717 9717 principal trust
## 9718 9718 prison anger
## 9719 9719 prison fear
## 9720 9720 prison negative
## 9721 9721 prison sadness
## 9722 9722 prisoner anger
## 9723 9723 prisoner disgust
## 9724 9724 prisoner fear
## 9725 9725 prisoner negative
## 9726 9726 prisoner sadness
## 9727 9727 pristine positive
## 9728 9728 privileged joy
## 9729 9729 privileged positive
## 9730 9730 privileged trust
## 9731 9731 privy negative
## 9732 9732 privy trust
## 9733 9733 probability anticipation
## 9734 9734 probation anticipation
## 9735 9735 probation fear
## 9736 9736 probation sadness
## 9737 9737 probity positive
## 9738 9738 probity trust
## 9739 9739 problem fear
## 9740 9740 problem negative
## 9741 9741 problem sadness
## 9742 9742 procedure fear
## 9743 9743 procedure positive
## 9744 9744 proceeding anticipation
## 9745 9745 procession joy
## 9746 9746 procession positive
## 9747 9747 procession sadness
## 9748 9748 procession surprise
## 9749 9749 procrastinate negative
## 9750 9750 procrastination negative
## 9751 9751 proctor positive
## 9752 9752 proctor trust
## 9753 9753 procure positive
## 9754 9754 prodigal negative
## 9755 9755 prodigal positive
## 9756 9756 prodigious positive
## 9757 9757 prodigy positive
## 9758 9758 producer positive
## 9759 9759 producing positive
## 9760 9760 production anticipation
## 9761 9761 production positive
## 9762 9762 productive positive
## 9763 9763 productivity positive
## 9764 9764 profane anger
## 9765 9765 profane negative
## 9766 9766 profanity anger
## 9767 9767 profanity negative
## 9768 9768 profession positive
## 9769 9769 professional positive
## 9770 9770 professional trust
## 9771 9771 professor positive
## 9772 9772 professor trust
## 9773 9773 professorship trust
## 9774 9774 proficiency anticipation
## 9775 9775 proficiency joy
## 9776 9776 proficiency positive
## 9777 9777 proficiency surprise
## 9778 9778 proficiency trust
## 9779 9779 proficient positive
## 9780 9780 proficient trust
## 9781 9781 profuse positive
## 9782 9782 profusion negative
## 9783 9783 prognosis anticipation
## 9784 9784 prognosis fear
## 9785 9785 prognostic anticipation
## 9786 9786 progress anticipation
## 9787 9787 progress joy
## 9788 9788 progress positive
## 9789 9789 progression anticipation
## 9790 9790 progression joy
## 9791 9791 progression positive
## 9792 9792 progression sadness
## 9793 9793 progression trust
## 9794 9794 progressive positive
## 9795 9795 prohibited anger
## 9796 9796 prohibited disgust
## 9797 9797 prohibited fear
## 9798 9798 prohibited negative
## 9799 9799 prohibition negative
## 9800 9800 projectile fear
## 9801 9801 projectiles fear
## 9802 9802 prolific positive
## 9803 9803 prologue anticipation
## 9804 9804 prolong disgust
## 9805 9805 prolong negative
## 9806 9806 prominence positive
## 9807 9807 prominently positive
## 9808 9808 promiscuous negative
## 9809 9809 promise joy
## 9810 9810 promise positive
## 9811 9811 promise trust
## 9812 9812 promising positive
## 9813 9813 promotion positive
## 9814 9814 proof trust
## 9815 9815 prop positive
## 9816 9816 propaganda negative
## 9817 9817 proper positive
## 9818 9818 prophecy anticipation
## 9819 9819 prophet anticipation
## 9820 9820 prophet positive
## 9821 9821 prophet trust
## 9822 9822 prophetic anticipation
## 9823 9823 prophylactic anticipation
## 9824 9824 prophylactic positive
## 9825 9825 prophylactic trust
## 9826 9826 proposition positive
## 9827 9827 prosecute anger
## 9828 9828 prosecute fear
## 9829 9829 prosecute negative
## 9830 9830 prosecute sadness
## 9831 9831 prosecution disgust
## 9832 9832 prosecution negative
## 9833 9833 prospect positive
## 9834 9834 prospectively anticipation
## 9835 9835 prosper anticipation
## 9836 9836 prosper joy
## 9837 9837 prosper positive
## 9838 9838 prosperity positive
## 9839 9839 prosperous joy
## 9840 9840 prosperous positive
## 9841 9841 prostitute disgust
## 9842 9842 prostitute negative
## 9843 9843 prostitution disgust
## 9844 9844 prostitution negative
## 9845 9845 prostitution sadness
## 9846 9846 protect positive
## 9847 9847 protected trust
## 9848 9848 protecting positive
## 9849 9849 protecting trust
## 9850 9850 protective positive
## 9851 9851 protector positive
## 9852 9852 protector trust
## 9853 9853 proud anticipation
## 9854 9854 proud joy
## 9855 9855 proud positive
## 9856 9856 proud trust
## 9857 9857 prove positive
## 9858 9858 proven trust
## 9859 9859 proverbial positive
## 9860 9860 provide positive
## 9861 9861 provide trust
## 9862 9862 providing anticipation
## 9863 9863 providing joy
## 9864 9864 providing positive
## 9865 9865 providing trust
## 9866 9866 proviso trust
## 9867 9867 provocation anger
## 9868 9868 provocation negative
## 9869 9869 provoking anger
## 9870 9870 provoking disgust
## 9871 9871 provoking negative
## 9872 9872 prowl fear
## 9873 9873 prowl surprise
## 9874 9874 proxy trust
## 9875 9875 prudence positive
## 9876 9876 prudent positive
## 9877 9877 prudent trust
## 9878 9878 pry anger
## 9879 9879 pry anticipation
## 9880 9880 pry negative
## 9881 9881 pry trust
## 9882 9882 prying negative
## 9883 9883 psalm positive
## 9884 9884 psychosis anger
## 9885 9885 psychosis fear
## 9886 9886 psychosis negative
## 9887 9887 psychosis sadness
## 9888 9888 public anticipation
## 9889 9889 public positive
## 9890 9890 publicist negative
## 9891 9891 puffy negative
## 9892 9892 puke disgust
## 9893 9893 pull positive
## 9894 9894 pulpit positive
## 9895 9895 puma fear
## 9896 9896 puma surprise
## 9897 9897 punch anger
## 9898 9898 punch fear
## 9899 9899 punch negative
## 9900 9900 punch sadness
## 9901 9901 punch surprise
## 9902 9902 punctual anticipation
## 9903 9903 punctual positive
## 9904 9904 punctual trust
## 9905 9905 punctuality positive
## 9906 9906 pungent disgust
## 9907 9907 pungent negative
## 9908 9908 punish fear
## 9909 9909 punish negative
## 9910 9910 punished anger
## 9911 9911 punished anticipation
## 9912 9912 punished disgust
## 9913 9913 punished fear
## 9914 9914 punished negative
## 9915 9915 punished sadness
## 9916 9916 punishing anger
## 9917 9917 punishing fear
## 9918 9918 punishing negative
## 9919 9919 punishing sadness
## 9920 9920 punishment anger
## 9921 9921 punishment disgust
## 9922 9922 punishment fear
## 9923 9923 punishment negative
## 9924 9924 punitive anger
## 9925 9925 punitive fear
## 9926 9926 punitive negative
## 9927 9927 punitive sadness
## 9928 9928 punt anticipation
## 9929 9929 puppy anticipation
## 9930 9930 puppy positive
## 9931 9931 puppy trust
## 9932 9932 purely positive
## 9933 9933 purely trust
## 9934 9934 purgatory disgust
## 9935 9935 purgatory fear
## 9936 9936 purgatory negative
## 9937 9937 purge fear
## 9938 9938 purge negative
## 9939 9939 purification positive
## 9940 9940 purification trust
## 9941 9941 purify joy
## 9942 9942 purify positive
## 9943 9943 purify trust
## 9944 9944 purist positive
## 9945 9945 purity positive
## 9946 9946 purity surprise
## 9947 9947 purr joy
## 9948 9948 purr positive
## 9949 9949 purr trust
## 9950 9950 pus disgust
## 9951 9951 pus negative
## 9952 9952 putative trust
## 9953 9953 quack disgust
## 9954 9954 quack negative
## 9955 9955 quagmire disgust
## 9956 9956 quagmire negative
## 9957 9957 quail fear
## 9958 9958 quail negative
## 9959 9959 quaint joy
## 9960 9960 quaint positive
## 9961 9961 quaint trust
## 9962 9962 quake fear
## 9963 9963 qualified positive
## 9964 9964 qualified trust
## 9965 9965 qualifying positive
## 9966 9966 quandary anger
## 9967 9967 quandary fear
## 9968 9968 quandary negative
## 9969 9969 quarantine fear
## 9970 9970 quarrel anger
## 9971 9971 quarrel negative
## 9972 9972 quash fear
## 9973 9973 quash negative
## 9974 9974 quell negative
## 9975 9975 quest anticipation
## 9976 9976 quest positive
## 9977 9977 question positive
## 9978 9978 questionable disgust
## 9979 9979 questionable negative
## 9980 9980 quicken anticipation
## 9981 9981 quickness positive
## 9982 9982 quickness surprise
## 9983 9983 quicksilver negative
## 9984 9984 quicksilver surprise
## 9985 9985 quiescent positive
## 9986 9986 quiet positive
## 9987 9987 quiet sadness
## 9988 9988 quinine positive
## 9989 9989 quit negative
## 9990 9990 quiver fear
## 9991 9991 quiver negative
## 9992 9992 quivering fear
## 9993 9993 quivering negative
## 9994 9994 quiz negative
## 9995 9995 quote anticipation
## 9996 9996 quote negative
## 9997 9997 quote positive
## 9998 9998 quote surprise
## 9999 9999 rabble anger
## 10000 10000 rabble fear
## 10001 10001 rabble negative
## 10002 10002 rabid anger
## 10003 10003 rabid anticipation
## 10004 10004 rabid disgust
## 10005 10005 rabid fear
## 10006 10006 rabid negative
## 10007 10007 rabid sadness
## 10008 10008 rabies negative
## 10009 10009 rack negative
## 10010 10010 rack sadness
## 10011 10011 racket negative
## 10012 10012 radar trust
## 10013 10013 radiance anticipation
## 10014 10014 radiance joy
## 10015 10015 radiance positive
## 10016 10016 radiance trust
## 10017 10017 radiant joy
## 10018 10018 radiant positive
## 10019 10019 radiate positive
## 10020 10020 radiation fear
## 10021 10021 radiation negative
## 10022 10022 radio positive
## 10023 10023 radioactive fear
## 10024 10024 radioactive negative
## 10025 10025 radon fear
## 10026 10026 radon negative
## 10027 10027 raffle anticipation
## 10028 10028 raffle surprise
## 10029 10029 rage anger
## 10030 10030 rage negative
## 10031 10031 raging anger
## 10032 10032 raging disgust
## 10033 10033 raging fear
## 10034 10034 raging negative
## 10035 10035 rags disgust
## 10036 10036 rags negative
## 10037 10037 raid anger
## 10038 10038 raid fear
## 10039 10039 raid negative
## 10040 10040 raid surprise
## 10041 10041 rail anger
## 10042 10042 rail anticipation
## 10043 10043 rail negative
## 10044 10044 rainy sadness
## 10045 10045 ram anger
## 10046 10046 ram anticipation
## 10047 10047 ram negative
## 10048 10048 rambling negative
## 10049 10049 rampage anger
## 10050 10050 rampage fear
## 10051 10051 rampage negative
## 10052 10052 rancid disgust
## 10053 10053 rancid negative
## 10054 10054 randomly surprise
## 10055 10055 ranger trust
## 10056 10056 ransom anger
## 10057 10057 ransom fear
## 10058 10058 ransom negative
## 10059 10059 rape anger
## 10060 10060 rape disgust
## 10061 10061 rape fear
## 10062 10062 rape negative
## 10063 10063 rape sadness
## 10064 10064 rapid surprise
## 10065 10065 rapping anger
## 10066 10066 rapt joy
## 10067 10067 rapt positive
## 10068 10068 rapt surprise
## 10069 10069 rapt trust
## 10070 10070 raptors fear
## 10071 10071 raptors negative
## 10072 10072 rapture anticipation
## 10073 10073 rapture joy
## 10074 10074 rapture positive
## 10075 10075 rarity surprise
## 10076 10076 rascal anger
## 10077 10077 rascal disgust
## 10078 10078 rascal fear
## 10079 10079 rascal negative
## 10080 10080 rash disgust
## 10081 10081 rash negative
## 10082 10082 rat disgust
## 10083 10083 rat fear
## 10084 10084 rat negative
## 10085 10085 ratify trust
## 10086 10086 rating anger
## 10087 10087 rating fear
## 10088 10088 rating negative
## 10089 10089 rating sadness
## 10090 10090 rational positive
## 10091 10091 rational trust
## 10092 10092 rattlesnake fear
## 10093 10093 raucous negative
## 10094 10094 rave anger
## 10095 10095 rave disgust
## 10096 10096 rave joy
## 10097 10097 rave negative
## 10098 10098 rave positive
## 10099 10099 rave surprise
## 10100 10100 rave trust
## 10101 10101 ravenous anger
## 10102 10102 ravenous fear
## 10103 10103 ravenous negative
## 10104 10104 ravenous sadness
## 10105 10105 ravine fear
## 10106 10106 raving anger
## 10107 10107 raving anticipation
## 10108 10108 raving fear
## 10109 10109 raving joy
## 10110 10110 raving negative
## 10111 10111 raving surprise
## 10112 10112 razor fear
## 10113 10113 react anger
## 10114 10114 react fear
## 10115 10115 reactionary negative
## 10116 10116 reader positive
## 10117 10117 readily positive
## 10118 10118 readiness anticipation
## 10119 10119 readiness joy
## 10120 10120 readiness positive
## 10121 10121 readiness trust
## 10122 10122 reading positive
## 10123 10123 ready anticipation
## 10124 10124 reaffirm positive
## 10125 10125 real positive
## 10126 10126 real trust
## 10127 10127 reappear surprise
## 10128 10128 rear negative
## 10129 10129 reason positive
## 10130 10130 reassurance positive
## 10131 10131 reassurance trust
## 10132 10132 reassure positive
## 10133 10133 reassure trust
## 10134 10134 rebate positive
## 10135 10135 rebel anger
## 10136 10136 rebel fear
## 10137 10137 rebel negative
## 10138 10138 rebellion anger
## 10139 10139 rebellion disgust
## 10140 10140 rebellion fear
## 10141 10141 rebuke negative
## 10142 10142 rebut negative
## 10143 10143 recalcitrant anger
## 10144 10144 recalcitrant disgust
## 10145 10145 recalcitrant negative
## 10146 10146 recast positive
## 10147 10147 received positive
## 10148 10148 receiving anticipation
## 10149 10149 receiving joy
## 10150 10150 receiving positive
## 10151 10151 receiving surprise
## 10152 10152 recesses fear
## 10153 10153 recession anger
## 10154 10154 recession disgust
## 10155 10155 recession fear
## 10156 10156 recession negative
## 10157 10157 recession sadness
## 10158 10158 recherche positive
## 10159 10159 recidivism anger
## 10160 10160 recidivism disgust
## 10161 10161 recidivism negative
## 10162 10162 recidivism sadness
## 10163 10163 recipient anticipation
## 10164 10164 reciprocate positive
## 10165 10165 reckless anger
## 10166 10166 reckless fear
## 10167 10167 reckless negative
## 10168 10168 recklessness anger
## 10169 10169 recklessness disgust
## 10170 10170 recklessness fear
## 10171 10171 recklessness negative
## 10172 10172 recklessness surprise
## 10173 10173 reclamation positive
## 10174 10174 recline positive
## 10175 10175 recline trust
## 10176 10176 recognizable anticipation
## 10177 10177 recognizable positive
## 10178 10178 recombination anticipation
## 10179 10179 recommend positive
## 10180 10180 recommend trust
## 10181 10181 reconciliation anticipation
## 10182 10182 reconciliation joy
## 10183 10183 reconciliation positive
## 10184 10184 reconciliation trust
## 10185 10185 reconsideration positive
## 10186 10186 reconsideration trust
## 10187 10187 reconstruct anticipation
## 10188 10188 reconstruct positive
## 10189 10189 reconstruction anticipation
## 10190 10190 reconstruction positive
## 10191 10191 recorder positive
## 10192 10192 recoup positive
## 10193 10193 recovery positive
## 10194 10194 recreation anticipation
## 10195 10195 recreation joy
## 10196 10196 recreation positive
## 10197 10197 recreational anticipation
## 10198 10198 recreational joy
## 10199 10199 recreational positive
## 10200 10200 recruits trust
## 10201 10201 rectify positive
## 10202 10202 recurrent anticipation
## 10203 10203 redemption positive
## 10204 10204 redress positive
## 10205 10205 redundant negative
## 10206 10206 referee trust
## 10207 10207 refine positive
## 10208 10208 refinement positive
## 10209 10209 reflex surprise
## 10210 10210 reflux disgust
## 10211 10211 reflux negative
## 10212 10212 reform positive
## 10213 10213 reformation positive
## 10214 10214 reformer positive
## 10215 10215 refractory negative
## 10216 10216 refreshing positive
## 10217 10217 refugee sadness
## 10218 10218 refurbish positive
## 10219 10219 refusal negative
## 10220 10220 refuse negative
## 10221 10221 refused negative
## 10222 10222 refused sadness
## 10223 10223 refutation fear
## 10224 10224 regal positive
## 10225 10225 regal trust
## 10226 10226 regatta anticipation
## 10227 10227 regent positive
## 10228 10228 regent trust
## 10229 10229 regiment fear
## 10230 10230 registry trust
## 10231 10231 regress negative
## 10232 10232 regression negative
## 10233 10233 regressive negative
## 10234 10234 regressive positive
## 10235 10235 regret negative
## 10236 10236 regret sadness
## 10237 10237 regrettable negative
## 10238 10238 regrettable sadness
## 10239 10239 regretted negative
## 10240 10240 regretted sadness
## 10241 10241 regretting negative
## 10242 10242 regretting sadness
## 10243 10243 regularity anticipation
## 10244 10244 regularity positive
## 10245 10245 regularity trust
## 10246 10246 regulate positive
## 10247 10247 regulatory fear
## 10248 10248 regulatory negative
## 10249 10249 regurgitation disgust
## 10250 10250 rehabilitate positive
## 10251 10251 rehabilitation anticipation
## 10252 10252 rehabilitation positive
## 10253 10253 reimburse positive
## 10254 10254 reimbursement positive
## 10255 10255 reimbursement trust
## 10256 10256 rein negative
## 10257 10257 reinforcement positive
## 10258 10258 reinforcement trust
## 10259 10259 reinforcements trust
## 10260 10260 reinstate positive
## 10261 10261 reject anger
## 10262 10262 reject fear
## 10263 10263 reject negative
## 10264 10264 reject sadness
## 10265 10265 rejected negative
## 10266 10266 rejection anger
## 10267 10267 rejection disgust
## 10268 10268 rejection fear
## 10269 10269 rejection negative
## 10270 10270 rejection sadness
## 10271 10271 rejects anger
## 10272 10272 rejects fear
## 10273 10273 rejects negative
## 10274 10274 rejects sadness
## 10275 10275 rejoice anticipation
## 10276 10276 rejoice joy
## 10277 10277 rejoice positive
## 10278 10278 rejoice surprise
## 10279 10279 rejoice trust
## 10280 10280 rejoicing anticipation
## 10281 10281 rejoicing joy
## 10282 10282 rejoicing positive
## 10283 10283 rejoicing surprise
## 10284 10284 rejuvenate positive
## 10285 10285 rejuvenated positive
## 10286 10286 rekindle anticipation
## 10287 10287 rekindle fear
## 10288 10288 rekindle joy
## 10289 10289 rekindle negative
## 10290 10290 rekindle positive
## 10291 10291 rekindle surprise
## 10292 10292 relapse fear
## 10293 10293 relapse negative
## 10294 10294 relapse sadness
## 10295 10295 related trust
## 10296 10296 relative trust
## 10297 10297 relaxation positive
## 10298 10298 relegation negative
## 10299 10299 relevant positive
## 10300 10300 relevant trust
## 10301 10301 reliability positive
## 10302 10302 reliability trust
## 10303 10303 reliable positive
## 10304 10304 reliable trust
## 10305 10305 reliance positive
## 10306 10306 reliance trust
## 10307 10307 relics sadness
## 10308 10308 relief positive
## 10309 10309 relieving positive
## 10310 10310 religion trust
## 10311 10311 relinquish negative
## 10312 10312 reluctant fear
## 10313 10313 reluctant negative
## 10314 10314 remains disgust
## 10315 10315 remains fear
## 10316 10316 remains negative
## 10317 10317 remains positive
## 10318 10318 remains trust
## 10319 10319 remake positive
## 10320 10320 remand anger
## 10321 10321 remand negative
## 10322 10322 remarkable joy
## 10323 10323 remarkable positive
## 10324 10324 remarkable surprise
## 10325 10325 remarkable trust
## 10326 10326 remarkably positive
## 10327 10327 remedial negative
## 10328 10328 remedy anticipation
## 10329 10329 remedy joy
## 10330 10330 remedy positive
## 10331 10331 remedy trust
## 10332 10332 remiss anger
## 10333 10333 remiss disgust
## 10334 10334 remiss negative
## 10335 10335 remiss sadness
## 10336 10336 remission positive
## 10337 10337 remodel positive
## 10338 10338 remorse negative
## 10339 10339 remorse sadness
## 10340 10340 removal negative
## 10341 10341 remove anger
## 10342 10342 remove fear
## 10343 10343 remove negative
## 10344 10344 remove sadness
## 10345 10345 renaissance positive
## 10346 10346 rencontre negative
## 10347 10347 rend negative
## 10348 10348 render positive
## 10349 10349 renegade anger
## 10350 10350 renegade negative
## 10351 10351 renewal positive
## 10352 10352 renounce anger
## 10353 10353 renounce negative
## 10354 10354 renovate anticipation
## 10355 10355 renovate positive
## 10356 10356 renovation anticipation
## 10357 10357 renovation joy
## 10358 10358 renovation positive
## 10359 10359 renown positive
## 10360 10360 renowned positive
## 10361 10361 renunciation negative
## 10362 10362 reorganize positive
## 10363 10363 reparation positive
## 10364 10364 reparation trust
## 10365 10365 repay anger
## 10366 10366 repay anticipation
## 10367 10367 repay joy
## 10368 10368 repay positive
## 10369 10369 repay trust
## 10370 10370 repellant disgust
## 10371 10371 repellant fear
## 10372 10372 repellant negative
## 10373 10373 repellent anger
## 10374 10374 repellent disgust
## 10375 10375 repellent fear
## 10376 10376 repellent negative
## 10377 10377 repelling disgust
## 10378 10378 repelling negative
## 10379 10379 repent fear
## 10380 10380 repent positive
## 10381 10381 replenish positive
## 10382 10382 replete positive
## 10383 10383 reporter positive
## 10384 10384 reporter trust
## 10385 10385 repose positive
## 10386 10386 represented positive
## 10387 10387 representing anticipation
## 10388 10388 repress negative
## 10389 10389 repress sadness
## 10390 10390 repression fear
## 10391 10391 repression negative
## 10392 10392 reprimand anger
## 10393 10393 reprimand negative
## 10394 10394 reprint negative
## 10395 10395 reprisal anger
## 10396 10396 reprisal fear
## 10397 10397 reprisal negative
## 10398 10398 reprisal sadness
## 10399 10399 reproach anger
## 10400 10400 reproach disgust
## 10401 10401 reproach negative
## 10402 10402 reproach sadness
## 10403 10403 reproductive joy
## 10404 10404 reproductive positive
## 10405 10405 republic negative
## 10406 10406 repudiation anger
## 10407 10407 repudiation disgust
## 10408 10408 repudiation negative
## 10409 10409 repulsion disgust
## 10410 10410 repulsion fear
## 10411 10411 repulsion negative
## 10412 10412 reputable positive
## 10413 10413 reputable trust
## 10414 10414 requiem sadness
## 10415 10415 rescind negative
## 10416 10416 rescission negative
## 10417 10417 rescue anticipation
## 10418 10418 rescue joy
## 10419 10419 rescue positive
## 10420 10420 rescue surprise
## 10421 10421 rescue trust
## 10422 10422 resection fear
## 10423 10423 resent anger
## 10424 10424 resent negative
## 10425 10425 resentful anger
## 10426 10426 resentful negative
## 10427 10427 resentment anger
## 10428 10428 resentment disgust
## 10429 10429 resentment negative
## 10430 10430 resentment sadness
## 10431 10431 reserve positive
## 10432 10432 resident positive
## 10433 10433 resign anger
## 10434 10434 resign disgust
## 10435 10435 resign fear
## 10436 10436 resign negative
## 10437 10437 resign sadness
## 10438 10438 resignation negative
## 10439 10439 resignation sadness
## 10440 10440 resignation surprise
## 10441 10441 resigned negative
## 10442 10442 resigned sadness
## 10443 10443 resilient positive
## 10444 10444 resist negative
## 10445 10445 resistance anger
## 10446 10446 resistance negative
## 10447 10447 resistant fear
## 10448 10448 resistant negative
## 10449 10449 resisting anger
## 10450 10450 resisting fear
## 10451 10451 resisting negative
## 10452 10452 resisting sadness
## 10453 10453 resistive positive
## 10454 10454 resolutely positive
## 10455 10455 resources joy
## 10456 10456 resources positive
## 10457 10457 resources trust
## 10458 10458 respect anticipation
## 10459 10459 respect joy
## 10460 10460 respect positive
## 10461 10461 respect trust
## 10462 10462 respectability positive
## 10463 10463 respectable positive
## 10464 10464 respectable trust
## 10465 10465 respectful positive
## 10466 10466 respectful trust
## 10467 10467 respecting positive
## 10468 10468 respects positive
## 10469 10469 respects trust
## 10470 10470 respite joy
## 10471 10471 respite positive
## 10472 10472 respite trust
## 10473 10473 resplendent joy
## 10474 10474 resplendent positive
## 10475 10475 responsible positive
## 10476 10476 responsible trust
## 10477 10477 responsive anticipation
## 10478 10478 responsive positive
## 10479 10479 responsive trust
## 10480 10480 rest positive
## 10481 10481 restful positive
## 10482 10482 restitution anger
## 10483 10483 restitution positive
## 10484 10484 restlessness anticipation
## 10485 10485 restorative anticipation
## 10486 10486 restorative joy
## 10487 10487 restorative positive
## 10488 10488 restorative trust
## 10489 10489 restoring positive
## 10490 10490 restrain anger
## 10491 10491 restrain fear
## 10492 10492 restrain negative
## 10493 10493 restrained fear
## 10494 10494 restraint positive
## 10495 10495 restrict negative
## 10496 10496 restrict sadness
## 10497 10497 restriction anger
## 10498 10498 restriction fear
## 10499 10499 restriction negative
## 10500 10500 restriction sadness
## 10501 10501 restrictive negative
## 10502 10502 result anticipation
## 10503 10503 resultant anticipation
## 10504 10504 resumption positive
## 10505 10505 retain trust
## 10506 10506 retaliate anger
## 10507 10507 retaliate negative
## 10508 10508 retaliation anger
## 10509 10509 retaliation fear
## 10510 10510 retaliation negative
## 10511 10511 retaliatory anger
## 10512 10512 retaliatory negative
## 10513 10513 retard disgust
## 10514 10514 retard fear
## 10515 10515 retard negative
## 10516 10516 retard sadness
## 10517 10517 retardation negative
## 10518 10518 retention positive
## 10519 10519 reticent fear
## 10520 10520 reticent negative
## 10521 10521 retirement anticipation
## 10522 10522 retirement fear
## 10523 10523 retirement joy
## 10524 10524 retirement negative
## 10525 10525 retirement positive
## 10526 10526 retirement sadness
## 10527 10527 retirement trust
## 10528 10528 retort negative
## 10529 10529 retract anger
## 10530 10530 retract negative
## 10531 10531 retraction negative
## 10532 10532 retrenchment fear
## 10533 10533 retrenchment negative
## 10534 10534 retribution anger
## 10535 10535 retribution fear
## 10536 10536 retribution negative
## 10537 10537 retribution sadness
## 10538 10538 retrograde negative
## 10539 10539 reunion anticipation
## 10540 10540 reunion positive
## 10541 10541 reunion trust
## 10542 10542 revel joy
## 10543 10543 revel positive
## 10544 10544 revels joy
## 10545 10545 revels positive
## 10546 10546 revenge anger
## 10547 10547 revenge anticipation
## 10548 10548 revenge fear
## 10549 10549 revenge negative
## 10550 10550 revenge surprise
## 10551 10551 revere anticipation
## 10552 10552 revere joy
## 10553 10553 revere positive
## 10554 10554 revere trust
## 10555 10555 reverence joy
## 10556 10556 reverence positive
## 10557 10557 reverence trust
## 10558 10558 reverend joy
## 10559 10559 reverend positive
## 10560 10560 reverie joy
## 10561 10561 reverie positive
## 10562 10562 reverie trust
## 10563 10563 reversal anger
## 10564 10564 reversal disgust
## 10565 10565 reversal negative
## 10566 10566 reversal surprise
## 10567 10567 revise positive
## 10568 10568 revival anticipation
## 10569 10569 revival joy
## 10570 10570 revival positive
## 10571 10571 revival trust
## 10572 10572 revive anticipation
## 10573 10573 revive negative
## 10574 10574 revive positive
## 10575 10575 revocation negative
## 10576 10576 revoke anger
## 10577 10577 revoke disgust
## 10578 10578 revoke fear
## 10579 10579 revoke negative
## 10580 10580 revoke sadness
## 10581 10581 revolt anger
## 10582 10582 revolt negative
## 10583 10583 revolt surprise
## 10584 10584 revolting anger
## 10585 10585 revolting disgust
## 10586 10586 revolting fear
## 10587 10587 revolting negative
## 10588 10588 revolution anger
## 10589 10589 revolution anticipation
## 10590 10590 revolution fear
## 10591 10591 revolution negative
## 10592 10592 revolution positive
## 10593 10593 revolution sadness
## 10594 10594 revolution surprise
## 10595 10595 revolutionary positive
## 10596 10596 revolver anger
## 10597 10597 revolver fear
## 10598 10598 revolver negative
## 10599 10599 revolver sadness
## 10600 10600 revulsion anger
## 10601 10601 revulsion disgust
## 10602 10602 revulsion fear
## 10603 10603 revulsion negative
## 10604 10604 reward anticipation
## 10605 10605 reward joy
## 10606 10606 reward positive
## 10607 10607 reward surprise
## 10608 10608 reward trust
## 10609 10609 rheumatism anger
## 10610 10610 rheumatism fear
## 10611 10611 rheumatism negative
## 10612 10612 rheumatism sadness
## 10613 10613 rhythm positive
## 10614 10614 rhythmical joy
## 10615 10615 rhythmical positive
## 10616 10616 rhythmical surprise
## 10617 10617 ribbon anger
## 10618 10618 ribbon anticipation
## 10619 10619 ribbon joy
## 10620 10620 ribbon positive
## 10621 10621 richness positive
## 10622 10622 rickety negative
## 10623 10623 riddle surprise
## 10624 10624 riddled negative
## 10625 10625 rider positive
## 10626 10626 ridicule anger
## 10627 10627 ridicule disgust
## 10628 10628 ridicule negative
## 10629 10629 ridicule sadness
## 10630 10630 ridiculous anger
## 10631 10631 ridiculous disgust
## 10632 10632 ridiculous negative
## 10633 10633 rife negative
## 10634 10634 rifle anger
## 10635 10635 rifle fear
## 10636 10636 rifle negative
## 10637 10637 rift negative
## 10638 10638 righteous positive
## 10639 10639 rightful positive
## 10640 10640 rightly positive
## 10641 10641 rigid negative
## 10642 10642 rigidity negative
## 10643 10643 rigor disgust
## 10644 10644 rigor fear
## 10645 10645 rigor negative
## 10646 10646 rigorous negative
## 10647 10647 ringer anger
## 10648 10648 ringer negative
## 10649 10649 riot anger
## 10650 10650 riot fear
## 10651 10651 riot negative
## 10652 10652 riotous anger
## 10653 10653 riotous fear
## 10654 10654 riotous negative
## 10655 10655 riotous surprise
## 10656 10656 ripe positive
## 10657 10657 ripen anticipation
## 10658 10658 ripen positive
## 10659 10659 rising anticipation
## 10660 10660 rising joy
## 10661 10661 rising positive
## 10662 10662 risk anticipation
## 10663 10663 risk fear
## 10664 10664 risk negative
## 10665 10665 risky anticipation
## 10666 10666 risky fear
## 10667 10667 risky negative
## 10668 10668 rivalry anger
## 10669 10669 rivalry negative
## 10670 10670 riveting positive
## 10671 10671 roadster joy
## 10672 10672 roadster positive
## 10673 10673 roadster trust
## 10674 10674 rob anger
## 10675 10675 rob disgust
## 10676 10676 rob fear
## 10677 10677 rob negative
## 10678 10678 rob sadness
## 10679 10679 robber disgust
## 10680 10680 robber fear
## 10681 10681 robber negative
## 10682 10682 robbery anger
## 10683 10683 robbery disgust
## 10684 10684 robbery fear
## 10685 10685 robbery negative
## 10686 10686 robbery sadness
## 10687 10687 robust positive
## 10688 10688 rock positive
## 10689 10689 rocket anger
## 10690 10690 rod fear
## 10691 10691 rod positive
## 10692 10692 rod trust
## 10693 10693 rogue disgust
## 10694 10694 rogue negative
## 10695 10695 rollicking joy
## 10696 10696 rollicking positive
## 10697 10697 romance anticipation
## 10698 10698 romance fear
## 10699 10699 romance joy
## 10700 10700 romance positive
## 10701 10701 romance sadness
## 10702 10702 romance surprise
## 10703 10703 romance trust
## 10704 10704 romantic anticipation
## 10705 10705 romantic joy
## 10706 10706 romantic positive
## 10707 10707 romantic trust
## 10708 10708 romanticism joy
## 10709 10709 romanticism positive
## 10710 10710 romp joy
## 10711 10711 romp positive
## 10712 10712 rook anger
## 10713 10713 rook disgust
## 10714 10714 rook negative
## 10715 10715 rooted positive
## 10716 10716 rooted trust
## 10717 10717 rosy positive
## 10718 10718 rot disgust
## 10719 10719 rot fear
## 10720 10720 rot negative
## 10721 10721 rot sadness
## 10722 10722 rota positive
## 10723 10723 rota trust
## 10724 10724 rotting disgust
## 10725 10725 rotting negative
## 10726 10726 roughness negative
## 10727 10727 roulette anticipation
## 10728 10728 rout negative
## 10729 10729 routine positive
## 10730 10730 routine trust
## 10731 10731 row anger
## 10732 10732 row negative
## 10733 10733 rowdy negative
## 10734 10734 royalty positive
## 10735 10735 rubbish disgust
## 10736 10736 rubbish negative
## 10737 10737 rubble fear
## 10738 10738 rubble negative
## 10739 10739 rubble sadness
## 10740 10740 rubric positive
## 10741 10741 rue negative
## 10742 10742 rue sadness
## 10743 10743 ruffle negative
## 10744 10744 rugged negative
## 10745 10745 ruin fear
## 10746 10746 ruin negative
## 10747 10747 ruin sadness
## 10748 10748 ruined anger
## 10749 10749 ruined disgust
## 10750 10750 ruined fear
## 10751 10751 ruined negative
## 10752 10752 ruined sadness
## 10753 10753 ruinous anger
## 10754 10754 ruinous disgust
## 10755 10755 ruinous fear
## 10756 10756 ruinous negative
## 10757 10757 ruinous sadness
## 10758 10758 rule fear
## 10759 10759 rule trust
## 10760 10760 rumor negative
## 10761 10761 rumor sadness
## 10762 10762 runaway negative
## 10763 10763 runaway sadness
## 10764 10764 rupture fear
## 10765 10765 rupture negative
## 10766 10766 rupture sadness
## 10767 10767 rupture surprise
## 10768 10768 ruse negative
## 10769 10769 rust negative
## 10770 10770 rusty negative
## 10771 10771 ruth positive
## 10772 10772 ruthless anger
## 10773 10773 ruthless disgust
## 10774 10774 ruthless fear
## 10775 10775 ruthless negative
## 10776 10776 saber anger
## 10777 10777 saber fear
## 10778 10778 saber negative
## 10779 10779 sabotage anger
## 10780 10780 sabotage disgust
## 10781 10781 sabotage fear
## 10782 10782 sabotage negative
## 10783 10783 sabotage sadness
## 10784 10784 sabotage surprise
## 10785 10785 sacrifices disgust
## 10786 10786 sacrifices fear
## 10787 10787 sacrifices negative
## 10788 10788 sacrifices sadness
## 10789 10789 sadly negative
## 10790 10790 sadly sadness
## 10791 10791 sadness negative
## 10792 10792 sadness sadness
## 10793 10793 sadness trust
## 10794 10794 safe joy
## 10795 10795 safe positive
## 10796 10796 safe trust
## 10797 10797 safeguard positive
## 10798 10798 safeguard trust
## 10799 10799 safekeeping trust
## 10800 10800 sag fear
## 10801 10801 sag negative
## 10802 10802 sage positive
## 10803 10803 sage trust
## 10804 10804 saint anticipation
## 10805 10805 saint joy
## 10806 10806 saint positive
## 10807 10807 saint surprise
## 10808 10808 saint trust
## 10809 10809 saintly anticipation
## 10810 10810 saintly joy
## 10811 10811 saintly positive
## 10812 10812 saintly surprise
## 10813 10813 saintly trust
## 10814 10814 salary anticipation
## 10815 10815 salary joy
## 10816 10816 salary positive
## 10817 10817 salary trust
## 10818 10818 salient positive
## 10819 10819 saliva anticipation
## 10820 10820 sally surprise
## 10821 10821 saloon anger
## 10822 10822 salutary joy
## 10823 10823 salutary positive
## 10824 10824 salutary trust
## 10825 10825 salute joy
## 10826 10826 salute positive
## 10827 10827 salvation anticipation
## 10828 10828 salvation joy
## 10829 10829 salvation positive
## 10830 10830 salvation trust
## 10831 10831 salve positive
## 10832 10832 samurai fear
## 10833 10833 samurai positive
## 10834 10834 sanctification joy
## 10835 10835 sanctification positive
## 10836 10836 sanctification trust
## 10837 10837 sanctify anticipation
## 10838 10838 sanctify joy
## 10839 10839 sanctify positive
## 10840 10840 sanctify sadness
## 10841 10841 sanctify surprise
## 10842 10842 sanctify trust
## 10843 10843 sanctuary anticipation
## 10844 10844 sanctuary joy
## 10845 10845 sanctuary positive
## 10846 10846 sanctuary trust
## 10847 10847 sanguine positive
## 10848 10848 sanitary positive
## 10849 10849 sap negative
## 10850 10850 sap sadness
## 10851 10851 sappy trust
## 10852 10852 sarcasm anger
## 10853 10853 sarcasm disgust
## 10854 10854 sarcasm negative
## 10855 10855 sarcasm sadness
## 10856 10856 sarcoma fear
## 10857 10857 sarcoma negative
## 10858 10858 sarcoma sadness
## 10859 10859 sardonic negative
## 10860 10860 satanic anger
## 10861 10861 satanic negative
## 10862 10862 satin positive
## 10863 10863 satisfactorily positive
## 10864 10864 satisfied joy
## 10865 10865 satisfied positive
## 10866 10866 saturated disgust
## 10867 10867 saturated negative
## 10868 10868 savage anger
## 10869 10869 savage fear
## 10870 10870 savage negative
## 10871 10871 savagery anger
## 10872 10872 savagery fear
## 10873 10873 savagery negative
## 10874 10874 save joy
## 10875 10875 save positive
## 10876 10876 save trust
## 10877 10877 savings positive
## 10878 10878 savor anticipation
## 10879 10879 savor disgust
## 10880 10880 savor joy
## 10881 10881 savor positive
## 10882 10882 savor sadness
## 10883 10883 savor trust
## 10884 10884 savory positive
## 10885 10885 savvy positive
## 10886 10886 scab negative
## 10887 10887 scaffold fear
## 10888 10888 scaffold negative
## 10889 10889 scalpel fear
## 10890 10890 scalpel negative
## 10891 10891 scaly negative
## 10892 10892 scandal fear
## 10893 10893 scandal negative
## 10894 10894 scandalous anger
## 10895 10895 scandalous negative
## 10896 10896 scanty negative
## 10897 10897 scapegoat anger
## 10898 10898 scapegoat fear
## 10899 10899 scapegoat negative
## 10900 10900 scar anger
## 10901 10901 scar disgust
## 10902 10902 scar fear
## 10903 10903 scar negative
## 10904 10904 scar sadness
## 10905 10905 scarce fear
## 10906 10906 scarce negative
## 10907 10907 scarce sadness
## 10908 10908 scarcely negative
## 10909 10909 scarcely sadness
## 10910 10910 scarcity anger
## 10911 10911 scarcity fear
## 10912 10912 scarcity negative
## 10913 10913 scarcity sadness
## 10914 10914 scare anger
## 10915 10915 scare anticipation
## 10916 10916 scare fear
## 10917 10917 scare negative
## 10918 10918 scare surprise
## 10919 10919 scarecrow fear
## 10920 10920 scarecrow negative
## 10921 10921 scarecrow positive
## 10922 10922 scavenger negative
## 10923 10923 sceptical trust
## 10924 10924 scheme negative
## 10925 10925 schism anger
## 10926 10926 schism negative
## 10927 10927 schizophrenia anger
## 10928 10928 schizophrenia disgust
## 10929 10929 schizophrenia fear
## 10930 10930 schizophrenia negative
## 10931 10931 schizophrenia sadness
## 10932 10932 scholar positive
## 10933 10933 scholarship joy
## 10934 10934 scholarship positive
## 10935 10935 school trust
## 10936 10936 sciatica negative
## 10937 10937 scientific positive
## 10938 10938 scientific trust
## 10939 10939 scientist anticipation
## 10940 10940 scientist positive
## 10941 10941 scientist trust
## 10942 10942 scintilla positive
## 10943 10943 scoff anger
## 10944 10944 scoff disgust
## 10945 10945 scoff negative
## 10946 10946 scold anger
## 10947 10947 scold disgust
## 10948 10948 scold fear
## 10949 10949 scold negative
## 10950 10950 scold sadness
## 10951 10951 scolding anger
## 10952 10952 scolding negative
## 10953 10953 scorching anger
## 10954 10954 scorching negative
## 10955 10955 score anticipation
## 10956 10956 score joy
## 10957 10957 score positive
## 10958 10958 score surprise
## 10959 10959 scorn anger
## 10960 10960 scorn negative
## 10961 10961 scorpion anger
## 10962 10962 scorpion disgust
## 10963 10963 scorpion fear
## 10964 10964 scorpion negative
## 10965 10965 scorpion surprise
## 10966 10966 scotch negative
## 10967 10967 scoundrel anger
## 10968 10968 scoundrel disgust
## 10969 10969 scoundrel fear
## 10970 10970 scoundrel negative
## 10971 10971 scoundrel trust
## 10972 10972 scourge anger
## 10973 10973 scourge fear
## 10974 10974 scourge negative
## 10975 10975 scourge sadness
## 10976 10976 scrambling negative
## 10977 10977 scrapie anger
## 10978 10978 scrapie fear
## 10979 10979 scrapie negative
## 10980 10980 scrapie sadness
## 10981 10981 scream anger
## 10982 10982 scream disgust
## 10983 10983 scream fear
## 10984 10984 scream negative
## 10985 10985 scream surprise
## 10986 10986 screaming anger
## 10987 10987 screaming disgust
## 10988 10988 screaming fear
## 10989 10989 screaming negative
## 10990 10990 screech fear
## 10991 10991 screech negative
## 10992 10992 screech surprise
## 10993 10993 screwed anger
## 10994 10994 screwed negative
## 10995 10995 scribe positive
## 10996 10996 scrimmage negative
## 10997 10997 scrimmage surprise
## 10998 10998 script positive
## 10999 10999 scripture trust
## 11000 11000 scrub disgust
## 11001 11001 scrub negative
## 11002 11002 scrumptious positive
## 11003 11003 scrutinize anticipation
## 11004 11004 scrutinize negative
## 11005 11005 scrutiny negative
## 11006 11006 sculpture positive
## 11007 11007 scum disgust
## 11008 11008 scum negative
## 11009 11009 sea positive
## 11010 11010 seal positive
## 11011 11011 seal trust
## 11012 11012 seals trust
## 11013 11013 sear negative
## 11014 11014 seasoned positive
## 11015 11015 secession negative
## 11016 11016 secluded negative
## 11017 11017 secluded sadness
## 11018 11018 seclusion fear
## 11019 11019 seclusion negative
## 11020 11020 seclusion positive
## 11021 11021 secondhand negative
## 11022 11022 secrecy surprise
## 11023 11023 secrecy trust
## 11024 11024 secret trust
## 11025 11025 secretariat positive
## 11026 11026 secrete disgust
## 11027 11027 secretion disgust
## 11028 11028 secretion negative
## 11029 11029 secretive negative
## 11030 11030 sectarian anger
## 11031 11031 sectarian fear
## 11032 11032 sectarian negative
## 11033 11033 secular anticipation
## 11034 11034 securities trust
## 11035 11035 sedition anger
## 11036 11036 sedition negative
## 11037 11037 sedition sadness
## 11038 11038 seduce negative
## 11039 11039 seduction negative
## 11040 11040 seductive anticipation
## 11041 11041 seek anticipation
## 11042 11042 segregate anger
## 11043 11043 segregate disgust
## 11044 11044 segregate negative
## 11045 11045 segregate sadness
## 11046 11046 segregated negative
## 11047 11047 seize fear
## 11048 11048 seize negative
## 11049 11049 seizure fear
## 11050 11050 selfish anger
## 11051 11051 selfish disgust
## 11052 11052 selfish negative
## 11053 11053 selfishness negative
## 11054 11054 senate trust
## 11055 11055 senile fear
## 11056 11056 senile negative
## 11057 11057 senile sadness
## 11058 11058 seniority positive
## 11059 11059 seniority trust
## 11060 11060 sensational joy
## 11061 11061 sensational positive
## 11062 11062 sense positive
## 11063 11063 senseless anger
## 11064 11064 senseless disgust
## 11065 11065 senseless fear
## 11066 11066 senseless negative
## 11067 11067 senseless sadness
## 11068 11068 senseless surprise
## 11069 11069 sensibility positive
## 11070 11070 sensibly positive
## 11071 11071 sensual anticipation
## 11072 11072 sensual joy
## 11073 11073 sensual negative
## 11074 11074 sensual positive
## 11075 11075 sensual surprise
## 11076 11076 sensual trust
## 11077 11077 sensuality anticipation
## 11078 11078 sensuality joy
## 11079 11079 sensuality positive
## 11080 11080 sensuous joy
## 11081 11081 sensuous positive
## 11082 11082 sentence anger
## 11083 11083 sentence anticipation
## 11084 11084 sentence disgust
## 11085 11085 sentence fear
## 11086 11086 sentence negative
## 11087 11087 sentence sadness
## 11088 11088 sentimental positive
## 11089 11089 sentimentality positive
## 11090 11090 sentinel positive
## 11091 11091 sentinel trust
## 11092 11092 sentry trust
## 11093 11093 separatist anger
## 11094 11094 separatist disgust
## 11095 11095 separatist negative
## 11096 11096 sepsis fear
## 11097 11097 sepsis negative
## 11098 11098 sepsis sadness
## 11099 11099 septic disgust
## 11100 11100 septic negative
## 11101 11101 sequel anticipation
## 11102 11102 sequestration negative
## 11103 11103 sequestration sadness
## 11104 11104 serene negative
## 11105 11105 serene trust
## 11106 11106 serenity anticipation
## 11107 11107 serenity joy
## 11108 11108 serenity positive
## 11109 11109 serenity trust
## 11110 11110 serial anticipation
## 11111 11111 series trust
## 11112 11112 seriousness fear
## 11113 11113 seriousness sadness
## 11114 11114 sermon positive
## 11115 11115 sermon trust
## 11116 11116 serpent disgust
## 11117 11117 serpent fear
## 11118 11118 serpent negative
## 11119 11119 serum positive
## 11120 11120 servant negative
## 11121 11121 servant trust
## 11122 11122 serve negative
## 11123 11123 serve trust
## 11124 11124 servile disgust
## 11125 11125 servile fear
## 11126 11126 servile negative
## 11127 11127 servile sadness
## 11128 11128 servitude negative
## 11129 11129 setback negative
## 11130 11130 setback sadness
## 11131 11131 settlor fear
## 11132 11132 settlor positive
## 11133 11133 sever negative
## 11134 11134 severance sadness
## 11135 11135 sewage disgust
## 11136 11136 sewage negative
## 11137 11137 sewer disgust
## 11138 11138 sewerage disgust
## 11139 11139 sewerage negative
## 11140 11140 sex anticipation
## 11141 11141 sex joy
## 11142 11142 sex positive
## 11143 11143 sex trust
## 11144 11144 shabby disgust
## 11145 11145 shabby negative
## 11146 11146 shack disgust
## 11147 11147 shack negative
## 11148 11148 shack sadness
## 11149 11149 shackle anger
## 11150 11150 shackle anticipation
## 11151 11151 shackle disgust
## 11152 11152 shackle fear
## 11153 11153 shackle negative
## 11154 11154 shackle sadness
## 11155 11155 shady fear
## 11156 11156 shady negative
## 11157 11157 shaking fear
## 11158 11158 shaking negative
## 11159 11159 shaky anger
## 11160 11160 shaky anticipation
## 11161 11161 shaky fear
## 11162 11162 shaky negative
## 11163 11163 sham anger
## 11164 11164 sham disgust
## 11165 11165 sham negative
## 11166 11166 shambles negative
## 11167 11167 shame disgust
## 11168 11168 shame fear
## 11169 11169 shame negative
## 11170 11170 shame sadness
## 11171 11171 shameful negative
## 11172 11172 shameful sadness
## 11173 11173 shameless disgust
## 11174 11174 shameless negative
## 11175 11175 shanghai disgust
## 11176 11176 shanghai fear
## 11177 11177 shanghai negative
## 11178 11178 shank fear
## 11179 11179 shape positive
## 11180 11180 shapely positive
## 11181 11181 share anticipation
## 11182 11182 share joy
## 11183 11183 share positive
## 11184 11184 share trust
## 11185 11185 shark negative
## 11186 11186 sharpen anger
## 11187 11187 sharpen anticipation
## 11188 11188 shatter anger
## 11189 11189 shatter fear
## 11190 11190 shatter negative
## 11191 11191 shatter sadness
## 11192 11192 shatter surprise
## 11193 11193 shattered negative
## 11194 11194 shattered sadness
## 11195 11195 shed negative
## 11196 11196 shell anger
## 11197 11197 shell fear
## 11198 11198 shell negative
## 11199 11199 shell sadness
## 11200 11200 shell surprise
## 11201 11201 shelter positive
## 11202 11202 shelter trust
## 11203 11203 shelved negative
## 11204 11204 shepherd positive
## 11205 11205 shepherd trust
## 11206 11206 sheriff trust
## 11207 11207 shine positive
## 11208 11208 shining anticipation
## 11209 11209 shining joy
## 11210 11210 shining positive
## 11211 11211 ship anticipation
## 11212 11212 shipwreck fear
## 11213 11213 shipwreck negative
## 11214 11214 shipwreck sadness
## 11215 11215 shit anger
## 11216 11216 shit disgust
## 11217 11217 shit negative
## 11218 11218 shiver anger
## 11219 11219 shiver anticipation
## 11220 11220 shiver fear
## 11221 11221 shiver negative
## 11222 11222 shiver sadness
## 11223 11223 shock anger
## 11224 11224 shock fear
## 11225 11225 shock negative
## 11226 11226 shock surprise
## 11227 11227 shockingly surprise
## 11228 11228 shoddy anger
## 11229 11229 shoddy disgust
## 11230 11230 shoddy negative
## 11231 11231 shoot anger
## 11232 11232 shoot fear
## 11233 11233 shoot negative
## 11234 11234 shooter fear
## 11235 11235 shooting anger
## 11236 11236 shooting fear
## 11237 11237 shooting negative
## 11238 11238 shopkeeper trust
## 11239 11239 shoplifting anger
## 11240 11240 shoplifting disgust
## 11241 11241 shoplifting negative
## 11242 11242 shopping anticipation
## 11243 11243 shopping joy
## 11244 11244 shopping positive
## 11245 11245 shopping surprise
## 11246 11246 shopping trust
## 11247 11247 shortage anger
## 11248 11248 shortage fear
## 11249 11249 shortage negative
## 11250 11250 shortage sadness
## 11251 11251 shortcoming negative
## 11252 11252 shortly anticipation
## 11253 11253 shot anger
## 11254 11254 shot fear
## 11255 11255 shot negative
## 11256 11256 shot sadness
## 11257 11257 shot surprise
## 11258 11258 shoulder positive
## 11259 11259 shoulder trust
## 11260 11260 shout anger
## 11261 11261 shout surprise
## 11262 11262 shove anger
## 11263 11263 shove negative
## 11264 11264 show trust
## 11265 11265 showy negative
## 11266 11266 shrapnel fear
## 11267 11267 shrewd positive
## 11268 11268 shriek anger
## 11269 11269 shriek fear
## 11270 11270 shriek negative
## 11271 11271 shriek sadness
## 11272 11272 shriek surprise
## 11273 11273 shrill anger
## 11274 11274 shrill fear
## 11275 11275 shrill negative
## 11276 11276 shrill surprise
## 11277 11277 shrink fear
## 11278 11278 shrink negative
## 11279 11279 shrink sadness
## 11280 11280 shroud sadness
## 11281 11281 shrunk negative
## 11282 11282 shudder fear
## 11283 11283 shudder negative
## 11284 11284 shun anger
## 11285 11285 shun disgust
## 11286 11286 shun negative
## 11287 11287 shun sadness
## 11288 11288 sib trust
## 11289 11289 sick disgust
## 11290 11290 sick negative
## 11291 11291 sick sadness
## 11292 11292 sickening anger
## 11293 11293 sickening disgust
## 11294 11294 sickening fear
## 11295 11295 sickening negative
## 11296 11296 sickening sadness
## 11297 11297 sickly disgust
## 11298 11298 sickly negative
## 11299 11299 sickly sadness
## 11300 11300 sickness disgust
## 11301 11301 sickness fear
## 11302 11302 sickness negative
## 11303 11303 sickness sadness
## 11304 11304 signature trust
## 11305 11305 signify anticipation
## 11306 11306 silk positive
## 11307 11307 silly joy
## 11308 11308 silly negative
## 11309 11309 simmer anger
## 11310 11310 simmer anticipation
## 11311 11311 simmering anticipation
## 11312 11312 simplify anticipation
## 11313 11313 simplify joy
## 11314 11314 simplify positive
## 11315 11315 simplify surprise
## 11316 11316 simplify trust
## 11317 11317 sin anger
## 11318 11318 sin disgust
## 11319 11319 sin fear
## 11320 11320 sin negative
## 11321 11321 sin sadness
## 11322 11322 sincere positive
## 11323 11323 sincere trust
## 11324 11324 sincerity positive
## 11325 11325 sinful anger
## 11326 11326 sinful disgust
## 11327 11327 sinful fear
## 11328 11328 sinful negative
## 11329 11329 sinful sadness
## 11330 11330 sing anticipation
## 11331 11331 sing joy
## 11332 11332 sing positive
## 11333 11333 sing sadness
## 11334 11334 sing trust
## 11335 11335 singly positive
## 11336 11336 singularly surprise
## 11337 11337 sinister anger
## 11338 11338 sinister disgust
## 11339 11339 sinister fear
## 11340 11340 sinister negative
## 11341 11341 sinner anger
## 11342 11342 sinner disgust
## 11343 11343 sinner fear
## 11344 11344 sinner negative
## 11345 11345 sinner sadness
## 11346 11346 sinning disgust
## 11347 11347 sinning negative
## 11348 11348 sir positive
## 11349 11349 sir trust
## 11350 11350 siren fear
## 11351 11351 siren negative
## 11352 11352 sissy negative
## 11353 11353 sisterhood anger
## 11354 11354 sisterhood positive
## 11355 11355 sisterhood sadness
## 11356 11356 sisterhood surprise
## 11357 11357 sisterhood trust
## 11358 11358 sizzle anger
## 11359 11359 skeptical negative
## 11360 11360 sketchy negative
## 11361 11361 skewed anger
## 11362 11362 skewed anticipation
## 11363 11363 skewed negative
## 11364 11364 skid anger
## 11365 11365 skid fear
## 11366 11366 skid negative
## 11367 11367 skid sadness
## 11368 11368 skilled positive
## 11369 11369 skillful positive
## 11370 11370 skillful trust
## 11371 11371 skip negative
## 11372 11372 skirmish anger
## 11373 11373 skirmish negative
## 11374 11374 sky positive
## 11375 11375 slack negative
## 11376 11376 slag negative
## 11377 11377 slam anger
## 11378 11378 slam fear
## 11379 11379 slam negative
## 11380 11380 slam surprise
## 11381 11381 slander anger
## 11382 11382 slander disgust
## 11383 11383 slander negative
## 11384 11384 slanderous negative
## 11385 11385 slap anger
## 11386 11386 slap negative
## 11387 11387 slap surprise
## 11388 11388 slash anger
## 11389 11389 slate positive
## 11390 11390 slaughter anger
## 11391 11391 slaughter disgust
## 11392 11392 slaughter fear
## 11393 11393 slaughter negative
## 11394 11394 slaughter sadness
## 11395 11395 slaughter surprise
## 11396 11396 slaughterhouse anger
## 11397 11397 slaughterhouse disgust
## 11398 11398 slaughterhouse fear
## 11399 11399 slaughterhouse negative
## 11400 11400 slaughterhouse sadness
## 11401 11401 slaughtering anger
## 11402 11402 slaughtering disgust
## 11403 11403 slaughtering fear
## 11404 11404 slaughtering negative
## 11405 11405 slaughtering sadness
## 11406 11406 slaughtering surprise
## 11407 11407 slave anger
## 11408 11408 slave fear
## 11409 11409 slave negative
## 11410 11410 slave sadness
## 11411 11411 slavery anger
## 11412 11412 slavery disgust
## 11413 11413 slavery fear
## 11414 11414 slavery negative
## 11415 11415 slavery sadness
## 11416 11416 slay anger
## 11417 11417 slay negative
## 11418 11418 slayer anger
## 11419 11419 slayer disgust
## 11420 11420 slayer fear
## 11421 11421 slayer negative
## 11422 11422 slayer sadness
## 11423 11423 slayer surprise
## 11424 11424 sleek positive
## 11425 11425 sleet negative
## 11426 11426 slender positive
## 11427 11427 slim positive
## 11428 11428 slime disgust
## 11429 11429 slimy disgust
## 11430 11430 slimy negative
## 11431 11431 slink negative
## 11432 11432 slip negative
## 11433 11433 slip surprise
## 11434 11434 slop disgust
## 11435 11435 slop negative
## 11436 11436 sloppy disgust
## 11437 11437 sloppy negative
## 11438 11438 sloth disgust
## 11439 11439 sloth negative
## 11440 11440 slouch negative
## 11441 11441 slough negative
## 11442 11442 slowness negative
## 11443 11443 sludge disgust
## 11444 11444 sludge negative
## 11445 11445 slug disgust
## 11446 11446 slug negative
## 11447 11447 sluggish negative
## 11448 11448 sluggish sadness
## 11449 11449 slum disgust
## 11450 11450 slum negative
## 11451 11451 slump negative
## 11452 11452 slump sadness
## 11453 11453 slur anger
## 11454 11454 slur disgust
## 11455 11455 slur negative
## 11456 11456 slur sadness
## 11457 11457 slush disgust
## 11458 11458 slush negative
## 11459 11459 slush surprise
## 11460 11460 slut anger
## 11461 11461 slut disgust
## 11462 11462 slut negative
## 11463 11463 sly anger
## 11464 11464 sly disgust
## 11465 11465 sly fear
## 11466 11466 sly negative
## 11467 11467 smack anger
## 11468 11468 smack negative
## 11469 11469 small negative
## 11470 11470 smash anger
## 11471 11471 smash fear
## 11472 11472 smash negative
## 11473 11473 smashed negative
## 11474 11474 smattering negative
## 11475 11475 smell anger
## 11476 11476 smell disgust
## 11477 11477 smell negative
## 11478 11478 smelling disgust
## 11479 11479 smelling negative
## 11480 11480 smile joy
## 11481 11481 smile positive
## 11482 11482 smile surprise
## 11483 11483 smile trust
## 11484 11484 smiling joy
## 11485 11485 smiling positive
## 11486 11486 smirk negative
## 11487 11487 smite anger
## 11488 11488 smite fear
## 11489 11489 smite negative
## 11490 11490 smite sadness
## 11491 11491 smith trust
## 11492 11492 smitten positive
## 11493 11493 smoker negative
## 11494 11494 smoothness positive
## 11495 11495 smother anger
## 11496 11496 smother negative
## 11497 11497 smudge negative
## 11498 11498 smug negative
## 11499 11499 smuggle fear
## 11500 11500 smuggle negative
## 11501 11501 smuggler anger
## 11502 11502 smuggler disgust
## 11503 11503 smuggler fear
## 11504 11504 smuggler negative
## 11505 11505 smuggling negative
## 11506 11506 smut disgust
## 11507 11507 smut fear
## 11508 11508 smut negative
## 11509 11509 snag negative
## 11510 11510 snag surprise
## 11511 11511 snags negative
## 11512 11512 snake disgust
## 11513 11513 snake fear
## 11514 11514 snake negative
## 11515 11515 snare fear
## 11516 11516 snare negative
## 11517 11517 snarl anger
## 11518 11518 snarl disgust
## 11519 11519 snarl negative
## 11520 11520 snarling anger
## 11521 11521 snarling negative
## 11522 11522 sneak anger
## 11523 11523 sneak fear
## 11524 11524 sneak negative
## 11525 11525 sneak surprise
## 11526 11526 sneaking anticipation
## 11527 11527 sneaking fear
## 11528 11528 sneaking negative
## 11529 11529 sneaking trust
## 11530 11530 sneer anger
## 11531 11531 sneer disgust
## 11532 11532 sneer negative
## 11533 11533 sneeze disgust
## 11534 11534 sneeze negative
## 11535 11535 sneeze surprise
## 11536 11536 snicker positive
## 11537 11537 snide negative
## 11538 11538 snob negative
## 11539 11539 snort sadness
## 11540 11540 soak negative
## 11541 11541 sob negative
## 11542 11542 sob sadness
## 11543 11543 sobriety positive
## 11544 11544 sobriety trust
## 11545 11545 sociable positive
## 11546 11546 socialism disgust
## 11547 11547 socialism fear
## 11548 11548 socialist anger
## 11549 11549 socialist disgust
## 11550 11550 socialist fear
## 11551 11551 socialist negative
## 11552 11552 socialist sadness
## 11553 11553 soil disgust
## 11554 11554 soil negative
## 11555 11555 soiled disgust
## 11556 11556 soiled negative
## 11557 11557 solace positive
## 11558 11558 soldier anger
## 11559 11559 soldier positive
## 11560 11560 soldier sadness
## 11561 11561 solid positive
## 11562 11562 solidarity trust
## 11563 11563 solidity positive
## 11564 11564 solidity trust
## 11565 11565 solution positive
## 11566 11566 solvency positive
## 11567 11567 somatic negative
## 11568 11568 somatic surprise
## 11569 11569 somber negative
## 11570 11570 somber sadness
## 11571 11571 sonar anticipation
## 11572 11572 sonar positive
## 11573 11573 sonata positive
## 11574 11574 sonnet joy
## 11575 11575 sonnet positive
## 11576 11576 sonnet sadness
## 11577 11577 sonorous joy
## 11578 11578 sonorous positive
## 11579 11579 soot disgust
## 11580 11580 soot negative
## 11581 11581 soothe positive
## 11582 11582 soothing joy
## 11583 11583 soothing positive
## 11584 11584 soothing trust
## 11585 11585 sorcery anticipation
## 11586 11586 sorcery fear
## 11587 11587 sorcery negative
## 11588 11588 sorcery surprise
## 11589 11589 sordid anger
## 11590 11590 sordid disgust
## 11591 11591 sordid fear
## 11592 11592 sordid negative
## 11593 11593 sordid sadness
## 11594 11594 sore anger
## 11595 11595 sore negative
## 11596 11596 sore sadness
## 11597 11597 sorely negative
## 11598 11598 sorely sadness
## 11599 11599 soreness disgust
## 11600 11600 soreness negative
## 11601 11601 soreness sadness
## 11602 11602 sorrow fear
## 11603 11603 sorrow negative
## 11604 11604 sorrow sadness
## 11605 11605 sorrowful negative
## 11606 11606 sorrowful sadness
## 11607 11607 sorter positive
## 11608 11608 sortie fear
## 11609 11609 sortie negative
## 11610 11610 soulless disgust
## 11611 11611 soulless fear
## 11612 11612 soulless negative
## 11613 11613 soulless sadness
## 11614 11614 soulmate fear
## 11615 11615 soulmate negative
## 11616 11616 soundness anticipation
## 11617 11617 soundness joy
## 11618 11618 soundness positive
## 11619 11619 soundness trust
## 11620 11620 soup positive
## 11621 11621 sour disgust
## 11622 11622 sour negative
## 11623 11623 sovereign trust
## 11624 11624 spa anticipation
## 11625 11625 spa joy
## 11626 11626 spa positive
## 11627 11627 spa surprise
## 11628 11628 spa trust
## 11629 11629 spacious positive
## 11630 11630 spaniel joy
## 11631 11631 spaniel positive
## 11632 11632 spaniel trust
## 11633 11633 spank anger
## 11634 11634 spank fear
## 11635 11635 spank negative
## 11636 11636 spank sadness
## 11637 11637 spanking anger
## 11638 11638 sparkle anticipation
## 11639 11639 sparkle joy
## 11640 11640 sparkle positive
## 11641 11641 sparkle surprise
## 11642 11642 spasm fear
## 11643 11643 spasm negative
## 11644 11644 spat anger
## 11645 11645 spat negative
## 11646 11646 spear anger
## 11647 11647 spear anticipation
## 11648 11648 spear fear
## 11649 11649 spear negative
## 11650 11650 special joy
## 11651 11651 special positive
## 11652 11652 specialist trust
## 11653 11653 specialize trust
## 11654 11654 specie positive
## 11655 11655 speck disgust
## 11656 11656 speck negative
## 11657 11657 spectacle negative
## 11658 11658 spectacle positive
## 11659 11659 spectacular anticipation
## 11660 11660 spectacular surprise
## 11661 11661 specter fear
## 11662 11662 specter negative
## 11663 11663 specter sadness
## 11664 11664 spectral negative
## 11665 11665 speculation fear
## 11666 11666 speculation negative
## 11667 11667 speculation sadness
## 11668 11668 speculative anticipation
## 11669 11669 speech positive
## 11670 11670 speedy positive
## 11671 11671 spelling positive
## 11672 11672 spent negative
## 11673 11673 spew disgust
## 11674 11674 spice positive
## 11675 11675 spider disgust
## 11676 11676 spider fear
## 11677 11677 spike fear
## 11678 11678 spine anger
## 11679 11679 spine negative
## 11680 11680 spine positive
## 11681 11681 spinster fear
## 11682 11682 spinster negative
## 11683 11683 spinster sadness
## 11684 11684 spirit positive
## 11685 11685 spirits anticipation
## 11686 11686 spirits joy
## 11687 11687 spirits positive
## 11688 11688 spirits surprise
## 11689 11689 spit disgust
## 11690 11690 spite anger
## 11691 11691 spite negative
## 11692 11692 spiteful anger
## 11693 11693 spiteful negative
## 11694 11694 splash surprise
## 11695 11695 splendid joy
## 11696 11696 splendid positive
## 11697 11697 splendid surprise
## 11698 11698 splendor anticipation
## 11699 11699 splendor joy
## 11700 11700 splendor positive
## 11701 11701 splendor surprise
## 11702 11702 splinter negative
## 11703 11703 split negative
## 11704 11704 splitting negative
## 11705 11705 splitting sadness
## 11706 11706 spoil disgust
## 11707 11707 spoil negative
## 11708 11708 spoiler negative
## 11709 11709 spoiler sadness
## 11710 11710 spoke negative
## 11711 11711 spokesman trust
## 11712 11712 sponge negative
## 11713 11713 sponsor positive
## 11714 11714 sponsor trust
## 11715 11715 spook fear
## 11716 11716 spook negative
## 11717 11717 spotless positive
## 11718 11718 spotless trust
## 11719 11719 spouse joy
## 11720 11720 spouse positive
## 11721 11721 spouse trust
## 11722 11722 sprain negative
## 11723 11723 sprain sadness
## 11724 11724 spree negative
## 11725 11725 sprite fear
## 11726 11726 sprite negative
## 11727 11727 spruce positive
## 11728 11728 spur fear
## 11729 11729 spurious disgust
## 11730 11730 spurious negative
## 11731 11731 squall fear
## 11732 11732 squall negative
## 11733 11733 squall sadness
## 11734 11734 squatter negative
## 11735 11735 squeamish disgust
## 11736 11736 squeamish fear
## 11737 11737 squeamish negative
## 11738 11738 squelch anger
## 11739 11739 squelch disgust
## 11740 11740 squelch negative
## 11741 11741 squirm disgust
## 11742 11742 squirm negative
## 11743 11743 stab anger
## 11744 11744 stab fear
## 11745 11745 stab negative
## 11746 11746 stab sadness
## 11747 11747 stab surprise
## 11748 11748 stable positive
## 11749 11749 stable trust
## 11750 11750 staccato positive
## 11751 11751 stagger surprise
## 11752 11752 staggering negative
## 11753 11753 stagnant negative
## 11754 11754 stagnant sadness
## 11755 11755 stain disgust
## 11756 11756 stain negative
## 11757 11757 stainless positive
## 11758 11758 stale negative
## 11759 11759 stalemate anger
## 11760 11760 stalemate disgust
## 11761 11761 stalk fear
## 11762 11762 stalk negative
## 11763 11763 stall disgust
## 11764 11764 stallion positive
## 11765 11765 stalwart positive
## 11766 11766 stamina positive
## 11767 11767 stamina trust
## 11768 11768 standing positive
## 11769 11769 standoff anger
## 11770 11770 standoff fear
## 11771 11771 standoff negative
## 11772 11772 standstill anger
## 11773 11773 standstill negative
## 11774 11774 star anticipation
## 11775 11775 star joy
## 11776 11776 star positive
## 11777 11777 star trust
## 11778 11778 staring negative
## 11779 11779 stark negative
## 11780 11780 stark trust
## 11781 11781 starlight positive
## 11782 11782 starry anticipation
## 11783 11783 starry joy
## 11784 11784 starry positive
## 11785 11785 start anticipation
## 11786 11786 startle fear
## 11787 11787 startle negative
## 11788 11788 startle surprise
## 11789 11789 startling surprise
## 11790 11790 starvation fear
## 11791 11791 starvation negative
## 11792 11792 starvation sadness
## 11793 11793 starved negative
## 11794 11794 starving negative
## 11795 11795 stately positive
## 11796 11796 statement positive
## 11797 11797 statement trust
## 11798 11798 stationary negative
## 11799 11799 statistical trust
## 11800 11800 statue positive
## 11801 11801 status positive
## 11802 11802 staunch positive
## 11803 11803 stave negative
## 11804 11804 steadfast positive
## 11805 11805 steadfast trust
## 11806 11806 steady surprise
## 11807 11807 steady trust
## 11808 11808 steal anger
## 11809 11809 steal fear
## 11810 11810 steal negative
## 11811 11811 steal sadness
## 11812 11812 stealing disgust
## 11813 11813 stealing fear
## 11814 11814 stealing negative
## 11815 11815 stealth surprise
## 11816 11816 stealthily surprise
## 11817 11817 stealthy anticipation
## 11818 11818 stealthy fear
## 11819 11819 stealthy negative
## 11820 11820 stealthy surprise
## 11821 11821 stellar positive
## 11822 11822 stereotype negative
## 11823 11823 stereotyped negative
## 11824 11824 sterile negative
## 11825 11825 sterile sadness
## 11826 11826 sterility negative
## 11827 11827 sterling anger
## 11828 11828 sterling anticipation
## 11829 11829 sterling joy
## 11830 11830 sterling negative
## 11831 11831 sterling positive
## 11832 11832 sterling trust
## 11833 11833 stern negative
## 11834 11834 steward positive
## 11835 11835 steward trust
## 11836 11836 sticky disgust
## 11837 11837 stiff negative
## 11838 11838 stiffness negative
## 11839 11839 stifle negative
## 11840 11840 stifled anger
## 11841 11841 stifled fear
## 11842 11842 stifled negative
## 11843 11843 stifled sadness
## 11844 11844 stigma anger
## 11845 11845 stigma disgust
## 11846 11846 stigma fear
## 11847 11847 stigma negative
## 11848 11848 stigma sadness
## 11849 11849 stillborn negative
## 11850 11850 stillborn sadness
## 11851 11851 stillness fear
## 11852 11852 stillness positive
## 11853 11853 stillness sadness
## 11854 11854 sting anger
## 11855 11855 sting fear
## 11856 11856 sting negative
## 11857 11857 stinging negative
## 11858 11858 stingy anger
## 11859 11859 stingy disgust
## 11860 11860 stingy fear
## 11861 11861 stingy negative
## 11862 11862 stingy sadness
## 11863 11863 stink disgust
## 11864 11864 stink negative
## 11865 11865 stinking disgust
## 11866 11866 stinking negative
## 11867 11867 stint fear
## 11868 11868 stint negative
## 11869 11869 stint sadness
## 11870 11870 stocks negative
## 11871 11871 stolen anger
## 11872 11872 stolen negative
## 11873 11873 stomach disgust
## 11874 11874 stone anger
## 11875 11875 stone negative
## 11876 11876 stoned negative
## 11877 11877 stools disgust
## 11878 11878 stools negative
## 11879 11879 stoppage negative
## 11880 11880 store anticipation
## 11881 11881 store positive
## 11882 11882 storm anger
## 11883 11883 storm negative
## 11884 11884 storming anger
## 11885 11885 stormy fear
## 11886 11886 stormy negative
## 11887 11887 straightforward positive
## 11888 11888 straightforward trust
## 11889 11889 strained anger
## 11890 11890 strained negative
## 11891 11891 straits fear
## 11892 11892 straits negative
## 11893 11893 stranded negative
## 11894 11894 stranger fear
## 11895 11895 stranger negative
## 11896 11896 strangle anger
## 11897 11897 strangle disgust
## 11898 11898 strangle fear
## 11899 11899 strangle negative
## 11900 11900 strangle sadness
## 11901 11901 strangle surprise
## 11902 11902 strategic positive
## 11903 11903 strategist anticipation
## 11904 11904 strategist positive
## 11905 11905 strategist trust
## 11906 11906 stray negative
## 11907 11907 strength positive
## 11908 11908 strength trust
## 11909 11909 strengthen positive
## 11910 11910 strengthening joy
## 11911 11911 strengthening positive
## 11912 11912 strengthening trust
## 11913 11913 stress negative
## 11914 11914 stretcher fear
## 11915 11915 stretcher sadness
## 11916 11916 stricken sadness
## 11917 11917 strife anger
## 11918 11918 strife negative
## 11919 11919 strike anger
## 11920 11920 strike negative
## 11921 11921 striking positive
## 11922 11922 strikingly positive
## 11923 11923 strip negative
## 11924 11924 strip sadness
## 11925 11925 stripe negative
## 11926 11926 stripped anger
## 11927 11927 stripped anticipation
## 11928 11928 stripped disgust
## 11929 11929 stripped fear
## 11930 11930 stripped negative
## 11931 11931 stripped sadness
## 11932 11932 strive anticipation
## 11933 11933 stroke fear
## 11934 11934 stroke negative
## 11935 11935 stroke sadness
## 11936 11936 strongly positive
## 11937 11937 structural trust
## 11938 11938 structure positive
## 11939 11939 structure trust
## 11940 11940 struggle anger
## 11941 11941 struggle fear
## 11942 11942 struggle negative
## 11943 11943 struggle sadness
## 11944 11944 strut negative
## 11945 11945 stud positive
## 11946 11946 study positive
## 11947 11947 stuffy negative
## 11948 11948 stumble negative
## 11949 11949 stunned fear
## 11950 11950 stunned negative
## 11951 11951 stunned surprise
## 11952 11952 stunted negative
## 11953 11953 stupid negative
## 11954 11954 stupidity negative
## 11955 11955 stupor negative
## 11956 11956 sturdy positive
## 11957 11957 sty disgust
## 11958 11958 sty negative
## 11959 11959 subdue negative
## 11960 11960 subito surprise
## 11961 11961 subject negative
## 11962 11962 subjected negative
## 11963 11963 subjected sadness
## 11964 11964 subjection negative
## 11965 11965 subjugation anger
## 11966 11966 subjugation disgust
## 11967 11967 subjugation fear
## 11968 11968 subjugation negative
## 11969 11969 subjugation sadness
## 11970 11970 sublimation joy
## 11971 11971 sublimation positive
## 11972 11972 submit anticipation
## 11973 11973 subordinate fear
## 11974 11974 subordinate negative
## 11975 11975 subpoena negative
## 11976 11976 subscribe anticipation
## 11977 11977 subsidence negative
## 11978 11978 subsidence sadness
## 11979 11979 subsidy anger
## 11980 11980 subsidy disgust
## 11981 11981 subsidy negative
## 11982 11982 subsist negative
## 11983 11983 substance positive
## 11984 11984 substantiate trust
## 11985 11985 substantive positive
## 11986 11986 subtract negative
## 11987 11987 subversion anger
## 11988 11988 subversion fear
## 11989 11989 subversion negative
## 11990 11990 subversive anger
## 11991 11991 subversive negative
## 11992 11992 subversive surprise
## 11993 11993 subvert disgust
## 11994 11994 subvert fear
## 11995 11995 subvert negative
## 11996 11996 subvert sadness
## 11997 11997 succeed anticipation
## 11998 11998 succeed joy
## 11999 11999 succeed positive
## 12000 12000 succeed surprise
## 12001 12001 succeed trust
## 12002 12002 succeeding anticipation
## 12003 12003 succeeding joy
## 12004 12004 succeeding positive
## 12005 12005 succeeding trust
## 12006 12006 success anticipation
## 12007 12007 success joy
## 12008 12008 success positive
## 12009 12009 successful anticipation
## 12010 12010 successful joy
## 12011 12011 successful positive
## 12012 12012 successful trust
## 12013 12013 succinct positive
## 12014 12014 succulent negative
## 12015 12015 succulent positive
## 12016 12016 succumb negative
## 12017 12017 suck negative
## 12018 12018 sucker anger
## 12019 12019 sucker negative
## 12020 12020 sudden surprise
## 12021 12021 suddenly surprise
## 12022 12022 sue anger
## 12023 12023 sue negative
## 12024 12024 sue sadness
## 12025 12025 suffer negative
## 12026 12026 sufferer fear
## 12027 12027 sufferer negative
## 12028 12028 sufferer sadness
## 12029 12029 suffering disgust
## 12030 12030 suffering fear
## 12031 12031 suffering negative
## 12032 12032 suffering sadness
## 12033 12033 sufficiency positive
## 12034 12034 suffocating disgust
## 12035 12035 suffocating fear
## 12036 12036 suffocating negative
## 12037 12037 suffocating sadness
## 12038 12038 suffocation anger
## 12039 12039 suffocation fear
## 12040 12040 suffocation negative
## 12041 12041 sugar positive
## 12042 12042 suggest trust
## 12043 12043 suicidal anger
## 12044 12044 suicidal disgust
## 12045 12045 suicidal fear
## 12046 12046 suicidal negative
## 12047 12047 suicidal sadness
## 12048 12048 suicide anger
## 12049 12049 suicide fear
## 12050 12050 suicide negative
## 12051 12051 suicide sadness
## 12052 12052 suitable positive
## 12053 12053 sullen anger
## 12054 12054 sullen negative
## 12055 12055 sullen sadness
## 12056 12056 sultan fear
## 12057 12057 sultry positive
## 12058 12058 summons negative
## 12059 12059 sump disgust
## 12060 12060 sun anticipation
## 12061 12061 sun joy
## 12062 12062 sun positive
## 12063 12063 sun surprise
## 12064 12064 sun trust
## 12065 12065 sundial anticipation
## 12066 12066 sundial trust
## 12067 12067 sunk disgust
## 12068 12068 sunk fear
## 12069 12069 sunk negative
## 12070 12070 sunk sadness
## 12071 12071 sunny anticipation
## 12072 12072 sunny joy
## 12073 12073 sunny positive
## 12074 12074 sunny surprise
## 12075 12075 sunset anticipation
## 12076 12076 sunset positive
## 12077 12077 sunshine joy
## 12078 12078 sunshine positive
## 12079 12079 superb positive
## 12080 12080 superficial negative
## 12081 12081 superfluous negative
## 12082 12082 superhuman positive
## 12083 12083 superior positive
## 12084 12084 superiority positive
## 12085 12085 superman joy
## 12086 12086 superman positive
## 12087 12087 superman trust
## 12088 12088 superstar joy
## 12089 12089 superstar positive
## 12090 12090 superstar trust
## 12091 12091 superstition fear
## 12092 12092 superstition negative
## 12093 12093 superstition positive
## 12094 12094 superstitious anticipation
## 12095 12095 superstitious fear
## 12096 12096 superstitious negative
## 12097 12097 supplication positive
## 12098 12098 supplication trust
## 12099 12099 supplies positive
## 12100 12100 supply positive
## 12101 12101 supported positive
## 12102 12102 supporter joy
## 12103 12103 supporter positive
## 12104 12104 supporter trust
## 12105 12105 supporting positive
## 12106 12106 supporting trust
## 12107 12107 suppress anger
## 12108 12108 suppress fear
## 12109 12109 suppress negative
## 12110 12110 suppress sadness
## 12111 12111 suppression anger
## 12112 12112 suppression disgust
## 12113 12113 suppression fear
## 12114 12114 suppression negative
## 12115 12115 supremacy anger
## 12116 12116 supremacy anticipation
## 12117 12117 supremacy fear
## 12118 12118 supremacy joy
## 12119 12119 supremacy negative
## 12120 12120 supremacy positive
## 12121 12121 supremacy surprise
## 12122 12122 supremacy trust
## 12123 12123 supreme positive
## 12124 12124 supremely positive
## 12125 12125 surcharge anger
## 12126 12126 surcharge negative
## 12127 12127 surety positive
## 12128 12128 surety trust
## 12129 12129 surge surprise
## 12130 12130 surgery fear
## 12131 12131 surgery sadness
## 12132 12132 surly anger
## 12133 12133 surly disgust
## 12134 12134 surly negative
## 12135 12135 surmise positive
## 12136 12136 surpassing positive
## 12137 12137 surprise fear
## 12138 12138 surprise joy
## 12139 12139 surprise positive
## 12140 12140 surprise surprise
## 12141 12141 surprised surprise
## 12142 12142 surprising surprise
## 12143 12143 surprisingly anticipation
## 12144 12144 surprisingly surprise
## 12145 12145 surrender fear
## 12146 12146 surrender negative
## 12147 12147 surrender sadness
## 12148 12148 surrendering negative
## 12149 12149 surrendering sadness
## 12150 12150 surrogate trust
## 12151 12151 surround anticipation
## 12152 12152 surround negative
## 12153 12153 surround positive
## 12154 12154 surveillance fear
## 12155 12155 surveying positive
## 12156 12156 survive positive
## 12157 12157 susceptible negative
## 12158 12158 suspect fear
## 12159 12159 suspect negative
## 12160 12160 suspense anticipation
## 12161 12161 suspense fear
## 12162 12162 suspense surprise
## 12163 12163 suspension fear
## 12164 12164 suspension negative
## 12165 12165 suspicion fear
## 12166 12166 suspicion negative
## 12167 12167 suspicious anger
## 12168 12168 suspicious anticipation
## 12169 12169 suspicious negative
## 12170 12170 swab negative
## 12171 12171 swamp disgust
## 12172 12172 swamp fear
## 12173 12173 swamp negative
## 12174 12174 swampy disgust
## 12175 12175 swampy fear
## 12176 12176 swampy negative
## 12177 12177 swarm disgust
## 12178 12178 swastika anger
## 12179 12179 swastika fear
## 12180 12180 swastika negative
## 12181 12181 swear positive
## 12182 12182 swear trust
## 12183 12183 sweat fear
## 12184 12184 sweet anticipation
## 12185 12185 sweet joy
## 12186 12186 sweet positive
## 12187 12187 sweet surprise
## 12188 12188 sweet trust
## 12189 12189 sweetheart anticipation
## 12190 12190 sweetheart joy
## 12191 12191 sweetheart positive
## 12192 12192 sweetheart sadness
## 12193 12193 sweetheart trust
## 12194 12194 sweetie positive
## 12195 12195 sweetness positive
## 12196 12196 sweets anticipation
## 12197 12197 sweets joy
## 12198 12198 sweets positive
## 12199 12199 swelling fear
## 12200 12200 swelling negative
## 12201 12201 swerve fear
## 12202 12202 swerve surprise
## 12203 12203 swift positive
## 12204 12204 swig disgust
## 12205 12205 swig negative
## 12206 12206 swim anticipation
## 12207 12207 swim fear
## 12208 12208 swim joy
## 12209 12209 swim positive
## 12210 12210 swine disgust
## 12211 12211 swine negative
## 12212 12212 swollen negative
## 12213 12213 symbolic positive
## 12214 12214 symmetrical positive
## 12215 12215 symmetry joy
## 12216 12216 symmetry positive
## 12217 12217 symmetry trust
## 12218 12218 sympathetic fear
## 12219 12219 sympathetic joy
## 12220 12220 sympathetic positive
## 12221 12221 sympathetic sadness
## 12222 12222 sympathetic trust
## 12223 12223 sympathize sadness
## 12224 12224 sympathy positive
## 12225 12225 sympathy sadness
## 12226 12226 symphony anticipation
## 12227 12227 symphony joy
## 12228 12228 symphony positive
## 12229 12229 symptom negative
## 12230 12230 synchronize anticipation
## 12231 12231 synchronize joy
## 12232 12232 synchronize positive
## 12233 12233 synchronize surprise
## 12234 12234 synchronize trust
## 12235 12235 syncope fear
## 12236 12236 syncope negative
## 12237 12237 syncope sadness
## 12238 12238 syncope surprise
## 12239 12239 synergistic positive
## 12240 12240 synergistic trust
## 12241 12241 synod positive
## 12242 12242 synod trust
## 12243 12243 synonymous fear
## 12244 12244 synonymous negative
## 12245 12245 synonymous positive
## 12246 12246 synonymous trust
## 12247 12247 syringe fear
## 12248 12248 system trust
## 12249 12249 taboo disgust
## 12250 12250 taboo fear
## 12251 12251 taboo negative
## 12252 12252 tabulate anticipation
## 12253 12253 tackle anger
## 12254 12254 tackle surprise
## 12255 12255 tact positive
## 12256 12256 tactics fear
## 12257 12257 tactics trust
## 12258 12258 taint negative
## 12259 12259 taint sadness
## 12260 12260 tale positive
## 12261 12261 talent positive
## 12262 12262 talisman positive
## 12263 12263 talk positive
## 12264 12264 talons anger
## 12265 12265 talons fear
## 12266 12266 talons negative
## 12267 12267 tandem trust
## 12268 12268 tangled negative
## 12269 12269 tanned positive
## 12270 12270 tantalizing anticipation
## 12271 12271 tantalizing joy
## 12272 12272 tantalizing negative
## 12273 12273 tantalizing positive
## 12274 12274 tantalizing surprise
## 12275 12275 tantamount trust
## 12276 12276 tardiness negative
## 12277 12277 tardy negative
## 12278 12278 tariff anger
## 12279 12279 tariff disgust
## 12280 12280 tariff negative
## 12281 12281 tarnish disgust
## 12282 12282 tarnish negative
## 12283 12283 tarnish sadness
## 12284 12284 tarry negative
## 12285 12285 task positive
## 12286 12286 tasteful positive
## 12287 12287 tasteless disgust
## 12288 12288 tasteless negative
## 12289 12289 tasty positive
## 12290 12290 taught trust
## 12291 12291 taunt anger
## 12292 12292 taunt fear
## 12293 12293 taunt negative
## 12294 12294 taunt sadness
## 12295 12295 tawny disgust
## 12296 12296 tax negative
## 12297 12297 tax sadness
## 12298 12298 teach joy
## 12299 12299 teach positive
## 12300 12300 teach surprise
## 12301 12301 teach trust
## 12302 12302 teacher positive
## 12303 12303 teacher trust
## 12304 12304 team trust
## 12305 12305 tearful disgust
## 12306 12306 tearful fear
## 12307 12307 tearful sadness
## 12308 12308 tease anger
## 12309 12309 tease anticipation
## 12310 12310 tease negative
## 12311 12311 tease sadness
## 12312 12312 teasing anger
## 12313 12313 teasing fear
## 12314 12314 teasing negative
## 12315 12315 technology positive
## 12316 12316 tedious negative
## 12317 12317 tedium negative
## 12318 12318 teeming disgust
## 12319 12319 teens negative
## 12320 12320 teens positive
## 12321 12321 temperance positive
## 12322 12322 temperate trust
## 12323 12323 tempered positive
## 12324 12324 tempest anger
## 12325 12325 tempest anticipation
## 12326 12326 tempest fear
## 12327 12327 tempest negative
## 12328 12328 tempest sadness
## 12329 12329 tempest surprise
## 12330 12330 temptation negative
## 12331 12331 tenable positive
## 12332 12332 tenacious positive
## 12333 12333 tenacity positive
## 12334 12334 tenancy positive
## 12335 12335 tenant positive
## 12336 12336 tender joy
## 12337 12337 tender positive
## 12338 12338 tender trust
## 12339 12339 tenderness joy
## 12340 12340 tenderness positive
## 12341 12341 tenement negative
## 12342 12342 tension anger
## 12343 12343 terminal fear
## 12344 12344 terminal negative
## 12345 12345 terminal sadness
## 12346 12346 terminate sadness
## 12347 12347 termination negative
## 12348 12348 termination sadness
## 12349 12349 termite disgust
## 12350 12350 termite negative
## 12351 12351 terrible anger
## 12352 12352 terrible disgust
## 12353 12353 terrible fear
## 12354 12354 terrible negative
## 12355 12355 terrible sadness
## 12356 12356 terribly sadness
## 12357 12357 terrific sadness
## 12358 12358 terror fear
## 12359 12359 terror negative
## 12360 12360 terrorism anger
## 12361 12361 terrorism disgust
## 12362 12362 terrorism fear
## 12363 12363 terrorism negative
## 12364 12364 terrorism sadness
## 12365 12365 terrorist anger
## 12366 12366 terrorist disgust
## 12367 12367 terrorist fear
## 12368 12368 terrorist negative
## 12369 12369 terrorist sadness
## 12370 12370 terrorist surprise
## 12371 12371 terrorize anger
## 12372 12372 terrorize fear
## 12373 12373 terrorize negative
## 12374 12374 terrorize sadness
## 12375 12375 testament anticipation
## 12376 12376 testament trust
## 12377 12377 testimony trust
## 12378 12378 tetanus disgust
## 12379 12379 tetanus negative
## 12380 12380 tether negative
## 12381 12381 thankful joy
## 12382 12382 thankful positive
## 12383 12383 thanksgiving joy
## 12384 12384 thanksgiving positive
## 12385 12385 theft anger
## 12386 12386 theft disgust
## 12387 12387 theft fear
## 12388 12388 theft negative
## 12389 12389 theft sadness
## 12390 12390 theism disgust
## 12391 12391 theism negative
## 12392 12392 theocratic anger
## 12393 12393 theocratic fear
## 12394 12394 theocratic negative
## 12395 12395 theocratic sadness
## 12396 12396 theocratic trust
## 12397 12397 theological trust
## 12398 12398 theology anticipation
## 12399 12399 theorem trust
## 12400 12400 theoretical positive
## 12401 12401 theory anticipation
## 12402 12402 theory trust
## 12403 12403 therapeutic joy
## 12404 12404 therapeutic positive
## 12405 12405 therapeutic trust
## 12406 12406 therapeutics positive
## 12407 12407 thermocouple anticipation
## 12408 12408 thermometer trust
## 12409 12409 thief anger
## 12410 12410 thief disgust
## 12411 12411 thief fear
## 12412 12412 thief negative
## 12413 12413 thief sadness
## 12414 12414 thief surprise
## 12415 12415 thinker positive
## 12416 12416 thirst anticipation
## 12417 12417 thirst sadness
## 12418 12418 thirst surprise
## 12419 12419 thirsty negative
## 12420 12420 thirteenth fear
## 12421 12421 thorn negative
## 12422 12422 thorny fear
## 12423 12423 thorny negative
## 12424 12424 thoroughbred positive
## 12425 12425 thought anticipation
## 12426 12426 thoughtful positive
## 12427 12427 thoughtful trust
## 12428 12428 thoughtfulness positive
## 12429 12429 thoughtless anger
## 12430 12430 thoughtless disgust
## 12431 12431 thoughtless negative
## 12432 12432 thrash anger
## 12433 12433 thrash disgust
## 12434 12434 thrash fear
## 12435 12435 thrash negative
## 12436 12436 thrash sadness
## 12437 12437 threat anger
## 12438 12438 threat fear
## 12439 12439 threat negative
## 12440 12440 threaten anger
## 12441 12441 threaten anticipation
## 12442 12442 threaten fear
## 12443 12443 threaten negative
## 12444 12444 threatening anger
## 12445 12445 threatening disgust
## 12446 12446 threatening fear
## 12447 12447 threatening negative
## 12448 12448 thresh anger
## 12449 12449 thresh fear
## 12450 12450 thresh negative
## 12451 12451 thresh sadness
## 12452 12452 thrift disgust
## 12453 12453 thrift positive
## 12454 12454 thrift trust
## 12455 12455 thrill anticipation
## 12456 12456 thrill fear
## 12457 12457 thrill joy
## 12458 12458 thrill positive
## 12459 12459 thrill surprise
## 12460 12460 thrilling anticipation
## 12461 12461 thrilling joy
## 12462 12462 thrilling positive
## 12463 12463 thrilling surprise
## 12464 12464 thriving anticipation
## 12465 12465 thriving joy
## 12466 12466 thriving positive
## 12467 12467 throb fear
## 12468 12468 throb negative
## 12469 12469 throb sadness
## 12470 12470 throne positive
## 12471 12471 throne trust
## 12472 12472 throttle anger
## 12473 12473 throttle negative
## 12474 12474 thug anger
## 12475 12475 thug disgust
## 12476 12476 thug fear
## 12477 12477 thug negative
## 12478 12478 thump anger
## 12479 12479 thump negative
## 12480 12480 thumping fear
## 12481 12481 thundering anger
## 12482 12482 thundering fear
## 12483 12483 thundering negative
## 12484 12484 thwart negative
## 12485 12485 thwart surprise
## 12486 12486 tickle anticipation
## 12487 12487 tickle joy
## 12488 12488 tickle positive
## 12489 12489 tickle surprise
## 12490 12490 tickle trust
## 12491 12491 tiff anger
## 12492 12492 tiff negative
## 12493 12493 tighten anger
## 12494 12494 tiling positive
## 12495 12495 time anticipation
## 12496 12496 timely positive
## 12497 12497 timid fear
## 12498 12498 timid negative
## 12499 12499 timid sadness
## 12500 12500 timidity anticipation
## 12501 12501 timidity fear
## 12502 12502 timidity negative
## 12503 12503 tinsel joy
## 12504 12504 tinsel positive
## 12505 12505 tipsy negative
## 12506 12506 tirade anger
## 12507 12507 tirade disgust
## 12508 12508 tirade negative
## 12509 12509 tired negative
## 12510 12510 tiredness negative
## 12511 12511 tiresome negative
## 12512 12512 tit negative
## 12513 12513 title positive
## 12514 12514 title trust
## 12515 12515 toad disgust
## 12516 12516 toad negative
## 12517 12517 toast joy
## 12518 12518 toast positive
## 12519 12519 tobacco negative
## 12520 12520 toilet disgust
## 12521 12521 toilet negative
## 12522 12522 toils negative
## 12523 12523 tolerant positive
## 12524 12524 tolerate anger
## 12525 12525 tolerate negative
## 12526 12526 tolerate sadness
## 12527 12527 toleration positive
## 12528 12528 tomb sadness
## 12529 12529 tomorrow anticipation
## 12530 12530 toothache fear
## 12531 12531 toothache negative
## 12532 12532 top anticipation
## 12533 12533 top positive
## 12534 12534 top trust
## 12535 12535 topple surprise
## 12536 12536 torment anger
## 12537 12537 torment fear
## 12538 12538 torment negative
## 12539 12539 torment sadness
## 12540 12540 torn negative
## 12541 12541 tornado fear
## 12542 12542 torpedo anger
## 12543 12543 torpedo negative
## 12544 12544 torrent fear
## 12545 12545 torrid negative
## 12546 12546 tort negative
## 12547 12547 tortious anger
## 12548 12548 tortious disgust
## 12549 12549 tortious negative
## 12550 12550 torture anger
## 12551 12551 torture anticipation
## 12552 12552 torture disgust
## 12553 12553 torture fear
## 12554 12554 torture negative
## 12555 12555 torture sadness
## 12556 12556 touched negative
## 12557 12557 touchy anger
## 12558 12558 touchy negative
## 12559 12559 touchy sadness
## 12560 12560 tough negative
## 12561 12561 tough sadness
## 12562 12562 toughness anger
## 12563 12563 toughness fear
## 12564 12564 toughness positive
## 12565 12565 toughness trust
## 12566 12566 tower positive
## 12567 12567 towering anticipation
## 12568 12568 towering fear
## 12569 12569 towering positive
## 12570 12570 toxic disgust
## 12571 12571 toxic negative
## 12572 12572 toxin fear
## 12573 12573 toxin negative
## 12574 12574 track anticipation
## 12575 12575 tract fear
## 12576 12576 trade trust
## 12577 12577 traditional positive
## 12578 12578 tragedy fear
## 12579 12579 tragedy negative
## 12580 12580 tragedy sadness
## 12581 12581 tragic negative
## 12582 12582 trainer trust
## 12583 12583 traitor anger
## 12584 12584 traitor disgust
## 12585 12585 traitor fear
## 12586 12586 traitor negative
## 12587 12587 traitor sadness
## 12588 12588 tramp disgust
## 12589 12589 tramp fear
## 12590 12590 tramp negative
## 12591 12591 tramp sadness
## 12592 12592 trance negative
## 12593 12593 tranquil joy
## 12594 12594 tranquil positive
## 12595 12595 tranquility joy
## 12596 12596 tranquility positive
## 12597 12597 tranquility trust
## 12598 12598 transaction trust
## 12599 12599 transcendence anticipation
## 12600 12600 transcendence joy
## 12601 12601 transcendence positive
## 12602 12602 transcendence surprise
## 12603 12603 transcendence trust
## 12604 12604 transcendental positive
## 12605 12605 transcript trust
## 12606 12606 transgression negative
## 12607 12607 transitional anticipation
## 12608 12608 translation trust
## 12609 12609 trappings negative
## 12610 12610 traps negative
## 12611 12611 trash disgust
## 12612 12612 trash negative
## 12613 12613 trash sadness
## 12614 12614 trashy disgust
## 12615 12615 trashy negative
## 12616 12616 traumatic anger
## 12617 12617 traumatic fear
## 12618 12618 traumatic negative
## 12619 12619 traumatic sadness
## 12620 12620 travail negative
## 12621 12621 traveling positive
## 12622 12622 travesty disgust
## 12623 12623 travesty fear
## 12624 12624 travesty negative
## 12625 12625 travesty sadness
## 12626 12626 treacherous anger
## 12627 12627 treacherous disgust
## 12628 12628 treacherous fear
## 12629 12629 treacherous negative
## 12630 12630 treachery anger
## 12631 12631 treachery fear
## 12632 12632 treachery negative
## 12633 12633 treachery sadness
## 12634 12634 treachery surprise
## 12635 12635 treadmill anticipation
## 12636 12636 treason anger
## 12637 12637 treason disgust
## 12638 12638 treason fear
## 12639 12639 treason negative
## 12640 12640 treason surprise
## 12641 12641 treasure anticipation
## 12642 12642 treasure joy
## 12643 12643 treasure positive
## 12644 12644 treasure trust
## 12645 12645 treasurer trust
## 12646 12646 treat anger
## 12647 12647 treat anticipation
## 12648 12648 treat disgust
## 12649 12649 treat fear
## 12650 12650 treat joy
## 12651 12651 treat negative
## 12652 12652 treat positive
## 12653 12653 treat sadness
## 12654 12654 treat surprise
## 12655 12655 treat trust
## 12656 12656 tree anger
## 12657 12657 tree anticipation
## 12658 12658 tree disgust
## 12659 12659 tree joy
## 12660 12660 tree positive
## 12661 12661 tree surprise
## 12662 12662 tree trust
## 12663 12663 trembling fear
## 12664 12664 trembling negative
## 12665 12665 tremendously positive
## 12666 12666 tremor anger
## 12667 12667 tremor anticipation
## 12668 12668 tremor fear
## 12669 12669 tremor negative
## 12670 12670 tremor sadness
## 12671 12671 trend positive
## 12672 12672 trendy positive
## 12673 12673 trepidation anticipation
## 12674 12674 trepidation fear
## 12675 12675 trepidation negative
## 12676 12676 trepidation surprise
## 12677 12677 trespass anger
## 12678 12678 trespass negative
## 12679 12679 tribe trust
## 12680 12680 tribulation fear
## 12681 12681 tribulation negative
## 12682 12682 tribulation sadness
## 12683 12683 tribunal anticipation
## 12684 12684 tribunal disgust
## 12685 12685 tribunal fear
## 12686 12686 tribunal negative
## 12687 12687 tribunal trust
## 12688 12688 tribune trust
## 12689 12689 tributary anticipation
## 12690 12690 tributary positive
## 12691 12691 tribute positive
## 12692 12692 trick negative
## 12693 12693 trick surprise
## 12694 12694 trickery anger
## 12695 12695 trickery disgust
## 12696 12696 trickery fear
## 12697 12697 trickery negative
## 12698 12698 trickery sadness
## 12699 12699 trickery surprise
## 12700 12700 trifle negative
## 12701 12701 trig positive
## 12702 12702 trip surprise
## 12703 12703 tripping anger
## 12704 12704 tripping negative
## 12705 12705 tripping sadness
## 12706 12706 triumph anticipation
## 12707 12707 triumph joy
## 12708 12708 triumph positive
## 12709 12709 triumphant anticipation
## 12710 12710 triumphant joy
## 12711 12711 triumphant positive
## 12712 12712 triumphant trust
## 12713 12713 troll anger
## 12714 12714 troll fear
## 12715 12715 troll negative
## 12716 12716 trophy anticipation
## 12717 12717 trophy joy
## 12718 12718 trophy positive
## 12719 12719 trophy surprise
## 12720 12720 trophy trust
## 12721 12721 troublesome anger
## 12722 12722 troublesome fear
## 12723 12723 troublesome negative
## 12724 12724 truce joy
## 12725 12725 truce positive
## 12726 12726 truce trust
## 12727 12727 truck trust
## 12728 12728 true joy
## 12729 12729 true positive
## 12730 12730 true trust
## 12731 12731 trump surprise
## 12732 12732 trumpet negative
## 12733 12733 truss trust
## 12734 12734 trust trust
## 12735 12735 trustee trust
## 12736 12736 trusty positive
## 12737 12737 truth positive
## 12738 12738 truth trust
## 12739 12739 truthful trust
## 12740 12740 truthfulness positive
## 12741 12741 truthfulness trust
## 12742 12742 tumble negative
## 12743 12743 tumor fear
## 12744 12744 tumor negative
## 12745 12745 tumour fear
## 12746 12746 tumour negative
## 12747 12747 tumour sadness
## 12748 12748 tumult anger
## 12749 12749 tumult fear
## 12750 12750 tumult negative
## 12751 12751 tumult surprise
## 12752 12752 tumultuous anger
## 12753 12753 tumultuous fear
## 12754 12754 tumultuous negative
## 12755 12755 turbulence anger
## 12756 12756 turbulence fear
## 12757 12757 turbulence negative
## 12758 12758 turbulent fear
## 12759 12759 turbulent negative
## 12760 12760 turmoil anger
## 12761 12761 turmoil fear
## 12762 12762 turmoil negative
## 12763 12763 turmoil sadness
## 12764 12764 tussle anger
## 12765 12765 tutelage positive
## 12766 12766 tutelage trust
## 12767 12767 tutor positive
## 12768 12768 twin positive
## 12769 12769 twinkle anticipation
## 12770 12770 twinkle joy
## 12771 12771 twinkle positive
## 12772 12772 twitch negative
## 12773 12773 typhoon fear
## 12774 12774 typhoon negative
## 12775 12775 tyrannical anger
## 12776 12776 tyrannical disgust
## 12777 12777 tyrannical fear
## 12778 12778 tyrannical negative
## 12779 12779 tyranny fear
## 12780 12780 tyranny negative
## 12781 12781 tyranny sadness
## 12782 12782 tyrant anger
## 12783 12783 tyrant disgust
## 12784 12784 tyrant fear
## 12785 12785 tyrant negative
## 12786 12786 tyrant sadness
## 12787 12787 ugliness disgust
## 12788 12788 ugliness fear
## 12789 12789 ugliness negative
## 12790 12790 ugliness sadness
## 12791 12791 ugly disgust
## 12792 12792 ugly negative
## 12793 12793 ulcer anger
## 12794 12794 ulcer disgust
## 12795 12795 ulcer fear
## 12796 12796 ulcer negative
## 12797 12797 ulcer sadness
## 12798 12798 ulterior negative
## 12799 12799 ultimate anticipation
## 12800 12800 ultimate sadness
## 12801 12801 ultimately anticipation
## 12802 12802 ultimately positive
## 12803 12803 ultimatum anger
## 12804 12804 ultimatum fear
## 12805 12805 ultimatum negative
## 12806 12806 umpire positive
## 12807 12807 umpire trust
## 12808 12808 unable negative
## 12809 12809 unable sadness
## 12810 12810 unacceptable negative
## 12811 12811 unacceptable sadness
## 12812 12812 unaccountable anticipation
## 12813 12813 unaccountable disgust
## 12814 12814 unaccountable negative
## 12815 12815 unaccountable sadness
## 12816 12816 unaccountable trust
## 12817 12817 unacknowledged sadness
## 12818 12818 unanimity positive
## 12819 12819 unanimous positive
## 12820 12820 unanticipated surprise
## 12821 12821 unapproved negative
## 12822 12822 unassuming positive
## 12823 12823 unattached negative
## 12824 12824 unattainable anger
## 12825 12825 unattainable negative
## 12826 12826 unattainable sadness
## 12827 12827 unattractive disgust
## 12828 12828 unattractive negative
## 12829 12829 unattractive sadness
## 12830 12830 unauthorized negative
## 12831 12831 unavoidable negative
## 12832 12832 unaware negative
## 12833 12833 unbearable disgust
## 12834 12834 unbearable negative
## 12835 12835 unbearable sadness
## 12836 12836 unbeaten anticipation
## 12837 12837 unbeaten joy
## 12838 12838 unbeaten negative
## 12839 12839 unbeaten positive
## 12840 12840 unbeaten sadness
## 12841 12841 unbeaten surprise
## 12842 12842 unbelief negative
## 12843 12843 unbelievable negative
## 12844 12844 unbiased positive
## 12845 12845 unborn negative
## 12846 12846 unbreakable positive
## 12847 12847 unbridled anger
## 12848 12848 unbridled anticipation
## 12849 12849 unbridled fear
## 12850 12850 unbridled negative
## 12851 12851 unbridled positive
## 12852 12852 unbridled surprise
## 12853 12853 unbroken positive
## 12854 12854 unbroken trust
## 12855 12855 uncanny fear
## 12856 12856 uncanny negative
## 12857 12857 uncanny surprise
## 12858 12858 uncaring anger
## 12859 12859 uncaring disgust
## 12860 12860 uncaring negative
## 12861 12861 uncaring sadness
## 12862 12862 uncertain anger
## 12863 12863 uncertain disgust
## 12864 12864 uncertain fear
## 12865 12865 uncertain negative
## 12866 12866 uncertain surprise
## 12867 12867 unchangeable negative
## 12868 12868 unclean disgust
## 12869 12869 unclean negative
## 12870 12870 uncomfortable negative
## 12871 12871 unconscionable disgust
## 12872 12872 unconscionable negative
## 12873 12873 unconscious negative
## 12874 12874 unconstitutional negative
## 12875 12875 unconstrained joy
## 12876 12876 unconstrained positive
## 12877 12877 uncontrollable anger
## 12878 12878 uncontrollable anticipation
## 12879 12879 uncontrollable negative
## 12880 12880 uncontrollable surprise
## 12881 12881 uncontrolled negative
## 12882 12882 uncover surprise
## 12883 12883 undecided anticipation
## 12884 12884 undecided fear
## 12885 12885 undecided negative
## 12886 12886 underestimate surprise
## 12887 12887 underline positive
## 12888 12888 undermined negative
## 12889 12889 underpaid anger
## 12890 12890 underpaid negative
## 12891 12891 underpaid sadness
## 12892 12892 undersized negative
## 12893 12893 understanding positive
## 12894 12894 understanding trust
## 12895 12895 undertaker sadness
## 12896 12896 undertaking anticipation
## 12897 12897 underwrite positive
## 12898 12898 underwrite trust
## 12899 12899 undesirable anger
## 12900 12900 undesirable disgust
## 12901 12901 undesirable fear
## 12902 12902 undesirable negative
## 12903 12903 undesirable sadness
## 12904 12904 undesired negative
## 12905 12905 undesired sadness
## 12906 12906 undisclosed anticipation
## 12907 12907 undiscovered surprise
## 12908 12908 undivided positive
## 12909 12909 undo negative
## 12910 12910 undoubted anticipation
## 12911 12911 undoubted disgust
## 12912 12912 undying anticipation
## 12913 12913 undying joy
## 12914 12914 undying positive
## 12915 12915 undying sadness
## 12916 12916 undying trust
## 12917 12917 uneasiness anticipation
## 12918 12918 uneasiness negative
## 12919 12919 uneasiness sadness
## 12920 12920 uneasy disgust
## 12921 12921 uneasy fear
## 12922 12922 uneasy negative
## 12923 12923 uneducated negative
## 12924 12924 uneducated sadness
## 12925 12925 unemployed fear
## 12926 12926 unemployed negative
## 12927 12927 unemployed sadness
## 12928 12928 unequal anger
## 12929 12929 unequal disgust
## 12930 12930 unequal fear
## 12931 12931 unequal negative
## 12932 12932 unequal sadness
## 12933 12933 unequivocal trust
## 12934 12934 unequivocally positive
## 12935 12935 uneven negative
## 12936 12936 unexpected anticipation
## 12937 12937 unexpected fear
## 12938 12938 unexpected joy
## 12939 12939 unexpected negative
## 12940 12940 unexpected positive
## 12941 12941 unexpected surprise
## 12942 12942 unexpectedly surprise
## 12943 12943 unexplained anticipation
## 12944 12944 unexplained negative
## 12945 12945 unexplained sadness
## 12946 12946 unfair anger
## 12947 12947 unfair disgust
## 12948 12948 unfair negative
## 12949 12949 unfair sadness
## 12950 12950 unfairness anger
## 12951 12951 unfairness negative
## 12952 12952 unfairness sadness
## 12953 12953 unfaithful disgust
## 12954 12954 unfaithful negative
## 12955 12955 unfavorable disgust
## 12956 12956 unfavorable negative
## 12957 12957 unfavorable sadness
## 12958 12958 unfinished negative
## 12959 12959 unfold anticipation
## 12960 12960 unfold positive
## 12961 12961 unforeseen surprise
## 12962 12962 unforgiving anger
## 12963 12963 unforgiving negative
## 12964 12964 unforgiving sadness
## 12965 12965 unfortunate negative
## 12966 12966 unfortunate sadness
## 12967 12967 unfriendly anger
## 12968 12968 unfriendly disgust
## 12969 12969 unfriendly fear
## 12970 12970 unfriendly negative
## 12971 12971 unfriendly sadness
## 12972 12972 unfulfilled anger
## 12973 12973 unfulfilled anticipation
## 12974 12974 unfulfilled negative
## 12975 12975 unfulfilled sadness
## 12976 12976 unfulfilled surprise
## 12977 12977 unfurnished negative
## 12978 12978 ungodly negative
## 12979 12979 ungodly sadness
## 12980 12980 ungrateful anger
## 12981 12981 ungrateful disgust
## 12982 12982 ungrateful negative
## 12983 12983 unguarded surprise
## 12984 12984 unhappiness negative
## 12985 12985 unhappiness sadness
## 12986 12986 unhappy anger
## 12987 12987 unhappy disgust
## 12988 12988 unhappy negative
## 12989 12989 unhappy sadness
## 12990 12990 unhealthy disgust
## 12991 12991 unhealthy fear
## 12992 12992 unhealthy negative
## 12993 12993 unhealthy sadness
## 12994 12994 unholy fear
## 12995 12995 unholy negative
## 12996 12996 unification anticipation
## 12997 12997 unification joy
## 12998 12998 unification positive
## 12999 12999 unification trust
## 13000 13000 uniformly positive
## 13001 13001 unimaginable negative
## 13002 13002 unimaginable positive
## 13003 13003 unimaginable surprise
## 13004 13004 unimportant negative
## 13005 13005 unimportant sadness
## 13006 13006 unimpressed negative
## 13007 13007 unimproved negative
## 13008 13008 uninfected positive
## 13009 13009 uninformed negative
## 13010 13010 uninitiated negative
## 13011 13011 uninspired negative
## 13012 13012 uninspired sadness
## 13013 13013 unintelligible negative
## 13014 13014 unintended surprise
## 13015 13015 unintentional surprise
## 13016 13016 unintentionally negative
## 13017 13017 unintentionally surprise
## 13018 13018 uninterested negative
## 13019 13019 uninterested sadness
## 13020 13020 uninteresting negative
## 13021 13021 uninteresting sadness
## 13022 13022 uninvited sadness
## 13023 13023 unique positive
## 13024 13024 unique surprise
## 13025 13025 unison positive
## 13026 13026 unitary positive
## 13027 13027 united positive
## 13028 13028 united trust
## 13029 13029 unity positive
## 13030 13030 unity trust
## 13031 13031 university anticipation
## 13032 13032 university positive
## 13033 13033 unjust anger
## 13034 13034 unjust negative
## 13035 13035 unjustifiable anger
## 13036 13036 unjustifiable disgust
## 13037 13037 unjustifiable fear
## 13038 13038 unjustifiable negative
## 13039 13039 unjustified negative
## 13040 13040 unkind anger
## 13041 13041 unkind disgust
## 13042 13042 unkind fear
## 13043 13043 unkind negative
## 13044 13044 unkind sadness
## 13045 13045 unknown anticipation
## 13046 13046 unknown fear
## 13047 13047 unknown negative
## 13048 13048 unlawful anger
## 13049 13049 unlawful disgust
## 13050 13050 unlawful fear
## 13051 13051 unlawful negative
## 13052 13052 unlawful sadness
## 13053 13053 unlicensed negative
## 13054 13054 unlimited positive
## 13055 13055 unlucky anger
## 13056 13056 unlucky disgust
## 13057 13057 unlucky fear
## 13058 13058 unlucky negative
## 13059 13059 unlucky sadness
## 13060 13060 unmanageable disgust
## 13061 13061 unmanageable negative
## 13062 13062 unnatural disgust
## 13063 13063 unnatural fear
## 13064 13064 unnatural negative
## 13065 13065 unofficial negative
## 13066 13066 unpaid anger
## 13067 13067 unpaid negative
## 13068 13068 unpaid sadness
## 13069 13069 unpleasant disgust
## 13070 13070 unpleasant negative
## 13071 13071 unpleasant sadness
## 13072 13072 unpopular disgust
## 13073 13073 unpopular negative
## 13074 13074 unpopular sadness
## 13075 13075 unprecedented surprise
## 13076 13076 unpredictable negative
## 13077 13077 unpredictable surprise
## 13078 13078 unprepared negative
## 13079 13079 unpretentious positive
## 13080 13080 unproductive negative
## 13081 13081 unprofitable negative
## 13082 13082 unprotected negative
## 13083 13083 unpublished anticipation
## 13084 13084 unpublished negative
## 13085 13085 unpublished sadness
## 13086 13086 unquestionable positive
## 13087 13087 unquestionable trust
## 13088 13088 unquestionably positive
## 13089 13089 unquestionably trust
## 13090 13090 unquestioned positive
## 13091 13091 unquestioned trust
## 13092 13092 unreliable negative
## 13093 13093 unreliable trust
## 13094 13094 unrequited negative
## 13095 13095 unrequited sadness
## 13096 13096 unresolved anticipation
## 13097 13097 unrest fear
## 13098 13098 unrest sadness
## 13099 13099 unruly anger
## 13100 13100 unruly disgust
## 13101 13101 unruly fear
## 13102 13102 unruly negative
## 13103 13103 unsafe fear
## 13104 13104 unsafe negative
## 13105 13105 unsatisfactory disgust
## 13106 13106 unsatisfactory negative
## 13107 13107 unsatisfied disgust
## 13108 13108 unsatisfied negative
## 13109 13109 unsatisfied sadness
## 13110 13110 unsavory negative
## 13111 13111 unscathed positive
## 13112 13112 unscrupulous negative
## 13113 13113 unseat sadness
## 13114 13114 unselfish positive
## 13115 13115 unsettled anger
## 13116 13116 unsettled disgust
## 13117 13117 unsettled fear
## 13118 13118 unsettled negative
## 13119 13119 unsightly disgust
## 13120 13120 unsightly negative
## 13121 13121 unsophisticated negative
## 13122 13122 unspeakable fear
## 13123 13123 unspeakable negative
## 13124 13124 unstable fear
## 13125 13125 unstable negative
## 13126 13126 unstable surprise
## 13127 13127 unsteady fear
## 13128 13128 unsuccessful negative
## 13129 13129 unsuccessful sadness
## 13130 13130 unsuitable negative
## 13131 13131 unsung negative
## 13132 13132 unsupported negative
## 13133 13133 unsurpassed anticipation
## 13134 13134 unsurpassed fear
## 13135 13135 unsurpassed joy
## 13136 13136 unsurpassed positive
## 13137 13137 unsurpassed trust
## 13138 13138 unsuspecting surprise
## 13139 13139 unsustainable negative
## 13140 13140 unsympathetic anger
## 13141 13141 unsympathetic negative
## 13142 13142 untamed negative
## 13143 13143 untenable negative
## 13144 13144 unthinkable anger
## 13145 13145 unthinkable disgust
## 13146 13146 unthinkable fear
## 13147 13147 unthinkable negative
## 13148 13148 untidy disgust
## 13149 13149 untidy negative
## 13150 13150 untie joy
## 13151 13151 untie negative
## 13152 13152 untie positive
## 13153 13153 untimely negative
## 13154 13154 untitled negative
## 13155 13155 untitled sadness
## 13156 13156 untold anticipation
## 13157 13157 untold negative
## 13158 13158 untoward anger
## 13159 13159 untoward disgust
## 13160 13160 untoward negative
## 13161 13161 untrained negative
## 13162 13162 untrue negative
## 13163 13163 untrustworthy anger
## 13164 13164 untrustworthy negative
## 13165 13165 unverified anticipation
## 13166 13166 unwarranted negative
## 13167 13167 unwashed disgust
## 13168 13168 unwashed negative
## 13169 13169 unwavering positive
## 13170 13170 unwavering trust
## 13171 13171 unwelcome negative
## 13172 13172 unwelcome sadness
## 13173 13173 unwell negative
## 13174 13174 unwell sadness
## 13175 13175 unwillingness negative
## 13176 13176 unwise negative
## 13177 13177 unwitting negative
## 13178 13178 unworthy disgust
## 13179 13179 unworthy negative
## 13180 13180 unyielding negative
## 13181 13181 upheaval anger
## 13182 13182 upheaval fear
## 13183 13183 upheaval negative
## 13184 13184 upheaval sadness
## 13185 13185 uphill anticipation
## 13186 13186 uphill fear
## 13187 13187 uphill negative
## 13188 13188 uphill positive
## 13189 13189 uplift anticipation
## 13190 13190 uplift joy
## 13191 13191 uplift positive
## 13192 13192 uplift trust
## 13193 13193 upright positive
## 13194 13194 upright trust
## 13195 13195 uprising anger
## 13196 13196 uprising anticipation
## 13197 13197 uprising fear
## 13198 13198 uprising negative
## 13199 13199 uproar negative
## 13200 13200 upset anger
## 13201 13201 upset negative
## 13202 13202 upset sadness
## 13203 13203 urchin negative
## 13204 13204 urgency anticipation
## 13205 13205 urgency fear
## 13206 13206 urgency surprise
## 13207 13207 urgent anticipation
## 13208 13208 urgent fear
## 13209 13209 urgent negative
## 13210 13210 urgent surprise
## 13211 13211 urn sadness
## 13212 13212 usefulness positive
## 13213 13213 useless negative
## 13214 13214 usher positive
## 13215 13215 usher trust
## 13216 13216 usual positive
## 13217 13217 usual trust
## 13218 13218 usurp anger
## 13219 13219 usurp negative
## 13220 13220 usurped anger
## 13221 13221 usurped fear
## 13222 13222 usurped negative
## 13223 13223 usury negative
## 13224 13224 utility positive
## 13225 13225 utopian anticipation
## 13226 13226 utopian joy
## 13227 13227 utopian positive
## 13228 13228 utopian trust
## 13229 13229 vacancy negative
## 13230 13230 vacation anticipation
## 13231 13231 vacation joy
## 13232 13232 vacation positive
## 13233 13233 vaccine positive
## 13234 13234 vacuous disgust
## 13235 13235 vacuous negative
## 13236 13236 vague negative
## 13237 13237 vagueness negative
## 13238 13238 vainly disgust
## 13239 13239 vainly negative
## 13240 13240 vainly sadness
## 13241 13241 valiant positive
## 13242 13242 validity fear
## 13243 13243 valor positive
## 13244 13244 valor trust
## 13245 13245 valuable positive
## 13246 13246 vampire anger
## 13247 13247 vampire disgust
## 13248 13248 vampire fear
## 13249 13249 vampire negative
## 13250 13250 vanguard positive
## 13251 13251 vanish surprise
## 13252 13252 vanished fear
## 13253 13253 vanished negative
## 13254 13254 vanished sadness
## 13255 13255 vanished surprise
## 13256 13256 vanity negative
## 13257 13257 vanquish positive
## 13258 13258 variable surprise
## 13259 13259 varicella disgust
## 13260 13260 varicella fear
## 13261 13261 varicella negative
## 13262 13262 varicella sadness
## 13263 13263 varicose negative
## 13264 13264 veal sadness
## 13265 13265 veal trust
## 13266 13266 veer fear
## 13267 13267 veer surprise
## 13268 13268 vegetative disgust
## 13269 13269 vegetative negative
## 13270 13270 vegetative sadness
## 13271 13271 vehement anger
## 13272 13272 vehement fear
## 13273 13273 vehement negative
## 13274 13274 velvet positive
## 13275 13275 velvety positive
## 13276 13276 vendetta anger
## 13277 13277 vendetta fear
## 13278 13278 vendetta negative
## 13279 13279 vendetta sadness
## 13280 13280 venerable anticipation
## 13281 13281 venerable joy
## 13282 13282 venerable positive
## 13283 13283 venerable trust
## 13284 13284 veneration positive
## 13285 13285 vengeance anger
## 13286 13286 vengeance negative
## 13287 13287 vengeful anger
## 13288 13288 vengeful fear
## 13289 13289 vengeful negative
## 13290 13290 venom anger
## 13291 13291 venom disgust
## 13292 13292 venom fear
## 13293 13293 venom negative
## 13294 13294 venomous anger
## 13295 13295 venomous disgust
## 13296 13296 venomous fear
## 13297 13297 venomous negative
## 13298 13298 vent anger
## 13299 13299 veracity anticipation
## 13300 13300 veracity joy
## 13301 13301 veracity positive
## 13302 13302 veracity surprise
## 13303 13303 veracity trust
## 13304 13304 verbosity negative
## 13305 13305 verdant positive
## 13306 13306 verdict fear
## 13307 13307 verge anticipation
## 13308 13308 verge fear
## 13309 13309 verge negative
## 13310 13310 verification positive
## 13311 13311 verification trust
## 13312 13312 verified positive
## 13313 13313 verified trust
## 13314 13314 verily positive
## 13315 13315 verily trust
## 13316 13316 veritable positive
## 13317 13317 vermin anger
## 13318 13318 vermin disgust
## 13319 13319 vermin fear
## 13320 13320 vermin negative
## 13321 13321 vernal joy
## 13322 13322 vernal positive
## 13323 13323 versus anger
## 13324 13324 versus negative
## 13325 13325 vertigo fear
## 13326 13326 vertigo negative
## 13327 13327 verve positive
## 13328 13328 vesicular disgust
## 13329 13329 veteran positive
## 13330 13330 veteran trust
## 13331 13331 veto anger
## 13332 13332 veto negative
## 13333 13333 vicar positive
## 13334 13334 vicar trust
## 13335 13335 vice negative
## 13336 13336 vicious anger
## 13337 13337 vicious disgust
## 13338 13338 vicious negative
## 13339 13339 victim anger
## 13340 13340 victim fear
## 13341 13341 victim negative
## 13342 13342 victim sadness
## 13343 13343 victimized anger
## 13344 13344 victimized disgust
## 13345 13345 victimized fear
## 13346 13346 victimized negative
## 13347 13347 victimized sadness
## 13348 13348 victimized surprise
## 13349 13349 victor joy
## 13350 13350 victor positive
## 13351 13351 victorious joy
## 13352 13352 victorious positive
## 13353 13353 victory anticipation
## 13354 13354 victory joy
## 13355 13355 victory positive
## 13356 13356 victory trust
## 13357 13357 vigil anticipation
## 13358 13358 vigilance anticipation
## 13359 13359 vigilance positive
## 13360 13360 vigilance trust
## 13361 13361 vigilant fear
## 13362 13362 vigilant positive
## 13363 13363 vigilant trust
## 13364 13364 vigor positive
## 13365 13365 vigorous positive
## 13366 13366 vigorous trust
## 13367 13367 villager positive
## 13368 13368 villager trust
## 13369 13369 villain fear
## 13370 13370 villain negative
## 13371 13371 villainous anger
## 13372 13372 villainous disgust
## 13373 13373 villainous fear
## 13374 13374 villainous negative
## 13375 13375 vindicate anger
## 13376 13376 vindicated positive
## 13377 13377 vindication anticipation
## 13378 13378 vindication joy
## 13379 13379 vindication positive
## 13380 13380 vindication trust
## 13381 13381 vindictive anger
## 13382 13382 vindictive disgust
## 13383 13383 vindictive negative
## 13384 13384 violation anger
## 13385 13385 violation fear
## 13386 13386 violation negative
## 13387 13387 violation sadness
## 13388 13388 violation surprise
## 13389 13389 violence anger
## 13390 13390 violence fear
## 13391 13391 violence negative
## 13392 13392 violence sadness
## 13393 13393 violent anger
## 13394 13394 violent disgust
## 13395 13395 violent fear
## 13396 13396 violent negative
## 13397 13397 violent surprise
## 13398 13398 violently anger
## 13399 13399 violently disgust
## 13400 13400 violently fear
## 13401 13401 violently negative
## 13402 13402 violently sadness
## 13403 13403 viper fear
## 13404 13404 viper negative
## 13405 13405 virgin positive
## 13406 13406 virgin trust
## 13407 13407 virginity anticipation
## 13408 13408 virginity positive
## 13409 13409 virtue positive
## 13410 13410 virtue trust
## 13411 13411 virtuous joy
## 13412 13412 virtuous positive
## 13413 13413 virtuous trust
## 13414 13414 virulence anger
## 13415 13415 virulence fear
## 13416 13416 virulence negative
## 13417 13417 virus negative
## 13418 13418 vision anticipation
## 13419 13419 vision positive
## 13420 13420 visionary anticipation
## 13421 13421 visionary joy
## 13422 13422 visionary positive
## 13423 13423 visionary trust
## 13424 13424 visit positive
## 13425 13425 visitation negative
## 13426 13426 visitor anticipation
## 13427 13427 visitor joy
## 13428 13428 visitor positive
## 13429 13429 visor anticipation
## 13430 13430 visor surprise
## 13431 13431 vital positive
## 13432 13432 vitality joy
## 13433 13433 vitality positive
## 13434 13434 vitality trust
## 13435 13435 vivacious joy
## 13436 13436 vivacious positive
## 13437 13437 vivid joy
## 13438 13438 vivid positive
## 13439 13439 vixen negative
## 13440 13440 vocabulary positive
## 13441 13441 volatility anger
## 13442 13442 volatility anticipation
## 13443 13443 volatility fear
## 13444 13444 volatility negative
## 13445 13445 volatility surprise
## 13446 13446 volcano fear
## 13447 13447 volcano negative
## 13448 13448 volcano surprise
## 13449 13449 volunteer anticipation
## 13450 13450 volunteer fear
## 13451 13451 volunteer joy
## 13452 13452 volunteer positive
## 13453 13453 volunteer trust
## 13454 13454 volunteers trust
## 13455 13455 voluptuous anticipation
## 13456 13456 voluptuous joy
## 13457 13457 voluptuous positive
## 13458 13458 vomit disgust
## 13459 13459 vomiting negative
## 13460 13460 voodoo negative
## 13461 13461 vote anger
## 13462 13462 vote anticipation
## 13463 13463 vote joy
## 13464 13464 vote negative
## 13465 13465 vote positive
## 13466 13466 vote sadness
## 13467 13467 vote surprise
## 13468 13468 vote trust
## 13469 13469 votive trust
## 13470 13470 vouch positive
## 13471 13471 vouch trust
## 13472 13472 voucher trust
## 13473 13473 vow anticipation
## 13474 13474 vow joy
## 13475 13475 vow positive
## 13476 13476 vow trust
## 13477 13477 voyage anticipation
## 13478 13478 vulgar disgust
## 13479 13479 vulgar negative
## 13480 13480 vulgarity anger
## 13481 13481 vulgarity disgust
## 13482 13482 vulgarity negative
## 13483 13483 vulgarity sadness
## 13484 13484 vulnerability fear
## 13485 13485 vulnerability negative
## 13486 13486 vulnerability sadness
## 13487 13487 vulture disgust
## 13488 13488 vulture fear
## 13489 13489 vulture negative
## 13490 13490 waffle anger
## 13491 13491 waffle negative
## 13492 13492 waffle sadness
## 13493 13493 wages joy
## 13494 13494 wages positive
## 13495 13495 wail fear
## 13496 13496 wail negative
## 13497 13497 wail sadness
## 13498 13498 wait anticipation
## 13499 13499 wait negative
## 13500 13500 wallow disgust
## 13501 13501 wallow negative
## 13502 13502 wallow sadness
## 13503 13503 wan fear
## 13504 13504 wan negative
## 13505 13505 wan sadness
## 13506 13506 wane negative
## 13507 13507 wane sadness
## 13508 13508 wanting negative
## 13509 13509 wanting sadness
## 13510 13510 war fear
## 13511 13511 war negative
## 13512 13512 warden anger
## 13513 13513 warden fear
## 13514 13514 warden negative
## 13515 13515 warden trust
## 13516 13516 ware fear
## 13517 13517 ware negative
## 13518 13518 warfare anger
## 13519 13519 warfare fear
## 13520 13520 warfare negative
## 13521 13521 warfare sadness
## 13522 13522 warlike anger
## 13523 13523 warlike fear
## 13524 13524 warlike negative
## 13525 13525 warlock fear
## 13526 13526 warn anticipation
## 13527 13527 warn fear
## 13528 13528 warn negative
## 13529 13529 warn surprise
## 13530 13530 warn trust
## 13531 13531 warned anticipation
## 13532 13532 warned fear
## 13533 13533 warned surprise
## 13534 13534 warning fear
## 13535 13535 warp anger
## 13536 13536 warp negative
## 13537 13537 warp sadness
## 13538 13538 warped negative
## 13539 13539 warranty positive
## 13540 13540 warranty trust
## 13541 13541 warrior anger
## 13542 13542 warrior fear
## 13543 13543 warrior positive
## 13544 13544 wart disgust
## 13545 13545 wart negative
## 13546 13546 wary fear
## 13547 13547 waste disgust
## 13548 13548 waste negative
## 13549 13549 wasted anger
## 13550 13550 wasted disgust
## 13551 13551 wasted negative
## 13552 13552 wasteful anger
## 13553 13553 wasteful disgust
## 13554 13554 wasteful negative
## 13555 13555 wasteful sadness
## 13556 13556 wasting disgust
## 13557 13557 wasting fear
## 13558 13558 wasting negative
## 13559 13559 wasting sadness
## 13560 13560 watch anticipation
## 13561 13561 watch fear
## 13562 13562 watchdog positive
## 13563 13563 watchdog trust
## 13564 13564 watchful positive
## 13565 13565 watchful trust
## 13566 13566 watchman positive
## 13567 13567 watchman trust
## 13568 13568 waterproof positive
## 13569 13569 watery negative
## 13570 13570 waver fear
## 13571 13571 waver negative
## 13572 13572 weakened negative
## 13573 13573 weakly fear
## 13574 13574 weakly negative
## 13575 13575 weakly sadness
## 13576 13576 weakness negative
## 13577 13577 wealth joy
## 13578 13578 wealth positive
## 13579 13579 wealth trust
## 13580 13580 wear negative
## 13581 13581 wear trust
## 13582 13582 wearily negative
## 13583 13583 wearily sadness
## 13584 13584 weariness negative
## 13585 13585 weariness sadness
## 13586 13586 weary negative
## 13587 13587 weary sadness
## 13588 13588 weatherproof positive
## 13589 13589 weeds negative
## 13590 13590 weeds sadness
## 13591 13591 weep negative
## 13592 13592 weep sadness
## 13593 13593 weeping sadness
## 13594 13594 weigh anticipation
## 13595 13595 weigh trust
## 13596 13596 weight anticipation
## 13597 13597 weight disgust
## 13598 13598 weight fear
## 13599 13599 weight joy
## 13600 13600 weight negative
## 13601 13601 weight positive
## 13602 13602 weight sadness
## 13603 13603 weight surprise
## 13604 13604 weight trust
## 13605 13605 weighty fear
## 13606 13606 weird disgust
## 13607 13607 weird negative
## 13608 13608 weirdo fear
## 13609 13609 weirdo negative
## 13610 13610 welcomed joy
## 13611 13611 welcomed positive
## 13612 13612 wen negative
## 13613 13613 wench anger
## 13614 13614 wench disgust
## 13615 13615 wench negative
## 13616 13616 whack negative
## 13617 13617 whim anticipation
## 13618 13618 whim joy
## 13619 13619 whim negative
## 13620 13620 whim surprise
## 13621 13621 whimper fear
## 13622 13622 whimper sadness
## 13623 13623 whimsical joy
## 13624 13624 whine disgust
## 13625 13625 whine negative
## 13626 13626 whine sadness
## 13627 13627 whip anger
## 13628 13628 whip negative
## 13629 13629 whirlpool fear
## 13630 13630 whirlwind fear
## 13631 13631 whirlwind negative
## 13632 13632 whisky negative
## 13633 13633 whiteness joy
## 13634 13634 whiteness positive
## 13635 13635 wholesome positive
## 13636 13636 wholesome trust
## 13637 13637 whore disgust
## 13638 13638 whore negative
## 13639 13639 wicked fear
## 13640 13640 wicked negative
## 13641 13641 wickedness disgust
## 13642 13642 wickedness negative
## 13643 13643 wicket positive
## 13644 13644 widespread positive
## 13645 13645 widow sadness
## 13646 13646 widower sadness
## 13647 13647 wild negative
## 13648 13648 wild surprise
## 13649 13649 wildcat negative
## 13650 13650 wilderness anticipation
## 13651 13651 wilderness fear
## 13652 13652 wilderness sadness
## 13653 13653 wildfire fear
## 13654 13654 wildfire negative
## 13655 13655 wildfire sadness
## 13656 13656 wildfire surprise
## 13657 13657 willful anger
## 13658 13658 willful negative
## 13659 13659 willful sadness
## 13660 13660 willingly positive
## 13661 13661 willingness positive
## 13662 13662 wimp disgust
## 13663 13663 wimp fear
## 13664 13664 wimp negative
## 13665 13665 wimpy anger
## 13666 13666 wimpy disgust
## 13667 13667 wimpy fear
## 13668 13668 wimpy negative
## 13669 13669 wimpy sadness
## 13670 13670 wince anger
## 13671 13671 wince disgust
## 13672 13672 wince fear
## 13673 13673 wince negative
## 13674 13674 wince sadness
## 13675 13675 windfall positive
## 13676 13676 winner anticipation
## 13677 13677 winner joy
## 13678 13678 winner positive
## 13679 13679 winner surprise
## 13680 13680 winning anticipation
## 13681 13681 winning disgust
## 13682 13682 winning joy
## 13683 13683 winning positive
## 13684 13684 winning sadness
## 13685 13685 winning surprise
## 13686 13686 winning trust
## 13687 13687 winnings anticipation
## 13688 13688 winnings joy
## 13689 13689 winnings positive
## 13690 13690 wireless anger
## 13691 13691 wireless anticipation
## 13692 13692 wireless positive
## 13693 13693 wireless surprise
## 13694 13694 wis positive
## 13695 13695 wisdom positive
## 13696 13696 wisdom trust
## 13697 13697 wise positive
## 13698 13698 wishful anticipation
## 13699 13699 wit positive
## 13700 13700 witch anger
## 13701 13701 witch disgust
## 13702 13702 witch fear
## 13703 13703 witch negative
## 13704 13704 witchcraft anger
## 13705 13705 witchcraft fear
## 13706 13706 witchcraft negative
## 13707 13707 witchcraft sadness
## 13708 13708 withdraw negative
## 13709 13709 withdraw sadness
## 13710 13710 wither negative
## 13711 13711 wither sadness
## 13712 13712 withered disgust
## 13713 13713 withered negative
## 13714 13714 withstand anticipation
## 13715 13715 withstand fear
## 13716 13716 withstand positive
## 13717 13717 witness trust
## 13718 13718 wits positive
## 13719 13719 witty joy
## 13720 13720 witty positive
## 13721 13721 wizard anticipation
## 13722 13722 wizard positive
## 13723 13723 wizard surprise
## 13724 13724 woe disgust
## 13725 13725 woe fear
## 13726 13726 woe negative
## 13727 13727 woe sadness
## 13728 13728 woeful negative
## 13729 13729 woeful sadness
## 13730 13730 woefully disgust
## 13731 13731 woefully negative
## 13732 13732 woefully sadness
## 13733 13733 womb positive
## 13734 13734 wonderful joy
## 13735 13735 wonderful positive
## 13736 13736 wonderful surprise
## 13737 13737 wonderful trust
## 13738 13738 wonderfully joy
## 13739 13739 wonderfully positive
## 13740 13740 wonderfully surprise
## 13741 13741 wondrous positive
## 13742 13742 wont anticipation
## 13743 13743 wop anger
## 13744 13744 word positive
## 13745 13745 word trust
## 13746 13746 words anger
## 13747 13747 words negative
## 13748 13748 working positive
## 13749 13749 worm anticipation
## 13750 13750 worm negative
## 13751 13751 worm surprise
## 13752 13752 worn negative
## 13753 13753 worn sadness
## 13754 13754 worried negative
## 13755 13755 worried sadness
## 13756 13756 worry anticipation
## 13757 13757 worry fear
## 13758 13758 worry negative
## 13759 13759 worry sadness
## 13760 13760 worrying anticipation
## 13761 13761 worrying fear
## 13762 13762 worrying negative
## 13763 13763 worrying sadness
## 13764 13764 worse fear
## 13765 13765 worse negative
## 13766 13766 worse sadness
## 13767 13767 worsening disgust
## 13768 13768 worsening negative
## 13769 13769 worsening sadness
## 13770 13770 worship anticipation
## 13771 13771 worship fear
## 13772 13772 worship joy
## 13773 13773 worship positive
## 13774 13774 worship trust
## 13775 13775 worth positive
## 13776 13776 worthless anger
## 13777 13777 worthless disgust
## 13778 13778 worthless negative
## 13779 13779 worthless sadness
## 13780 13780 worthy positive
## 13781 13781 worthy trust
## 13782 13782 wot positive
## 13783 13783 wot trust
## 13784 13784 wound anger
## 13785 13785 wound fear
## 13786 13786 wound negative
## 13787 13787 wound sadness
## 13788 13788 wrangling anger
## 13789 13789 wrangling disgust
## 13790 13790 wrangling fear
## 13791 13791 wrangling negative
## 13792 13792 wrangling sadness
## 13793 13793 wrath anger
## 13794 13794 wrath fear
## 13795 13795 wrath negative
## 13796 13796 wreak anger
## 13797 13797 wreak negative
## 13798 13798 wreck anger
## 13799 13799 wreck disgust
## 13800 13800 wreck fear
## 13801 13801 wreck negative
## 13802 13802 wreck sadness
## 13803 13803 wreck surprise
## 13804 13804 wrecked anger
## 13805 13805 wrecked fear
## 13806 13806 wrecked negative
## 13807 13807 wrecked sadness
## 13808 13808 wrench negative
## 13809 13809 wrestling negative
## 13810 13810 wretch anger
## 13811 13811 wretch disgust
## 13812 13812 wretch negative
## 13813 13813 wretch sadness
## 13814 13814 wretched disgust
## 13815 13815 wretched negative
## 13816 13816 wretched sadness
## 13817 13817 wring anger
## 13818 13818 wrinkled sadness
## 13819 13819 writer positive
## 13820 13820 wrong negative
## 13821 13821 wrongdoing anger
## 13822 13822 wrongdoing disgust
## 13823 13823 wrongdoing negative
## 13824 13824 wrongdoing sadness
## 13825 13825 wrongful anger
## 13826 13826 wrongful disgust
## 13827 13827 wrongful negative
## 13828 13828 wrongful sadness
## 13829 13829 wrongly anger
## 13830 13830 wrongly fear
## 13831 13831 wrongly negative
## 13832 13832 wrongly sadness
## 13833 13833 wrought negative
## 13834 13834 wry negative
## 13835 13835 xenophobia fear
## 13836 13836 xenophobia negative
## 13837 13837 yawn negative
## 13838 13838 yawning negative
## 13839 13839 yearning anticipation
## 13840 13840 yearning joy
## 13841 13841 yearning negative
## 13842 13842 yearning positive
## 13843 13843 yearning trust
## 13844 13844 yell anger
## 13845 13845 yell fear
## 13846 13846 yell negative
## 13847 13847 yell surprise
## 13848 13848 yellows negative
## 13849 13849 yelp anger
## 13850 13850 yelp fear
## 13851 13851 yelp negative
## 13852 13852 yelp surprise
## 13853 13853 youth anger
## 13854 13854 youth anticipation
## 13855 13855 youth fear
## 13856 13856 youth joy
## 13857 13857 youth positive
## 13858 13858 youth surprise
## 13859 13859 zany surprise
## 13860 13860 zeal anticipation
## 13861 13861 zeal joy
## 13862 13862 zeal positive
## 13863 13863 zeal surprise
## 13864 13864 zeal trust
## 13865 13865 zealous joy
## 13866 13866 zealous positive
## 13867 13867 zealous trust
## 13868 13868 zest anticipation
## 13869 13869 zest joy
## 13870 13870 zest positive
## 13871 13871 zest trust
## 13872 13872 zip negative
These libraries were created either using crowdsourcing or cloud computing/ai like Amazon Mechanical Turk, or by labor of one of the authors, and then validating with crowdsourcing. Let’s look at the words with a joy score from NRC.
library(gutenbergr)
library(dplyr)
library(stringr)
darwin <- gutenberg_download(c(944, 1227, 1228, 2300), mirror = "https://mirror2.sandyriver.net/pub/gutenberg")
tidy_books <- darwin %>%
group_by(gutenberg_id) %>%
dplyr::mutate(linenumber = row_number(), chapter = cumsum(str_detect(text, regex("^chapter [\\divxlc]", ignore_case = TRUE)))) %>%
ungroup() %>%
unnest_tokens(word, text)
tidy_books
## # A tibble: 786,575 × 4
## gutenberg_id linenumber chapter word
## <int> <int> <int> <chr>
## 1 944 1 0 the
## 2 944 1 0 voyage
## 3 944 1 0 of
## 4 944 1 0 the
## 5 944 1 0 beagle
## 6 944 1 0 by
## 7 944 2 0 charles
## 8 944 2 0 darwin
## 9 944 8 0 about
## 10 944 8 0 the
## # … with 786,565 more rows
Let’s add the book name instead of GID
colnames(tidy_books)[1] <- "book"
tidy_books$book[tidy_books$book == 944] <- "The Voyage of the Beagle"
tidy_books$book[tidy_books$book == 1227] <- "The Expression of the Emotions in Man and Animals"
tidy_books$book[tidy_books$book == 1228] <- "On the Origin of Species By Means of Natural Selection"
tidy_books$book[tidy_books$book == 2300] <- "The Descent of Man, and Selection in Relation to Sex"
tidy_books
## # A tibble: 786,575 × 4
## book linenumber chapter word
## <chr> <int> <int> <chr>
## 1 The Voyage of the Beagle 1 0 the
## 2 The Voyage of the Beagle 1 0 voyage
## 3 The Voyage of the Beagle 1 0 of
## 4 The Voyage of the Beagle 1 0 the
## 5 The Voyage of the Beagle 1 0 beagle
## 6 The Voyage of the Beagle 1 0 by
## 7 The Voyage of the Beagle 2 0 charles
## 8 The Voyage of the Beagle 2 0 darwin
## 9 The Voyage of the Beagle 8 0 about
## 10 The Voyage of the Beagle 8 0 the
## # … with 786,565 more rows
Now that we have a tidy format with one word per row, we are ready for sentiment analysis. First, let’s use NRC.
nrc_joy <- nrc %>%
filter(sentiment == "joy")
tidy_books %>%
filter(book == "The Voyage of the Beagle") %>%
inner_join(nrc_joy) %>%
count(word, sort = TRUE)
## Joining, by = "word"
## # A tibble: 277 × 2
## word n
## <chr> <int>
## 1 found 301
## 2 good 161
## 3 remarkable 114
## 4 green 95
## 5 kind 92
## 6 tree 86
## 7 present 85
## 8 food 78
## 9 beautiful 61
## 10 elevation 60
## # … with 267 more rows
We can also examine how sentiment changes throughout a work.
library(tidyr)
Charles_Darwin_sentiment <- tidy_books %>%
inner_join(get_sentiments("bing")) %>%
count(book, index = linenumber %/% 80, sentiment) %>%
pivot_wider(names_from = sentiment, values_from = n, values_fill = 0) %>%
dplyr::mutate(sentiments = positive - negative)
## Joining, by = "word"
Now, let’s plot it
library(ggplot2)
ggplot(Charles_Darwin_sentiment, aes(index, sentiments, fill = book)) +
geom_col(show.legend = FALSE) +
facet_wrap(~book, ncol = 2, scales = "free_x")
Let’s compare the three sentiment dictionaries. There are several options for sentiment lexicons, you might want some more information on which is more appropriate for your purpose. Here we will use all three of our dictionaries and examine how the sentiment changes across the arc of TVOTB.
library(tidyr)
voyage <- tidy_books %>%
filter(book == "The Voyage of the Beagle")
voyage
## # A tibble: 208,118 × 4
## book linenumber chapter word
## <chr> <int> <int> <chr>
## 1 The Voyage of the Beagle 1 0 the
## 2 The Voyage of the Beagle 1 0 voyage
## 3 The Voyage of the Beagle 1 0 of
## 4 The Voyage of the Beagle 1 0 the
## 5 The Voyage of the Beagle 1 0 beagle
## 6 The Voyage of the Beagle 1 0 by
## 7 The Voyage of the Beagle 2 0 charles
## 8 The Voyage of the Beagle 2 0 darwin
## 9 The Voyage of the Beagle 8 0 about
## 10 The Voyage of the Beagle 8 0 the
## # … with 208,108 more rows
Let’s again use integer division (“%/%”) to define larger sections of the text that span multiple lines, and we can use the same pattern with “count()”, “pivot_wider()”, and “dplyr::mutate()”, to find the sentiment in each of these sections of text.
affin <- voyage %>%
inner_join(afinn) %>%
group_by(index = linenumber %/% 80) %>%
summarise(sentiment = sum(value)) %>%
dplyr::mutate(method = "AFINN")
## Joining, by = "word"
bing_and_nrc <- bind_rows(
voyage%>%
inner_join(get_sentiments("bing")) %>%
dplyr::mutate(method = "Bing et al."),
voyage %>%
inner_join(nrc) %>%
filter(sentiment %in% c("positive", "negative"))
) %>%
dplyr::mutate(method = "NRC") %>%
count(method, index = linenumber %/% 80, sentiment) %>%
pivot_wider(names_from = sentiment,
values_from = n,
values_fill = 0) %>%
dplyr::mutate(sentiment = positive - negative)
## Joining, by = "word"
## Joining, by = "word"
Now, we can estimate the net sentiment (positive-negative) in each chunk of the novel text for each lexicon (dictionary). Let’s bind them all together and visualize them with ggplot.
bind_rows(affin, bing_and_nrc) %>%
ggplot(aes(index, sentiment, fill = method)) +
geom_col(show.legend = FALSE) +
facet_wrap(~method, ncol = 1, scales = "free_y")
Let’s look at the counts based on each dictionary
nrc %>%
filter(sentiment %in% c("positive", "negative")) %>%
count(sentiment)
## sentiment n
## 1 negative 3316
## 2 positive 2308
And for bing
get_sentiments("bing") %>%
count(sentiment)
## # A tibble: 2 × 2
## sentiment n
## <chr> <int>
## 1 negative 4781
## 2 positive 2005
Let’s create the function
bing_word_counts <- tidy_books %>%
inner_join(get_sentiments("bing")) %>%
count(word, sentiment, sort = TRUE) %>%
ungroup()
## Joining, by = "word"
bing_word_counts
## # A tibble: 2,492 × 3
## word sentiment n
## <chr> <chr> <int>
## 1 great positive 1226
## 2 well positive 855
## 3 like positive 813
## 4 good positive 487
## 5 doubt negative 414
## 6 wild negative 317
## 7 respect positive 310
## 8 remarkable positive 295
## 9 important positive 281
## 10 bright positive 258
## # … with 2,482 more rows
This can be shown visually, and we can pipe straight into ggplot2.
bing_word_counts %>%
group_by(sentiment) %>%
slice_max(n, n = 10) %>%
ungroup() %>%
dplyr:: mutate(word = reorder(word, n)) %>%
ggplot(aes(n, word, fill = sentiment)) +
geom_col(show.legend = FALSE) +
facet_wrap(~sentiment, scale = "free_y") +
labs(x = "contribution to sentiment", y = NULL)
Let’s spot an anomaly in the dataset.
custom_stop_words <- bind_rows(tibble(word = c("wild", "dark", "great", "like"), lexicon = c("custom")), stop_words)
custom_stop_words
## # A tibble: 1,153 × 2
## word lexicon
## <chr> <chr>
## 1 wild custom
## 2 dark custom
## 3 great custom
## 4 like custom
## 5 a SMART
## 6 a's SMART
## 7 able SMART
## 8 about SMART
## 9 above SMART
## 10 according SMART
## # … with 1,143 more rows
Word Clouds! We can see that tidy text mining and sentiment analysis works well with ggplot2, but having our data in tidy format leads to other nice graphing techniques. Let’s use the wordcloud package.
library(wordcloud)
##
## Attaching package: 'wordcloud'
## The following object is masked from 'package:gplots':
##
## textplot
tidy_books %>%
anti_join(stop_words) %>%
count(word) %>%
with(wordcloud(word, n, max.words = 100))
## Joining, by = "word"
Let’s also look at the comparison.cloud(), which may require turning the dataframe into a matrix. We can change to matrix using the acast() function.
library(reshape2)
##
## Attaching package: 'reshape2'
## The following object is masked from 'package:tigerstats':
##
## tips
## The following object is masked from 'package:tidyr':
##
## smiths
tidy_books %>%
inner_join(get_sentiments("bing")) %>%
count(word, sentiment, sort = TRUE) %>%
acast(word ~ sentiment, value.var = "n", fill = 0) %>%
comparison.cloud(colors = c("gray20", "gray80"), max.words = 100)
## Joining, by = "word"
Looking at units beyond words. A lot of useful work can be done by tokenizing at the word level, but sometimes it is nice to look at different units or text. For example, we can look beyond just unigrams. Ex. I am not having a good day.
bingnegative <- get_sentiments("bing") %>%
filter(sentiment == "negative")
word_counts <- tidy_books %>%
group_by(book, chapter) %>%
dplyr::summarize(words = n())
## `summarise()` has grouped output by 'book'. You can override using the
## `.groups` argument.
tidy_books %>%
semi_join(bingnegative) %>%
group_by(book, chapter) %>%
dplyr::summarize(negativewords = n()) %>%
left_join(word_counts, by = c("book", "chapter")) %>%
dplyr::mutate(ratio = negativewords/words) %>%
filter(chapter !=0) %>%
slice_max(ratio, n = 1) %>%
ungroup()
## Joining, by = "word"
## `summarise()` has grouped output by 'book'. You can override using the
## `.groups` argument.
## # A tibble: 4 × 5
## book chapter negat…¹ words ratio
## <chr> <int> <int> <int> <dbl>
## 1 On the Origin of Species By Means of Natural Sel… 3 5 86 0.0581
## 2 The Descent of Man, and Selection in Relation to… 20 4 87 0.0460
## 3 The Expression of the Emotions in Man and Animals 10 249 4220 0.0590
## 4 The Voyage of the Beagle 10 375 11202 0.0335
## # … with abbreviated variable name ¹negativewords
So far, we have only looked at single words, but many interesting (more accurate) analyses are based on the relationship between words. Let’s look at some methods of tidytext for calculating and visualizing word relationships.
library(dplyr)
library(tidytext)
library(gutenbergr)
darwin_books <- gutenberg_download(c(944, 1227, 1228, 2300), mirror = "https://mirror2.sandyriver.net/pub/gutenberg")
colnames(darwin_books)[1] <- "book"
darwin_books$book[darwin_books$book == 944] <- "The Voyage of the Beagle"
darwin_books$book[darwin_books$book == 1227] <- "The Expression of the Emotions in Man and Animals"
darwin_books$book[darwin_books$book == 1228] <- "On the Origin of Species By Means of Natural Selection"
darwin_books$book[darwin_books$book == 2300] <- "The Descent of Man, and Selection in Relation to Sex"
darwin_bigrams <- darwin_books %>%
unnest_tokens(bigram, text, token = "ngrams", n = 2)
darwin_bigrams
## # A tibble: 724,531 × 2
## book bigram
## <chr> <chr>
## 1 The Voyage of the Beagle the voyage
## 2 The Voyage of the Beagle voyage of
## 3 The Voyage of the Beagle of the
## 4 The Voyage of the Beagle the beagle
## 5 The Voyage of the Beagle beagle by
## 6 The Voyage of the Beagle charles darwin
## 7 The Voyage of the Beagle <NA>
## 8 The Voyage of the Beagle <NA>
## 9 The Voyage of the Beagle <NA>
## 10 The Voyage of the Beagle <NA>
## # … with 724,521 more rows
This data is still in tidytext format, and is not structured as one-token-per-row. Each token is a bigram.
Counting and filtering n-grams.
darwin_bigrams %>%
count(bigram, sort = TRUE)
## # A tibble: 238,516 × 2
## bigram n
## <chr> <int>
## 1 of the 11297
## 2 <NA> 8947
## 3 in the 5257
## 4 on the 4093
## 5 to the 2849
## 6 the same 2048
## 7 that the 1947
## 8 it is 1830
## 9 of a 1610
## 10 and the 1590
## # … with 238,506 more rows
Most of the common bigrams are stop-words. This can be a good time to use tidyr’s separate command which splilts a common into multiple based on a delimiter. This will let us make a column for word one and word two.
library(tidyr)
bigrams_separated <- darwin_bigrams %>%
separate(bigram, c("word1", "word2"), sep = " ")
bigrams_filtered <- bigrams_separated %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word)
bigrams_filtered
## # A tibble: 94,896 × 3
## book word1 word2
## <chr> <chr> <chr>
## 1 The Voyage of the Beagle charles darwin
## 2 The Voyage of the Beagle <NA> <NA>
## 3 The Voyage of the Beagle <NA> <NA>
## 4 The Voyage of the Beagle <NA> <NA>
## 5 The Voyage of the Beagle <NA> <NA>
## 6 The Voyage of the Beagle <NA> <NA>
## 7 The Voyage of the Beagle online edition
## 8 The Voyage of the Beagle <NA> <NA>
## 9 The Voyage of the Beagle degree symbol
## 10 The Voyage of the Beagle degs italics
## # … with 94,886 more rows
New bigram counts.
bigram_counts <- bigrams_filtered %>%
unite(bigram, word1, word2, sep = " ")
bigram_counts
## # A tibble: 94,896 × 2
## book bigram
## <chr> <chr>
## 1 The Voyage of the Beagle charles darwin
## 2 The Voyage of the Beagle NA NA
## 3 The Voyage of the Beagle NA NA
## 4 The Voyage of the Beagle NA NA
## 5 The Voyage of the Beagle NA NA
## 6 The Voyage of the Beagle NA NA
## 7 The Voyage of the Beagle online edition
## 8 The Voyage of the Beagle NA NA
## 9 The Voyage of the Beagle degree symbol
## 10 The Voyage of the Beagle degs italics
## # … with 94,886 more rows
We may also be interested in trigrams, which are three word combos.
trigrams <- darwin_books %>%
unnest_tokens(trigram, text, token = "ngrams", n =3) %>%
separate(trigram, c("word1", "word2", "word3"), sep = " ") %>%
filter(!word1 %in% stop_words$word,
!word2 %in% stop_words$word,
!word3 %in% stop_words$word) %>%
count(word1, word2, word3, sort = TRUE)
trigrams
## # A tibble: 19,971 × 4
## word1 word2 word3 n
## <chr> <chr> <chr> <int>
## 1 <NA> <NA> <NA> 9884
## 2 tierra del fuego 92
## 3 secondary sexual characters 91
## 4 captain fitz roy 45
## 5 closely allied species 30
## 6 de la physionomie 30
## 7 domestication vol ii 26
## 8 vol ii pp 22
## 9 vertebrates vol iii 21
## 10 proc zoolog soc 18
## # … with 19,961 more rows
Let’s analyze some bigrams
bigrams_filtered %>%
filter(word2 == "selection") %>%
count(book, word1, sort = TRUE)
## # A tibble: 39 × 3
## book word1 n
## <chr> <chr> <int>
## 1 The Descent of Man, and Selection in Relation to Sex sexual 254
## 2 On the Origin of Species By Means of Natural Selection natural 250
## 3 The Descent of Man, and Selection in Relation to Sex natural 156
## 4 On the Origin of Species By Means of Natural Selection sexual 18
## 5 On the Origin of Species By Means of Natural Selection continued 6
## 6 The Descent of Man, and Selection in Relation to Sex unconscious 6
## 7 On the Origin of Species By Means of Natural Selection methodical 5
## 8 The Descent of Man, and Selection in Relation to Sex continued 5
## 9 On the Origin of Species By Means of Natural Selection unconscious 4
## 10 The Expression of the Emotions in Man and Animals natural 4
## # … with 29 more rows
Let’s again look at tf-idf across bigrams across Darwin’s works
bigram_tf_idf <- bigram_counts %>%
count(book, bigram) %>%
bind_tf_idf(bigram, book, n) %>%
arrange(desc(tf_idf))
bigram_tf_idf
## # A tibble: 60,595 × 6
## book bigram n tf idf tf_idf
## <chr> <chr> <int> <dbl> <dbl> <dbl>
## 1 The Expression of the Emotions in Man and… nerve… 47 0.00350 1.39 0.00485
## 2 On the Origin of Species By Means of Natu… natur… 250 0.0160 0.288 0.00460
## 3 The Expression of the Emotions in Man and… la ph… 35 0.00260 1.39 0.00361
## 4 The Voyage of the Beagle bueno… 54 0.00245 1.39 0.00339
## 5 The Voyage of the Beagle capta… 53 0.00240 1.39 0.00333
## 6 On the Origin of Species By Means of Natu… glaci… 36 0.00230 1.39 0.00319
## 7 The Voyage of the Beagle fitz … 50 0.00227 1.39 0.00314
## 8 The Expression of the Emotions in Man and… muscl… 30 0.00223 1.39 0.00310
## 9 The Expression of the Emotions in Man and… orbic… 29 0.00216 1.39 0.00299
## 10 The Expression of the Emotions in Man and… dr du… 26 0.00194 1.39 0.00268
## # … with 60,585 more rows
Let’s load the required package.
library(ggplot2)
Now, let’s plot.
bigram_tf_idf %>%
arrange(desc(tf_idf)) %>%
group_by(book) %>%
slice_max(tf_idf, n = 10) %>%
ungroup() %>%
dplyr:: mutate(bigram = reorder(bigram, tf_idf)) %>%
ggplot(aes(tf_idf, bigram, fill = book)) +
geom_col(show.legend = FALSE) +
facet_wrap(~book, ncol = 2, scales = "free") +
labs(x = "tf-idf of bigrams", y = NULL)
Using bigrams to provide context in sentiment analysis.
bigrams_separated %>%
filter(word1 == "not") %>%
count(word1, word2, sort = TRUE)
## # A tibble: 867 × 3
## word1 word2 n
## <chr> <chr> <int>
## 1 not be 128
## 2 not have 104
## 3 not only 103
## 4 not a 100
## 5 not to 98
## 6 not been 89
## 7 not the 82
## 8 not at 70
## 9 not know 60
## 10 not so 58
## # … with 857 more rows
By doing sentiment analysis on bigrams, we can examine how often sentiment-associated words are preceded by a modifier like “not” or other negating words. Let’s load the required package.
library(textdata)
Let’s get the data.
AFINN <- afinn
AFINN
## X word value
## 1 1 abandon -2
## 2 2 abandoned -2
## 3 3 abandons -2
## 4 4 abducted -2
## 5 5 abduction -2
## 6 6 abductions -2
## 7 7 abhor -3
## 8 8 abhorred -3
## 9 9 abhorrent -3
## 10 10 abhors -3
## 11 11 abilities 2
## 12 12 ability 2
## 13 13 aboard 1
## 14 14 absentee -1
## 15 15 absentees -1
## 16 16 absolve 2
## 17 17 absolved 2
## 18 18 absolves 2
## 19 19 absolving 2
## 20 20 absorbed 1
## 21 21 abuse -3
## 22 22 abused -3
## 23 23 abuses -3
## 24 24 abusive -3
## 25 25 accept 1
## 26 26 accepted 1
## 27 27 accepting 1
## 28 28 accepts 1
## 29 29 accident -2
## 30 30 accidental -2
## 31 31 accidentally -2
## 32 32 accidents -2
## 33 33 accomplish 2
## 34 34 accomplished 2
## 35 35 accomplishes 2
## 36 36 accusation -2
## 37 37 accusations -2
## 38 38 accuse -2
## 39 39 accused -2
## 40 40 accuses -2
## 41 41 accusing -2
## 42 42 ache -2
## 43 43 achievable 1
## 44 44 aching -2
## 45 45 acquit 2
## 46 46 acquits 2
## 47 47 acquitted 2
## 48 48 acquitting 2
## 49 49 acrimonious -3
## 50 50 active 1
## 51 51 adequate 1
## 52 52 admire 3
## 53 53 admired 3
## 54 54 admires 3
## 55 55 admiring 3
## 56 56 admit -1
## 57 57 admits -1
## 58 58 admitted -1
## 59 59 admonish -2
## 60 60 admonished -2
## 61 61 adopt 1
## 62 62 adopts 1
## 63 63 adorable 3
## 64 64 adore 3
## 65 65 adored 3
## 66 66 adores 3
## 67 67 advanced 1
## 68 68 advantage 2
## 69 69 advantages 2
## 70 70 adventure 2
## 71 71 adventures 2
## 72 72 adventurous 2
## 73 73 affected -1
## 74 74 affection 3
## 75 75 affectionate 3
## 76 76 afflicted -1
## 77 77 affronted -1
## 78 78 afraid -2
## 79 79 aggravate -2
## 80 80 aggravated -2
## 81 81 aggravates -2
## 82 82 aggravating -2
## 83 83 aggression -2
## 84 84 aggressions -2
## 85 85 aggressive -2
## 86 86 aghast -2
## 87 87 agog 2
## 88 88 agonise -3
## 89 89 agonised -3
## 90 90 agonises -3
## 91 91 agonising -3
## 92 92 agonize -3
## 93 93 agonized -3
## 94 94 agonizes -3
## 95 95 agonizing -3
## 96 96 agree 1
## 97 97 agreeable 2
## 98 98 agreed 1
## 99 99 agreement 1
## 100 100 agrees 1
## 101 101 alarm -2
## 102 102 alarmed -2
## 103 103 alarmist -2
## 104 104 alarmists -2
## 105 105 alas -1
## 106 106 alert -1
## 107 107 alienation -2
## 108 108 alive 1
## 109 109 allergic -2
## 110 110 allow 1
## 111 111 alone -2
## 112 112 amaze 2
## 113 113 amazed 2
## 114 114 amazes 2
## 115 115 amazing 4
## 116 116 ambitious 2
## 117 117 ambivalent -1
## 118 118 amuse 3
## 119 119 amused 3
## 120 120 amusement 3
## 121 121 amusements 3
## 122 122 anger -3
## 123 123 angers -3
## 124 124 angry -3
## 125 125 anguish -3
## 126 126 anguished -3
## 127 127 animosity -2
## 128 128 annoy -2
## 129 129 annoyance -2
## 130 130 annoyed -2
## 131 131 annoying -2
## 132 132 annoys -2
## 133 133 antagonistic -2
## 134 134 anti -1
## 135 135 anticipation 1
## 136 136 anxiety -2
## 137 137 anxious -2
## 138 138 apathetic -3
## 139 139 apathy -3
## 140 140 apeshit -3
## 141 141 apocalyptic -2
## 142 142 apologise -1
## 143 143 apologised -1
## 144 144 apologises -1
## 145 145 apologising -1
## 146 146 apologize -1
## 147 147 apologized -1
## 148 148 apologizes -1
## 149 149 apologizing -1
## 150 150 apology -1
## 151 151 appalled -2
## 152 152 appalling -2
## 153 153 appease 2
## 154 154 appeased 2
## 155 155 appeases 2
## 156 156 appeasing 2
## 157 157 applaud 2
## 158 158 applauded 2
## 159 159 applauding 2
## 160 160 applauds 2
## 161 161 applause 2
## 162 162 appreciate 2
## 163 163 appreciated 2
## 164 164 appreciates 2
## 165 165 appreciating 2
## 166 166 appreciation 2
## 167 167 apprehensive -2
## 168 168 approval 2
## 169 169 approved 2
## 170 170 approves 2
## 171 171 ardent 1
## 172 172 arrest -2
## 173 173 arrested -3
## 174 174 arrests -2
## 175 175 arrogant -2
## 176 176 ashame -2
## 177 177 ashamed -2
## 178 178 ass -4
## 179 179 assassination -3
## 180 180 assassinations -3
## 181 181 asset 2
## 182 182 assets 2
## 183 183 assfucking -4
## 184 184 asshole -4
## 185 185 astonished 2
## 186 186 astound 3
## 187 187 astounded 3
## 188 188 astounding 3
## 189 189 astoundingly 3
## 190 190 astounds 3
## 191 191 attack -1
## 192 192 attacked -1
## 193 193 attacking -1
## 194 194 attacks -1
## 195 195 attract 1
## 196 196 attracted 1
## 197 197 attracting 2
## 198 198 attraction 2
## 199 199 attractions 2
## 200 200 attracts 1
## 201 201 audacious 3
## 202 202 authority 1
## 203 203 avert -1
## 204 204 averted -1
## 205 205 averts -1
## 206 206 avid 2
## 207 207 avoid -1
## 208 208 avoided -1
## 209 209 avoids -1
## 210 210 await -1
## 211 211 awaited -1
## 212 212 awaits -1
## 213 213 award 3
## 214 214 awarded 3
## 215 215 awards 3
## 216 216 awesome 4
## 217 217 awful -3
## 218 218 awkward -2
## 219 219 axe -1
## 220 220 axed -1
## 221 221 backed 1
## 222 222 backing 2
## 223 223 backs 1
## 224 224 bad -3
## 225 225 badass -3
## 226 226 badly -3
## 227 227 bailout -2
## 228 228 bamboozle -2
## 229 229 bamboozled -2
## 230 230 bamboozles -2
## 231 231 ban -2
## 232 232 banish -1
## 233 233 bankrupt -3
## 234 234 bankster -3
## 235 235 banned -2
## 236 236 bargain 2
## 237 237 barrier -2
## 238 238 bastard -5
## 239 239 bastards -5
## 240 240 battle -1
## 241 241 battles -1
## 242 242 beaten -2
## 243 243 beatific 3
## 244 244 beating -1
## 245 245 beauties 3
## 246 246 beautiful 3
## 247 247 beautifully 3
## 248 248 beautify 3
## 249 249 belittle -2
## 250 250 belittled -2
## 251 251 beloved 3
## 252 252 benefit 2
## 253 253 benefits 2
## 254 254 benefitted 2
## 255 255 benefitting 2
## 256 256 bereave -2
## 257 257 bereaved -2
## 258 258 bereaves -2
## 259 259 bereaving -2
## 260 260 best 3
## 261 261 betray -3
## 262 262 betrayal -3
## 263 263 betrayed -3
## 264 264 betraying -3
## 265 265 betrays -3
## 266 266 better 2
## 267 267 bias -1
## 268 268 biased -2
## 269 269 big 1
## 270 270 bitch -5
## 271 271 bitches -5
## 272 272 bitter -2
## 273 273 bitterly -2
## 274 274 bizarre -2
## 275 275 blah -2
## 276 276 blame -2
## 277 277 blamed -2
## 278 278 blames -2
## 279 279 blaming -2
## 280 280 bless 2
## 281 281 blesses 2
## 282 282 blessing 3
## 283 283 blind -1
## 284 284 bliss 3
## 285 285 blissful 3
## 286 286 blithe 2
## 287 287 block -1
## 288 288 blockbuster 3
## 289 289 blocked -1
## 290 290 blocking -1
## 291 291 blocks -1
## 292 292 bloody -3
## 293 293 blurry -2
## 294 294 boastful -2
## 295 295 bold 2
## 296 296 boldly 2
## 297 297 bomb -1
## 298 298 boost 1
## 299 299 boosted 1
## 300 300 boosting 1
## 301 301 boosts 1
## 302 302 bore -2
## 303 303 bored -2
## 304 304 boring -3
## 305 305 bother -2
## 306 306 bothered -2
## 307 307 bothers -2
## 308 308 bothersome -2
## 309 309 boycott -2
## 310 310 boycotted -2
## 311 311 boycotting -2
## 312 312 boycotts -2
## 313 313 brainwashing -3
## 314 314 brave 2
## 315 315 breakthrough 3
## 316 316 breathtaking 5
## 317 317 bribe -3
## 318 318 bright 1
## 319 319 brightest 2
## 320 320 brightness 1
## 321 321 brilliant 4
## 322 322 brisk 2
## 323 323 broke -1
## 324 324 broken -1
## 325 325 brooding -2
## 326 326 bullied -2
## 327 327 bullshit -4
## 328 328 bully -2
## 329 329 bullying -2
## 330 330 bummer -2
## 331 331 buoyant 2
## 332 332 burden -2
## 333 333 burdened -2
## 334 334 burdening -2
## 335 335 burdens -2
## 336 336 calm 2
## 337 337 calmed 2
## 338 338 calming 2
## 339 339 calms 2
## 340 340 can't stand -3
## 341 341 cancel -1
## 342 342 cancelled -1
## 343 343 cancelling -1
## 344 344 cancels -1
## 345 345 cancer -1
## 346 346 capable 1
## 347 347 captivated 3
## 348 348 care 2
## 349 349 carefree 1
## 350 350 careful 2
## 351 351 carefully 2
## 352 352 careless -2
## 353 353 cares 2
## 354 354 cashing in -2
## 355 355 casualty -2
## 356 356 catastrophe -3
## 357 357 catastrophic -4
## 358 358 cautious -1
## 359 359 celebrate 3
## 360 360 celebrated 3
## 361 361 celebrates 3
## 362 362 celebrating 3
## 363 363 censor -2
## 364 364 censored -2
## 365 365 censors -2
## 366 366 certain 1
## 367 367 chagrin -2
## 368 368 chagrined -2
## 369 369 challenge -1
## 370 370 chance 2
## 371 371 chances 2
## 372 372 chaos -2
## 373 373 chaotic -2
## 374 374 charged -3
## 375 375 charges -2
## 376 376 charm 3
## 377 377 charming 3
## 378 378 charmless -3
## 379 379 chastise -3
## 380 380 chastised -3
## 381 381 chastises -3
## 382 382 chastising -3
## 383 383 cheat -3
## 384 384 cheated -3
## 385 385 cheater -3
## 386 386 cheaters -3
## 387 387 cheats -3
## 388 388 cheer 2
## 389 389 cheered 2
## 390 390 cheerful 2
## 391 391 cheering 2
## 392 392 cheerless -2
## 393 393 cheers 2
## 394 394 cheery 3
## 395 395 cherish 2
## 396 396 cherished 2
## 397 397 cherishes 2
## 398 398 cherishing 2
## 399 399 chic 2
## 400 400 childish -2
## 401 401 chilling -1
## 402 402 choke -2
## 403 403 choked -2
## 404 404 chokes -2
## 405 405 choking -2
## 406 406 clarifies 2
## 407 407 clarity 2
## 408 408 clash -2
## 409 409 classy 3
## 410 410 clean 2
## 411 411 cleaner 2
## 412 412 clear 1
## 413 413 cleared 1
## 414 414 clearly 1
## 415 415 clears 1
## 416 416 clever 2
## 417 417 clouded -1
## 418 418 clueless -2
## 419 419 cock -5
## 420 420 cocksucker -5
## 421 421 cocksuckers -5
## 422 422 cocky -2
## 423 423 coerced -2
## 424 424 collapse -2
## 425 425 collapsed -2
## 426 426 collapses -2
## 427 427 collapsing -2
## 428 428 collide -1
## 429 429 collides -1
## 430 430 colliding -1
## 431 431 collision -2
## 432 432 collisions -2
## 433 433 colluding -3
## 434 434 combat -1
## 435 435 combats -1
## 436 436 comedy 1
## 437 437 comfort 2
## 438 438 comfortable 2
## 439 439 comforting 2
## 440 440 comforts 2
## 441 441 commend 2
## 442 442 commended 2
## 443 443 commit 1
## 444 444 commitment 2
## 445 445 commits 1
## 446 446 committed 1
## 447 447 committing 1
## 448 448 compassionate 2
## 449 449 compelled 1
## 450 450 competent 2
## 451 451 competitive 2
## 452 452 complacent -2
## 453 453 complain -2
## 454 454 complained -2
## 455 455 complains -2
## 456 456 comprehensive 2
## 457 457 conciliate 2
## 458 458 conciliated 2
## 459 459 conciliates 2
## 460 460 conciliating 2
## 461 461 condemn -2
## 462 462 condemnation -2
## 463 463 condemned -2
## 464 464 condemns -2
## 465 465 confidence 2
## 466 466 confident 2
## 467 467 conflict -2
## 468 468 conflicting -2
## 469 469 conflictive -2
## 470 470 conflicts -2
## 471 471 confuse -2
## 472 472 confused -2
## 473 473 confusing -2
## 474 474 congrats 2
## 475 475 congratulate 2
## 476 476 congratulation 2
## 477 477 congratulations 2
## 478 478 consent 2
## 479 479 consents 2
## 480 480 consolable 2
## 481 481 conspiracy -3
## 482 482 constrained -2
## 483 483 contagion -2
## 484 484 contagions -2
## 485 485 contagious -1
## 486 486 contempt -2
## 487 487 contemptuous -2
## 488 488 contemptuously -2
## 489 489 contend -1
## 490 490 contender -1
## 491 491 contending -1
## 492 492 contentious -2
## 493 493 contestable -2
## 494 494 controversial -2
## 495 495 controversially -2
## 496 496 convince 1
## 497 497 convinced 1
## 498 498 convinces 1
## 499 499 convivial 2
## 500 500 cool 1
## 501 501 cool stuff 3
## 502 502 cornered -2
## 503 503 corpse -1
## 504 504 costly -2
## 505 505 courage 2
## 506 506 courageous 2
## 507 507 courteous 2
## 508 508 courtesy 2
## 509 509 cover-up -3
## 510 510 coward -2
## 511 511 cowardly -2
## 512 512 coziness 2
## 513 513 cramp -1
## 514 514 crap -3
## 515 515 crash -2
## 516 516 crazier -2
## 517 517 craziest -2
## 518 518 crazy -2
## 519 519 creative 2
## 520 520 crestfallen -2
## 521 521 cried -2
## 522 522 cries -2
## 523 523 crime -3
## 524 524 criminal -3
## 525 525 criminals -3
## 526 526 crisis -3
## 527 527 critic -2
## 528 528 criticism -2
## 529 529 criticize -2
## 530 530 criticized -2
## 531 531 criticizes -2
## 532 532 criticizing -2
## 533 533 critics -2
## 534 534 cruel -3
## 535 535 cruelty -3
## 536 536 crush -1
## 537 537 crushed -2
## 538 538 crushes -1
## 539 539 crushing -1
## 540 540 cry -1
## 541 541 crying -2
## 542 542 cunt -5
## 543 543 curious 1
## 544 544 curse -1
## 545 545 cut -1
## 546 546 cute 2
## 547 547 cuts -1
## 548 548 cutting -1
## 549 549 cynic -2
## 550 550 cynical -2
## 551 551 cynicism -2
## 552 552 damage -3
## 553 553 damages -3
## 554 554 damn -4
## 555 555 damned -4
## 556 556 damnit -4
## 557 557 danger -2
## 558 558 daredevil 2
## 559 559 daring 2
## 560 560 darkest -2
## 561 561 darkness -1
## 562 562 dauntless 2
## 563 563 dead -3
## 564 564 deadlock -2
## 565 565 deafening -1
## 566 566 dear 2
## 567 567 dearly 3
## 568 568 death -2
## 569 569 debonair 2
## 570 570 debt -2
## 571 571 deceit -3
## 572 572 deceitful -3
## 573 573 deceive -3
## 574 574 deceived -3
## 575 575 deceives -3
## 576 576 deceiving -3
## 577 577 deception -3
## 578 578 decisive 1
## 579 579 dedicated 2
## 580 580 defeated -2
## 581 581 defect -3
## 582 582 defects -3
## 583 583 defender 2
## 584 584 defenders 2
## 585 585 defenseless -2
## 586 586 defer -1
## 587 587 deferring -1
## 588 588 defiant -1
## 589 589 deficit -2
## 590 590 degrade -2
## 591 591 degraded -2
## 592 592 degrades -2
## 593 593 dehumanize -2
## 594 594 dehumanized -2
## 595 595 dehumanizes -2
## 596 596 dehumanizing -2
## 597 597 deject -2
## 598 598 dejected -2
## 599 599 dejecting -2
## 600 600 dejects -2
## 601 601 delay -1
## 602 602 delayed -1
## 603 603 delight 3
## 604 604 delighted 3
## 605 605 delighting 3
## 606 606 delights 3
## 607 607 demand -1
## 608 608 demanded -1
## 609 609 demanding -1
## 610 610 demands -1
## 611 611 demonstration -1
## 612 612 demoralized -2
## 613 613 denied -2
## 614 614 denier -2
## 615 615 deniers -2
## 616 616 denies -2
## 617 617 denounce -2
## 618 618 denounces -2
## 619 619 deny -2
## 620 620 denying -2
## 621 621 depressed -2
## 622 622 depressing -2
## 623 623 derail -2
## 624 624 derailed -2
## 625 625 derails -2
## 626 626 deride -2
## 627 627 derided -2
## 628 628 derides -2
## 629 629 deriding -2
## 630 630 derision -2
## 631 631 desirable 2
## 632 632 desire 1
## 633 633 desired 2
## 634 634 desirous 2
## 635 635 despair -3
## 636 636 despairing -3
## 637 637 despairs -3
## 638 638 desperate -3
## 639 639 desperately -3
## 640 640 despondent -3
## 641 641 destroy -3
## 642 642 destroyed -3
## 643 643 destroying -3
## 644 644 destroys -3
## 645 645 destruction -3
## 646 646 destructive -3
## 647 647 detached -1
## 648 648 detain -2
## 649 649 detained -2
## 650 650 detention -2
## 651 651 determined 2
## 652 652 devastate -2
## 653 653 devastated -2
## 654 654 devastating -2
## 655 655 devoted 3
## 656 656 diamond 1
## 657 657 dick -4
## 658 658 dickhead -4
## 659 659 die -3
## 660 660 died -3
## 661 661 difficult -1
## 662 662 diffident -2
## 663 663 dilemma -1
## 664 664 dipshit -3
## 665 665 dire -3
## 666 666 direful -3
## 667 667 dirt -2
## 668 668 dirtier -2
## 669 669 dirtiest -2
## 670 670 dirty -2
## 671 671 disabling -1
## 672 672 disadvantage -2
## 673 673 disadvantaged -2
## 674 674 disappear -1
## 675 675 disappeared -1
## 676 676 disappears -1
## 677 677 disappoint -2
## 678 678 disappointed -2
## 679 679 disappointing -2
## 680 680 disappointment -2
## 681 681 disappointments -2
## 682 682 disappoints -2
## 683 683 disaster -2
## 684 684 disasters -2
## 685 685 disastrous -3
## 686 686 disbelieve -2
## 687 687 discard -1
## 688 688 discarded -1
## 689 689 discarding -1
## 690 690 discards -1
## 691 691 disconsolate -2
## 692 692 disconsolation -2
## 693 693 discontented -2
## 694 694 discord -2
## 695 695 discounted -1
## 696 696 discouraged -2
## 697 697 discredited -2
## 698 698 disdain -2
## 699 699 disgrace -2
## 700 700 disgraced -2
## 701 701 disguise -1
## 702 702 disguised -1
## 703 703 disguises -1
## 704 704 disguising -1
## 705 705 disgust -3
## 706 706 disgusted -3
## 707 707 disgusting -3
## 708 708 disheartened -2
## 709 709 dishonest -2
## 710 710 disillusioned -2
## 711 711 disinclined -2
## 712 712 disjointed -2
## 713 713 dislike -2
## 714 714 dismal -2
## 715 715 dismayed -2
## 716 716 disorder -2
## 717 717 disorganized -2
## 718 718 disoriented -2
## 719 719 disparage -2
## 720 720 disparaged -2
## 721 721 disparages -2
## 722 722 disparaging -2
## 723 723 displeased -2
## 724 724 dispute -2
## 725 725 disputed -2
## 726 726 disputes -2
## 727 727 disputing -2
## 728 728 disqualified -2
## 729 729 disquiet -2
## 730 730 disregard -2
## 731 731 disregarded -2
## 732 732 disregarding -2
## 733 733 disregards -2
## 734 734 disrespect -2
## 735 735 disrespected -2
## 736 736 disruption -2
## 737 737 disruptions -2
## 738 738 disruptive -2
## 739 739 dissatisfied -2
## 740 740 distort -2
## 741 741 distorted -2
## 742 742 distorting -2
## 743 743 distorts -2
## 744 744 distract -2
## 745 745 distracted -2
## 746 746 distraction -2
## 747 747 distracts -2
## 748 748 distress -2
## 749 749 distressed -2
## 750 750 distresses -2
## 751 751 distressing -2
## 752 752 distrust -3
## 753 753 distrustful -3
## 754 754 disturb -2
## 755 755 disturbed -2
## 756 756 disturbing -2
## 757 757 disturbs -2
## 758 758 dithering -2
## 759 759 dizzy -1
## 760 760 dodging -2
## 761 761 dodgy -2
## 762 762 does not work -3
## 763 763 dolorous -2
## 764 764 dont like -2
## 765 765 doom -2
## 766 766 doomed -2
## 767 767 doubt -1
## 768 768 doubted -1
## 769 769 doubtful -1
## 770 770 doubting -1
## 771 771 doubts -1
## 772 772 douche -3
## 773 773 douchebag -3
## 774 774 downcast -2
## 775 775 downhearted -2
## 776 776 downside -2
## 777 777 drag -1
## 778 778 dragged -1
## 779 779 drags -1
## 780 780 drained -2
## 781 781 dread -2
## 782 782 dreaded -2
## 783 783 dreadful -3
## 784 784 dreading -2
## 785 785 dream 1
## 786 786 dreams 1
## 787 787 dreary -2
## 788 788 droopy -2
## 789 789 drop -1
## 790 790 drown -2
## 791 791 drowned -2
## 792 792 drowns -2
## 793 793 drunk -2
## 794 794 dubious -2
## 795 795 dud -2
## 796 796 dull -2
## 797 797 dumb -3
## 798 798 dumbass -3
## 799 799 dump -1
## 800 800 dumped -2
## 801 801 dumps -1
## 802 802 dupe -2
## 803 803 duped -2
## 804 804 dysfunction -2
## 805 805 eager 2
## 806 806 earnest 2
## 807 807 ease 2
## 808 808 easy 1
## 809 809 ecstatic 4
## 810 810 eerie -2
## 811 811 eery -2
## 812 812 effective 2
## 813 813 effectively 2
## 814 814 elated 3
## 815 815 elation 3
## 816 816 elegant 2
## 817 817 elegantly 2
## 818 818 embarrass -2
## 819 819 embarrassed -2
## 820 820 embarrasses -2
## 821 821 embarrassing -2
## 822 822 embarrassment -2
## 823 823 embittered -2
## 824 824 embrace 1
## 825 825 emergency -2
## 826 826 empathetic 2
## 827 827 emptiness -1
## 828 828 empty -1
## 829 829 enchanted 2
## 830 830 encourage 2
## 831 831 encouraged 2
## 832 832 encouragement 2
## 833 833 encourages 2
## 834 834 endorse 2
## 835 835 endorsed 2
## 836 836 endorsement 2
## 837 837 endorses 2
## 838 838 enemies -2
## 839 839 enemy -2
## 840 840 energetic 2
## 841 841 engage 1
## 842 842 engages 1
## 843 843 engrossed 1
## 844 844 enjoy 2
## 845 845 enjoying 2
## 846 846 enjoys 2
## 847 847 enlighten 2
## 848 848 enlightened 2
## 849 849 enlightening 2
## 850 850 enlightens 2
## 851 851 ennui -2
## 852 852 enrage -2
## 853 853 enraged -2
## 854 854 enrages -2
## 855 855 enraging -2
## 856 856 enrapture 3
## 857 857 enslave -2
## 858 858 enslaved -2
## 859 859 enslaves -2
## 860 860 ensure 1
## 861 861 ensuring 1
## 862 862 enterprising 1
## 863 863 entertaining 2
## 864 864 enthral 3
## 865 865 enthusiastic 3
## 866 866 entitled 1
## 867 867 entrusted 2
## 868 868 envies -1
## 869 869 envious -2
## 870 870 envy -1
## 871 871 envying -1
## 872 872 erroneous -2
## 873 873 error -2
## 874 874 errors -2
## 875 875 escape -1
## 876 876 escapes -1
## 877 877 escaping -1
## 878 878 esteemed 2
## 879 879 ethical 2
## 880 880 euphoria 3
## 881 881 euphoric 4
## 882 882 eviction -1
## 883 883 evil -3
## 884 884 exaggerate -2
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## 2472 2472 youthful 2
## 2473 2473 yucky -2
## 2474 2474 yummy 3
## 2475 2475 zealot -2
## 2476 2476 zealots -2
## 2477 2477 zealous 2
We can examine the most frequent words that were preceded by “not”, and associate with sentiment.
not_words <- bigrams_separated %>%
filter(word1 == "not") %>%
inner_join(AFINN, by = c(word2 = "word")) %>%
count(word2, value, sort = TRUE)
not_words
## # A tibble: 114 × 3
## word2 value n
## <chr> <int> <int>
## 1 doubt -1 25
## 2 like 2 11
## 3 pretend -1 9
## 4 wish 1 8
## 5 admit -1 7
## 6 difficult -1 5
## 7 easy 1 5
## 8 reach 1 5
## 9 extend 1 4
## 10 forget -1 4
## # … with 104 more rows
Let’s visualize
library(ggplot2)
not_words %>%
dplyr::mutate(contribution = n * value) %>%
arrange(desc(abs(contribution))) %>%
head(20) %>%
dplyr::mutate(word2 = reorder(word2, contribution)) %>%
ggplot(aes(n * value, word2, fill = n * value > 0)) +
geom_col(show.legend = FALSE) +
labs(x = "Sentiment value * number of occurences", y = "words preceded by \"not\"")
negation_words <- c("not", "no", "never", "non", "without")
negated_words <- bigrams_separated %>%
filter(word1 %in% negation_words) %>%
inner_join(AFINN, by = c(word2 = "word")) %>%
count(word1, word2, value, sort = TRUE)
negated_words
## # A tibble: 208 × 4
## word1 word2 value n
## <chr> <chr> <int> <int>
## 1 no doubt -1 210
## 2 not doubt -1 25
## 3 no great 3 19
## 4 not like 2 11
## 5 not pretend -1 9
## 6 not wish 1 8
## 7 without doubt -1 8
## 8 not admit -1 7
## 9 no greater 3 6
## 10 not difficult -1 5
## # … with 198 more rows
Let’s visualize the negation words.
negated_words %>%
dplyr::mutate(contribution = n *value,
word2 = reorder(paste(word2, word1, sep = "_"), contribution)) %>%
group_by(word1) %>%
slice_max(abs(contribution), n = 12, with_ties = FALSE) %>%
ggplot(aes(word2, contribution, fill = n * value > 0)) +
geom_col(show.legend = FALSE) +
facet_wrap(~word1, scales = "free") +
scale_x_discrete(labels = function(x) gsub("_.+$", "", x)) +
xlab("words preceded by negation term") +
ylab("Sentiment value * # of occurences") +
coord_flip()
Visualize a network of bigrams with ggraph
library(igraph)
##
## Attaching package: 'igraph'
## The following object is masked from 'package:mosaic':
##
## compare
## The following objects are masked from 'package:lubridate':
##
## %--%, union
## The following object is masked from 'package:plotly':
##
## groups
## The following object is masked from 'package:tidyr':
##
## crossing
## The following objects are masked from 'package:dplyr':
##
## as_data_frame, groups, union
## The following objects are masked from 'package:stats':
##
## decompose, spectrum
## The following object is masked from 'package:base':
##
## union
bigram_counts <- bigrams_filtered %>%
count(word1, word2, sort = TRUE)
bigram_graph <- bigram_counts %>%
filter(n > 20) %>%
graph_from_data_frame()
## Warning in graph_from_data_frame(.): In `d' `NA' elements were replaced with
## string "NA"
bigram_graph
## IGRAPH 20653a8 DN-- 203 140 --
## + attr: name (v/c), n (e/n)
## + edges from 20653a8 (vertex names):
## [1] NA ->NA natural ->selection sexual ->selection
## [4] vol ->ii lower ->animals sexual ->differences
## [7] south ->america distinct ->species secondary ->sexual
## [10] breeding ->season closely ->allied sexual ->characters
## [13] tierra ->del del ->fuego vol ->iii
## [16] de ->la natural ->history fresh ->water
## [19] north ->america bright ->colours sexual ->difference
## [22] allied ->species tail ->feathers strongly ->marked
## + ... omitted several edges
Let’s graph the data
library(ggraph)
set.seed(1234)
ggraph(bigram_graph, layout = "fr") +
geom_edge_link() +
geom_node_point() +
geom_node_text(aes(label = name), vjust = 1, hjust = 1)
We can also add directionality to this network.
set.seed(1234)
a <- grid::arrow(type = "closed", length = unit(0.15, "inches"))
ggraph(bigram_graph, layout = "fr") +
geom_edge_link(aes(edge_alpha = n), show.legend = FALSE, arrow = a, end_cap = circle(0.7, 'inches')) +
geom_node_point(color = "lightblue", size = 5) +
geom_node_text(aes(label = name), vjust = 1, hjust = 1) +
theme_void()
A control question in text mining is how to quantify what a document is about. We can do this by looking at words that make up the document, and measuring the term frequency. There are a lot of words that may not be important, these are the stop words. One way to remedy this is to look at inverse document frequency words, which decreases the weight for commonly used words and increases the weight for words that are not used very much. Term frequency in Darwin’s work.
library(dplyr)
library(tidytext)
book_words <- gutenberg_download(c(944, 1227, 1228, 2300), mirror = "https://mirror2.sandyriver.net/pub/gutenberg")
colnames(book_words)[1] <- "book"
book_words$book[book_words$book == 944] <- "The Voyage of the Beagle"
book_words$book[book_words$book == 1227] <- "The Expression of the Emotions in Man and Animals"
book_words$book[book_words$book == 1228] <- "On the Origin of Species By Means of Natural Selection"
book_words$book[book_words$book == 2300] <- "The Descent of Man, and Selection in Relation to Sex"
Now, let’s dissect
book_words <- book_words %>%
unnest_tokens(word, text) %>%
count(book, word, sort = TRUE)
book_words
## # A tibble: 43,024 × 3
## book word n
## <chr> <chr> <int>
## 1 The Descent of Man, and Selection in Relation to Sex the 25490
## 2 The Voyage of the Beagle the 16930
## 3 The Descent of Man, and Selection in Relation to Sex of 16762
## 4 On the Origin of Species By Means of Natural Selection the 10301
## 5 The Voyage of the Beagle of 9438
## 6 The Descent of Man, and Selection in Relation to Sex in 8882
## 7 The Expression of the Emotions in Man and Animals the 8045
## 8 On the Origin of Species By Means of Natural Selection of 7864
## 9 The Descent of Man, and Selection in Relation to Sex and 7854
## 10 The Descent of Man, and Selection in Relation to Sex to 5901
## # … with 43,014 more rows
book_words$n <- as.numeric(book_words$n)
total_words <- book_words %>%
group_by(book) %>%
dplyr::summarize(total = sum(n))
book_words
## # A tibble: 43,024 × 3
## book word n
## <chr> <chr> <dbl>
## 1 The Descent of Man, and Selection in Relation to Sex the 25490
## 2 The Voyage of the Beagle the 16930
## 3 The Descent of Man, and Selection in Relation to Sex of 16762
## 4 On the Origin of Species By Means of Natural Selection the 10301
## 5 The Voyage of the Beagle of 9438
## 6 The Descent of Man, and Selection in Relation to Sex in 8882
## 7 The Expression of the Emotions in Man and Animals the 8045
## 8 On the Origin of Species By Means of Natural Selection of 7864
## 9 The Descent of Man, and Selection in Relation to Sex and 7854
## 10 The Descent of Man, and Selection in Relation to Sex to 5901
## # … with 43,014 more rows
book_words <- left_join(book_words, total_words)
## Joining, by = "book"
book_words
## # A tibble: 43,024 × 4
## book word n total
## <chr> <chr> <dbl> <dbl>
## 1 The Descent of Man, and Selection in Relation to Sex the 25490 311041
## 2 The Voyage of the Beagle the 16930 208118
## 3 The Descent of Man, and Selection in Relation to Sex of 16762 311041
## 4 On the Origin of Species By Means of Natural Selection the 10301 157002
## 5 The Voyage of the Beagle of 9438 208118
## 6 The Descent of Man, and Selection in Relation to Sex in 8882 311041
## 7 The Expression of the Emotions in Man and Animals the 8045 110414
## 8 On the Origin of Species By Means of Natural Selection of 7864 157002
## 9 The Descent of Man, and Selection in Relation to Sex and 7854 311041
## 10 The Descent of Man, and Selection in Relation to Sex to 5901 311041
## # … with 43,014 more rows
You can see that the unusual suspects are the most common words, but do not tell us anything about what the book’s topic is.
library(ggplot2)
ggplot(book_words, aes(n/total, fill=book)) +
geom_histogram(show.legend=FALSE) +
xlim(NA, 0.0009) +
facet_wrap(~book, ncol=2, scales='free_y')
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 515 rows containing non-finite values (stat_bin).
## Warning: Removed 4 rows containing missing values (geom_bar).
Zipf’s Law The frequency that a word appears inversely proportional to its rank when predicting a topic. Let’s apply Zipf’s Law to Darwin’s work.
freq_by_rank <- book_words %>%
group_by(book) %>%
dplyr::mutate(rank = row_number(),
'term frequency' = n/total) %>%
ungroup()
freq_by_rank
## # A tibble: 43,024 × 6
## book word n total rank term …¹
## <chr> <chr> <dbl> <dbl> <int> <dbl>
## 1 The Descent of Man, and Selection in Relati… the 25490 311041 1 0.0820
## 2 The Voyage of the Beagle the 16930 208118 1 0.0813
## 3 The Descent of Man, and Selection in Relati… of 16762 311041 2 0.0539
## 4 On the Origin of Species By Means of Natura… the 10301 157002 1 0.0656
## 5 The Voyage of the Beagle of 9438 208118 2 0.0453
## 6 The Descent of Man, and Selection in Relati… in 8882 311041 3 0.0286
## 7 The Expression of the Emotions in Man and A… the 8045 110414 1 0.0729
## 8 On the Origin of Species By Means of Natura… of 7864 157002 2 0.0501
## 9 The Descent of Man, and Selection in Relati… and 7854 311041 4 0.0253
## 10 The Descent of Man, and Selection in Relati… to 5901 311041 5 0.0190
## # … with 43,014 more rows, and abbreviated variable name ¹`term frequency`
Let’s plot the data
freq_by_rank %>%
ggplot(aes(rank, `term frequency`, color = book)) +
geom_line(size = 1.1, alpha = 0.8, show.legend = FALSE) +
scale_x_log10() +
scale_y_log10()
Let’s use TF – IDF to find words for each document by decreasing the weight from commonly used words and increasing the weight for words that are not used very much in a collection of documents.
book_tf_idf <- book_words %>%
bind_tf_idf(word, book, n)
book_tf_idf
## # A tibble: 43,024 × 7
## book word n total tf idf tf_idf
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 The Descent of Man, and Selection in … the 25490 311041 0.0820 0 0
## 2 The Voyage of the Beagle the 16930 208118 0.0813 0 0
## 3 The Descent of Man, and Selection in … of 16762 311041 0.0539 0 0
## 4 On the Origin of Species By Means of … the 10301 157002 0.0656 0 0
## 5 The Voyage of the Beagle of 9438 208118 0.0453 0 0
## 6 The Descent of Man, and Selection in … in 8882 311041 0.0286 0 0
## 7 The Expression of the Emotions in Man… the 8045 110414 0.0729 0 0
## 8 On the Origin of Species By Means of … of 7864 157002 0.0501 0 0
## 9 The Descent of Man, and Selection in … and 7854 311041 0.0253 0 0
## 10 The Descent of Man, and Selection in … to 5901 311041 0.0190 0 0
## # … with 43,014 more rows
Let’s look at terms with high tf-idf in Darwin’s works.
book_tf_idf %>%
select(-total) %>%
arrange(desc(tf_idf))
## # A tibble: 43,024 × 6
## book word n tf idf tf_idf
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 The Expression of the Emotions in Man and … tears 126 1.14e-3 1.39 1.58e-3
## 2 The Expression of the Emotions in Man and … blush 114 1.03e-3 1.39 1.43e-3
## 3 The Expression of the Emotions in Man and … eyeb… 149 1.35e-3 0.693 9.35e-4
## 4 The Voyage of the Beagle degs 117 5.62e-4 1.39 7.79e-4
## 5 On the Origin of Species By Means of Natur… sele… 412 2.62e-3 0.288 7.55e-4
## 6 The Descent of Man, and Selection in Relat… sexu… 745 2.40e-3 0.288 6.89e-4
## 7 The Descent of Man, and Selection in Relat… shewn 143 4.60e-4 1.39 6.37e-4
## 8 On the Origin of Species By Means of Natur… hybr… 133 8.47e-4 0.693 5.87e-4
## 9 The Expression of the Emotions in Man and … frown 46 4.17e-4 1.39 5.78e-4
## 10 The Descent of Man, and Selection in Relat… sele… 621 2.00e-3 0.288 5.74e-4
## # … with 43,014 more rows
Let’s load the required package
library(forcats)
Let’s look at a visualization for these tf-idf words.
book_tf_idf %>%
group_by(book) %>%
slice_max(tf_idf, n = 15) %>%
ungroup() %>%
ggplot(aes(tf_idf, fct_reorder(word, tf_idf), fill = book)) +
geom_col(show.legend = FALSE) +
facet_wrap(~book, ncol = 2, scales = "free") +
labs(x = "tf-idf", y = NULL)
First thing, let’s load our required libraries and data Let’s load the Canada Data
tweets20CA<-read.csv("tweets20CA.csv", row.names = 1)
tweets21CA <- read.csv("tweets21CA.csv", row.names = 1)
tweets22CA <- read.csv("tweets22CA.csv", row.names = 1)
Let’s load the US data.
tweets20US <- read.csv("tweets20us.csv", row.names = 1)
tweets21US <- read.csv("tweets21us.csv", row.names = 1)
tweets22US <- read.csv("tweets22us.csv", row.names = 1)
Let’s load the GB data.
tweets20GB <- read.csv("tweets20GB.csv", row.names = 1)
tweets21GB <- read.csv("tweets21GB.csv", row.names = 1)
tweets22GB <- read.csv("tweets22GB.csv", row.names = 1)
Now, let’s load our required libraries.
library(lubridate)
library(ggplot2)
library(dplyr)
library(readr)
Let’s combine our US tweet.
tweetsUS <- bind_rows(tweets21US %>%
dplyr:: mutate(year = "y2021"),
tweets22US %>%
dplyr:: mutate(year = "y2022"),
tweets20US %>%
dplyr:: mutate(year = "y2020")) %>%
dplyr::mutate(timestamp = ymd_hms(created_at))
Let’s combine our GB tweets.
tweetsGB <- bind_rows(tweets21GB %>%
dplyr::mutate(year = "y2021"),
tweets22GB %>%
dplyr::mutate(year = "y2022"),
tweets20GB %>%
dplyr::mutate(year = "y2020")) %>%
dplyr::mutate(timestamp = ymd_hms(created_at))
Let’s combine our Canada tweets.
tweetsCA <- bind_rows(tweets21CA %>%
dplyr:: mutate(year = "y2021"),
tweets22CA %>%
dplyr::mutate(year = "y2022"),
tweets20CA %>%
dplyr::mutate(year = "y2020")) %>%
dplyr::mutate(timestamp = ymd_hms(created_at))
Load required libraries
library(stringr)
library(tidytext)
Now, let’s clean our US tweets.
remove_reg <- "&|<|>"
tidy_tweetsUS <- tweetsUS %>%
filter(!str_detect(text, "^RT")) %>%
dplyr::mutate(text = str_remove_all(text, remove_reg)) %>%
unnest_tokens(word, text, token = "tweets") %>%
filter(!word %in% stop_words$word,
!word %in% str_remove_all(stop_words$word, "'"),
str_detect(word, "[a-z]"))
## Using `to_lower = TRUE` with `token = 'tweets'` may not preserve URLs.
Now, let’s clean our GB tweets.
remove_reg <- "&|<|>"
tidy_tweetsGB <- tweetsGB %>%
filter(!str_detect(text, "^RT")) %>%
dplyr:: mutate(text = str_remove_all(text, remove_reg)) %>%
unnest_tokens(word, text, token = "tweets") %>%
filter(!word %in% stop_words$word,
!word %in% str_remove_all(stop_words$word, "'"),
str_detect(word, "[a-z]"))
## Using `to_lower = TRUE` with `token = 'tweets'` may not preserve URLs.
Now, let’s clean our Canada tweets.
remove_reg <- "&|<|>"
tidy_tweetsCA <- tweetsCA %>%
filter(!str_detect(text, "^RT")) %>%
dplyr::mutate(text = str_remove_all(text, remove_reg)) %>%
unnest_tokens(word, text, token = "tweets") %>%
filter(!word %in% stop_words$word,
!word %in% str_remove_all(stop_words$word, "'"),
str_detect(word, "[a-z]"))
## Using `to_lower = TRUE` with `token = 'tweets'` may not preserve URLs.
Let’s take a look at our US data.
ggplot(tweetsUS, aes(x = timestamp, fill = year)) +
geom_histogram(position = "identity", bins = 20, show.legend = FALSE) +
facet_wrap(~year, ncol = 1)
Let’s look at our GB data
ggplot(tweetsGB, aes(x = timestamp, fill = year)) +
geom_histogram(position = "identity", bins = 20, show.legend = FALSE) +
facet_wrap(~year, ncol = 1)
Let’s load our Canada data.
ggplot(tweetsCA, aes(x = timestamp, fill = year)) +
geom_histogram(position = "identity", bins = 20, show.legend = FALSE) +
facet_wrap(~year, ncol = 1)
Let’s look at the word frequencies for the US COVID data.
frequencyUS <- tidy_tweetsUS %>%
count(year, word, sort = TRUE) %>%
left_join(tidy_tweetsUS %>%
count(year, name = "total")) %>%
dplyr::mutate(freq = n/total)
## Joining, by = "year"
frequencyUS
## year
## 1 y2021
## 2 y2020
## 3 y2022
## 4 y2022
## 5 y2021
## 6 y2020
## 7 y2021
## 8 y2022
## 9 y2021
## 10 y2020
## 11 y2020
## 12 y2021
## 13 y2022
## 14 y2022
## 15 y2022
## 16 y2020
## 17 y2022
## 18 y2020
## 19 y2021
## 20 y2020
## 21 y2022
## 22 y2022
## 23 y2021
## 24 y2021
## 25 y2021
## 26 y2020
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## 28 y2020
## 29 y2021
## 30 y2020
## 31 y2022
## 32 y2021
## 33 y2020
## 34 y2021
## 35 y2021
## 36 y2022
## 37 y2020
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## word
## 1 covid19
## 2 covid19
## 3 covid19
## 4 #covid19
## 5 vaccine
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## 7 covid
## 8 covid
## 9 people
## 10 people
## 11 #covid19
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## 29 trump
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## 81 day
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## 1931 heard
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## 16016 #antivaxxerskill
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## 16019 #autismprowrestling
## 16020 #bachelor
## 16021 #bayarea
## 16022 #benfrazier
## 16023 #biden
## 16024 #bitcoin
## 16025 #bloodclot
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## 16039 #covidisnotdonewithus
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## 16041 #covidsucks
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## 16076 #gopdeathcult
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## 16111 #maryland
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## 16153 #representationmatters
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## 16162 #sfbayarea
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## 16190 #vaccinemandates
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## 19919 1 66780 1.497454e-05
## 19920 1 66780 1.497454e-05
## 19921 1 66780 1.497454e-05
## 19922 1 66780 1.497454e-05
## 19923 1 66780 1.497454e-05
## 19924 1 66780 1.497454e-05
## 19925 1 66780 1.497454e-05
## 19926 1 66780 1.497454e-05
## 19927 1 66780 1.497454e-05
## 19928 1 66780 1.497454e-05
## 19929 1 66780 1.497454e-05
## 19930 1 66780 1.497454e-05
## 19931 1 66780 1.497454e-05
## 19932 1 66780 1.497454e-05
## 19933 1 66780 1.497454e-05
## 19934 1 66780 1.497454e-05
## 19935 1 66780 1.497454e-05
## 19936 1 66780 1.497454e-05
## 19937 1 66780 1.497454e-05
## 19938 1 66780 1.497454e-05
## 19939 1 66780 1.497454e-05
## 19940 1 66780 1.497454e-05
## 19941 1 66780 1.497454e-05
## 19942 1 66780 1.497454e-05
## 19943 1 66780 1.497454e-05
## 19944 1 66780 1.497454e-05
## 19945 1 66780 1.497454e-05
## 19946 1 66780 1.497454e-05
## 19947 1 66780 1.497454e-05
## 19948 1 66780 1.497454e-05
## 19949 1 66780 1.497454e-05
## 19950 1 66780 1.497454e-05
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## 19962 1 66780 1.497454e-05
## 19963 1 66780 1.497454e-05
## 19964 1 66780 1.497454e-05
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## 19967 1 66780 1.497454e-05
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## 19971 1 66780 1.497454e-05
## 19972 1 66780 1.497454e-05
## 19973 1 66780 1.497454e-05
## 19974 1 66780 1.497454e-05
## 19975 1 66780 1.497454e-05
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## 19978 1 66780 1.497454e-05
## 19979 1 66780 1.497454e-05
## 19980 1 66780 1.497454e-05
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## 19986 1 66780 1.497454e-05
## 19987 1 66780 1.497454e-05
## 19988 1 66780 1.497454e-05
## 19989 1 66780 1.497454e-05
## 19990 1 66780 1.497454e-05
## 19991 1 66780 1.497454e-05
## 19992 1 66780 1.497454e-05
## 19993 1 66780 1.497454e-05
## 19994 1 66780 1.497454e-05
## 19995 1 66780 1.497454e-05
## 19996 1 66780 1.497454e-05
## 19997 1 66780 1.497454e-05
## 19998 1 66780 1.497454e-05
## 19999 1 66780 1.497454e-05
## [ reached 'max' / getOption("max.print") -- omitted 41509 rows ]
Let’s look at the word frequencies for the GB COVID data.
frequencyGB <- tidy_tweetsGB %>%
count(year, word, sort = TRUE) %>%
left_join(tidy_tweetsGB %>%
count(year, name = "total")) %>%
dplyr::mutate(freq = n/total)
## Joining, by = "year"
frequencyGB
## year
## 1 y2020
## 2 y2021
## 3 y2020
## 4 y2020
## 5 y2021
## 6 y2022
## 7 y2020
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## word
## 1 covid19
## 2 covid19
## 3 #covid19
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## 5 covid
## 6 covid19
## 7 people
## 8 #covid19
## 9 people
## 10 vaccine
## 11 #covid19
## 12 uk
## 13 uk
## 14 vaccination
## 15 deaths
## 16 lockdown
## 17 pandemic
## 18 lockdown
## 19 government
## 20 petition
## 21 due
## 22 staff
## 23 time
## 24 support
## 25 covid
## 26 it�s
## 27 school
## 28 news
## 29 health
## 30 time
## 31 government
## 32 deaths
## 33 petition
## 34 day
## 35 #coronavirus
## 36 due
## 37 nhs
## 38 social
## 39 crisis
## 40 health
## 41 day
## 42 coronavirus
## 43 pandemic
## 44 safe
## 45 home
## 46 @borisjohnson
## 47 death
## 48 world
## 49 impact
## 50 it�s
## 51 care
## 52 week
## 53 nhs
## 54 prioritise
## 55 people
## 56 lives
## 57 support
## 58 test
## 59 response
## 60 positive
## 61 bame
## 62 virus
## 63 public
## 64 daily
## 65 england
## 66 vaccines
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## 68 stay
## 69 risk
## 70 home
## 71 news
## 72 hospital
## 73 london
## 74 test
## 75 virus
## 76 teachers
## 77 distancing
## 78 hope
## 79 months
## 80 june
## 81 staff
## 82 safe
## 83 coronavirus
## 84 testing
## 85 london
## 86 country
## 87 care
## 88 post
## 89 sign
## 90 spread
## 91 positive
## 92 died
## 93 childcare
## 94 patients
## 95 black
## 96 weeks
## 97 days
## 98 vaccine
## 99 days
## 100 children
## 101 read
## 102 world
## 103 death
## 104 schools
## 105 i�m
## 106 @borisjohnson
## 107 public
## 108 read
## 109 restrictions
## 110 risk
## 111 police
## 112 report
## 113 life
## 114 rules
## 115 community
## 116 taking
## 117 team
## 118 week
## 119 protests
## 120 don�t
## 121 update
## 122 boris
## 123 brexit
## 124 january
## 125 #lockdown
## 126 local
## 127 protest
## 128 team
## 129 positive
## 130 hope
## 131 johnson
## 132 patients
## 133 update
## 134 national
## 135 spread
## 136 economy
## 137 stay
## 138 wave
## 139 police
## 140 live
## 141 worst
## 142 #lockdown
## 143 boris
## 144 received
## 145 sign
## 146 service
## 147 workers
## 148 change
## 149 hospital
## 150 tested
## 151 community
## 152 i�m
## 153 scotland
## 154 stop
## 155 bbc
## 156 vaccinations
## 157 can�t
## 158 join
## 159 school
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## 163 de
## 164 #coronavirus
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## 166 business
## 167 matter
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## 171 family
## 172 recovery
## 173 toll
## 174 dose
## 175 major
## 176 months
## 177 tests
## 178 test
## 179 money
## 180 proud
## 181 @updayuk
## 182 day
## 183 england
## 184 free
## 185 homes
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## 187 died
## 188 family
## 189 stop
## 190 hard
## 191 job
## 192 racism
## 193 situation
## 194 amazing
## 195 daily
## 196 grant
## 197 research
## 198 happy
## 199 local
## 200 pandemic
## 201 current
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## 203 i�ve
## 204 country
## 205 cummings
## 206 forward
## 207 future
## 208 lot
## 209 total
## 210 life
## 211 scotland
## 212 total
## 213 article
## 214 deal
## 215 feel
## 216 global
## 217 handling
## 218 signed
## 219 times
## 220 #covid
## 221 centre
## 222 data
## 223 records
## 224 nhs
## 225 families
## 226 food
## 227 link
## 228 paid
## 229 symptoms
## 230 health
## 231 @ukchange
## 232 affected
## 233 left
## 234 thousands
## 235 @piersmorgan
## 236 coming
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## 238 march
## 239 ready
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## 245 issues
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## 247 start
## 248 #nhs
## 249 programme
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## 258 online
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## 262 feel
## 263 information
## 264 jab
## 265 lost
## 266 negative
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## 268 time
## 269 call
## 270 economic
## 271 en
## 272 figures
## 273 friends
## 274 govt
## 275 masks
## 276 protesting
## 277 you�re
## 278 declared
## 279 follow
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## 281 social
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## 283 confirmed
## 284 countries
## 285 media
## 286 negative
## 287 close
## 288 incident
## 289 lives
## 290 real
## 291 allowed
## 292 communities
## 293 continue
## 294 dominic
## 295 johnson
## 296 makes
## 297 million
## 298 morning
## 299 rate
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## 301 whilst
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## 305 response
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## 307 days
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## 310 ago
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## 312 hit
## 313 hours
## 314 infection
## 315 information
## 316 services
## 317 understand
## 318 @ukchange
## 319 call
## 320 contact
## 321 free
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## 5080 https://t.co/vkwed0d8hu
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## 16733 #blmldn
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## 16751 #bookyourfacial
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## 16754 #borisjohnsonshouldnotbepm
## 16755 #borismeltdown
## 16756 #borismustgo
## 16757 #borisresign
## 16758 #borisvoteofnoconfidence
## 16759 #borrisbubble
## 16760 #borrisgiveyourheadawobble
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## 16857 #changingtheconvo
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## 16867 #chicken
## 16868 #childfriendlycities
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## 16876 #churchillthehero
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## 16902 #comebackstronger
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## 16981 #croweaudit
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## 16995 #cyclecommuter
## 16996 #cyclistsofinstagram
## 16997 #cymru
## 16998 #cymryambyth
## 16999 #cyrraeddsero
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## 17010 #daysofthebagnoldsummer
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## 17019 #dementiaawarenessweek2020
## 17020 #dementiafriendlycommunities
## 17021 #democracyunderthreat
## 17022 #dental
## 17023 #derby
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## 17025 #designer
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## 17033 #digitalagriculture
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## 17039 #dissolvetheunion
## 17040 #distancetraining
## 17041 #ditchthetories
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## 17054 #dogsarefamily
## 17055 #dogsofinstagram
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## 17162 #feedthoseinneed
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## 17192 #foodlovestories
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## 17252 #gmb<u+0001f304>piersmorgan<u+0001f496>susannareid100<u+0001f496>gmb
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## 17277 #group�alismasi
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## 17281 #gym
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## 17287 #hancockmustgo
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## 17293 #happytuesdayeveryone
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## 17374 #ignoretheconspiracytheories
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## 17517 #learnwithoak
## 17518 #leftbehindbyrishi
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## 17524 #lesiure
## 17525 #lestweforget
## 17526 #letsbamethegoverment
## 17527 #levellingup
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## 17549 #lizmcburneyphotographer
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## 17553 #localintimacy
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## 17559 #lockdownjournal
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## 17570 #lookforwardtoseeingyou
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## 17572 #lovedonthate
## 17573 #lovedosenthurt
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## 17577 #lovemk
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## 17581 #lutonweddings
## 17582 #machiavelli
## 17583 #maddiemccann
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## 18447 #wmidshr
## 18448 #woman
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## 18451 #wontyoubemyneighbor
## 18452 #woodmill
## 18453 #work
## 18454 #workerspartyofgreatbritain
## 18455 #workfromhome
## 18456 #workinghardfordevon
## 18457 #worksop
## 18458 #worldbeating
## 18459 #worlddayagainstchildlabour
## 18460 #worldenvironmentday
## 18461 #worldhealthorganization
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## 18463 #worldoceanday
## 18464 #worldsgonemad
## 18465 #wovenflowers
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## 18482 #yestoantiterrorbill
## 18483 #yoga
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## 18485 #youarecausingit
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## 18487 #youchoose
## 18488 #yourdaysarenumbered
## 18489 #yourvoice
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## 18791 <u+0001f914><u+0001f926><u+0001f3fd><u+200d><u+2642><u+fe0f><u+2b50><u+2b50><u+2b50><u+2b50><u+2b50><u+2b50>bcnlegendz
## 18792 <u+0001f914><u+0001f9d0><u+0001f928><u+0001f914>
## 18793 <u+0001f914>as
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## 18802 <u+0001f91b>
## 18803 <u+0001f91e><u+0001f91e><u+0001f64f><u+0001f64f>
## 18804 <u+0001f921>
## 18805 <u+0001f921>�s
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## 18808 <u+0001f923><u+0001f64f><u+2764><u+fe0f>
## 18809 <u+0001f923><u+0001f923><u+0001f923><u+0001f923><u+0001f923>
## 18810 <u+0001f923><u+0001f92b>
## 18811 <u+0001f923>if
## 18812 <u+0001f924>
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## 18814 <u+0001f926><u+0001f3fc><u+200d><u+2642><u+fe0f><u+0001f926><u+0001f3fc><u+200d><u+2642><u+fe0f>
## 18815 <u+0001f926><u+0001f3fd><u+200d><u+2640><u+fe0f><u+0001f926><u+0001f3fd><u+200d><u+2640><u+fe0f><u+0001f926><u+0001f3fd><u+200d><u+2640><u+fe0f>
## 18816 <u+0001f926><u+0001f3fd><u+200d><u+2642><u+fe0f>
## 18817 <u+0001f926><u+0001f926><u+0001f926>
## 18818 <u+0001f926><u+200d><u+2642><u+fe0f>30
## 18819 <u+0001f927><u+0001f912><u+0001f61d>
## 18820 <u+0001f929><u+0001f44d><u+0001f340><u+0001f633><u+0001f4af><u+2705><u+0001f30d><u+0001f970><u+0001f49d><u+0001f44c><u+0001f60e><u+0001f609>
## 18821 <u+0001f929><u+0001f44d>thats
## 18822 <u+0001f929><u+0001f4ab>
## 18823 <u+0001f929><u+0001f60e><u+0001f60d>lakedistrict
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## 18827 <u+0001f92b>
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## 18830 <u+0001f92e>
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## 18835 <u+0001f937><u+200d><u+2642><u+fe0f>they
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## 18843 <u+0001f973><u+0001f973>
## 18844 <u+0001f974><u+0001f603>
## 18845 <u+0001f976>
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## 18847 <u+0001f97a><u+0001f97a>
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## 18849 <u+0001f97c><u+0001f97d><u+0001f601>
## 18850 <u+0001f97e>
## 18851 <u+0001f97e>cumbrianrambler
## 18852 <u+0001f9b7>
## 18853 <u+0001f9d7><u+200d><u+2640><u+fe0f>first
## 18854 <u+0001f9e1><u+0001f5a4><u+0001f43a>covid19
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## 18859 <u+0395><u+03bb><u+03bb><u+03ac>da
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## 18863 <u+03ba><u+03bf><u+03bb><u+03bb><u+03ac><u+03bd>e
## 18864 <u+03ba><u+03bf>��<u+03c9>t<u+03ae><u+03c1><u+03b9><u+03bf>
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## 18866 <u+03c1>e
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## 18868 <u+0412>�<u+041c><u+043e><u+0441><u+043a><u+0432><u+0435>
## 18869 <u+0412>�<u+0431><u+0443><u+0440><u+044f><u+0442><u+0441><u+043a><u+043e><u+0439>
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## 18875 <u+0420><u+0424>�<u+043d><u+0435>�<u+0443><u+0432><u+0438><u+0434><u+0435><u+043b><u+0438>
## 18876 <u+0430>
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## 18879 <u+0432><u+0432><u+0435><u+0434><u+0435><u+043d><u+043d><u+044b><u+0445>
## 18880 <u+0432><u+043e><u+0442>
## 18881 <u+0432>�<u+043e><u+0433><u+0440><u+0430><u+043d><u+0438><u+0447><u+0435><u+043d><u+0438><u+044f><u+0445>
## 18882 <u+0432>�<u+0442><u+0443><u+0430><u+043b><u+0435><u+0442>
## 18883 <u+0433><u+0440><u+0430><u+0436><u+0434><u+0430><u+043d>
## 18884 <u+0436><u+0435>
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## 18886 <u+0437><u+0430><u+0449><u+0438><u+0442><u+044b>
## 18887 <u+0438><u+0437>
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## 18889 <u+0438><u+043b><u+0438>
## 18890 <u+0438><u+0445>
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## 18892 <u+043d><u+0430><u+0440><u+0443><u+0448><u+0435><u+043d><u+0438><u+0439>
## 18893 <u+043d><u+0430>�<u+043e><u+0442><u+0441><u+0443><u+0442><u+0441><u+0442><u+0432><u+0438><u+0435>
## 18894 <u+043d><u+0435>
## 18895 <u+043e><u+043f><u+044f><u+0442><u+044c>
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## 18897 <u+043e><u+0442>�covid19
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## 18899 <u+043f><u+0435><u+0440><u+0435><u+0432><u+043e><u+0437><u+044f><u+0449><u+0438><u+0435>
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## 18901 <u+043f><u+0440><u+0430><u+0432>
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## 18905 <u+0441><u+043b><u+044b><u+0448><u+0430><u+043b><u+0438>
## 18906 <u+0441>�covid19
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## 19049 <u+06be><u+06d2>
## 19050 <u+06c1><u+06d2>
## 19051 <u+06d4>
## 19052 <u+203c><u+fe0f><u+0001f4fa>
## 19053 <u+2066><u+2066><u+2066><u+2066><u+2066><u+2066>borisjohnson
## 19054 <u+2066>greatormondst<u+2069>
## 19055 <u+2066>gregclarkcities
## 19056 <u+2066>northmidnhs<u+2069>
## 19057 <u+2066>thetimes<u+2069>
## 19058 <u+231a><u+fe0f>
## 19059 <u+23ed><u+23ed>
## 19060 <u+23f3>
## 19061 <u+25aa><u+fe0f>
## 19062 <u+25aa><u+fe0f>through<u+25aa><u+fe0f>the<u+25aa><u+fe0f>window<u+25aa><u+fe0f>
## 19063 <u+25b6><u+fe0f><u+0001f50a>
## 19064 <u+25ba><u+25ba>accede
## 19065 <u+25ba>excelente
## 19066 <u+25ba>la
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## 19068 <u+2602><u+fe0f>
## 19069 <u+2602><u+fe0f>marypoppins
## 19070 <u+260e><u+fe0f>hrh
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## 19072 <u+2620><u+fe0f><u+0001f637><u+270c><u+0001f3fc>
## 19073 <u+2620><u+fe0f>covid19<u+2620><u+fe0f>theplague
## 19074 <u+2622><u+fe0f>
## 19075 <u+263a>
## 19076 <u+2665>
## 19077 <u+2665><u+fe0f><u+2665><u+fe0f><u+2665><u+fe0f>
## 19078 <u+26a0><u+fe0f>
## 19079 <u+26bd><u+fe0f>
## 19080 <u+26c5><u+fe0f>
## 19081 <u+26f0><u+fe0f>
## 19082 <u+2705>only
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## 19086 <u+270a><u+0001f3fd><u+270a><u+0001f3fe><u+270a><u+0001f3ff>
## 19087 <u+270a><u+0001f3fd>blm
## 19088 <u+270a>blacklivesmatter
## 19089 <u+270a>protestsurvive
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## 19092 <u+270c><u+0001f3fd>
## 19093 <u+270c><u+fe0f>news
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## 19095 <u+2753>
## 19096 <u+2753>|
## 19097 <u+2764><u+fe0f><u+0001f44f>sal4kf
## 19098 <u+2764><u+fe0f><u+0001f499>
## 19099 <u+2764><u+fe0f><u+0001f5a4>
## 19100 <u+2764><u+fe0f><u+2764><u+fe0f><u+2764><u+fe0f>
## 19101 <u+2764><u+fe0f>harry
## 19102 <u+2795>
## 19103 <u+27a1><u+fe0f>park
## 19104 <u+2b07><u+fe0f><u+0001f411>
## 19105 <u+2b50><u+fe0f>
## 19106 <u+2b50><u+fe0f><u+2b50><u+fe0f><u+2b50><u+fe0f><u+2b50><u+fe0f><u+2b50><u+fe0f>
## 19107 <u+2b55>huge
## 19108 <u+2b55>people
## 19109 <u+2b55>structual
## 19110 <u+3053><u+306e><u+30a2><u+30d7><u+30ea><u+3067><u+6bce><u+65e5><u+4f53><u+8abf><u+3092><u+30a2><u+30c3><u+30d7><u+30c7><u+30fc><u+30c8><u+3057><u+3066><u+3066><u+3001><u+3053><u+3053><u+6570><u+65e5><u+5589><u+304c><u+75db><u+304f><u+4ed6><u+66f4><u+65b0><u+3057><u+305f><u+3068><u+3053><u+308d><u+3001>covid19<u+30c6><u+30b9><u+30c8><u+3092><u+30dc><u+30e9><u+30f3><u+30c6><u+30a3><u+30a2><u+3067><u+53d7><u+3051><u+308b><u+4e8b><u+306b><u+3002><u+90f5><u+9001><u+3067>test
## 19111 <u+306b><u+7acb><u+3061><u+5411><u+304b><u+3046><u+30d0><u+30fc><u+30df><u+30f3><u+30ac><u+30e0><u+306e><u+8857><u+3068><u+4f4f><u+6c11><u+304c><u+63cf><u+304b><u+308c><u+3066><u+3044><u+308b><u+3002>
## 19112 <u+30a2><u+30fc><u+30c8>
## 19113 <u+30b0><u+30e9><u+30d5><u+30a3><u+30c6><u+30a3>
## 19114 <u+30d0><u+30fc><u+30df><u+30f3><u+30ac><u+30e0><u+5728><u+4f4f><u+306e><u+30a2><u+30fc><u+30c6><u+30a3><u+30b9><u+30c8><u+304c><u+63cf><u+3044><u+305f><u+30b0><u+30e9><u+30d5><u+30a3><u+30c6><u+30a3><u+304c><u+30ab><u+30c3><u+30b3><u+3088><u+3059><u+304e><u+308b><u+306e><u+3067><u+898b><u+3066><u+3044><u+305f><u+3060><u+304d><u+305f><u+3044><u+3002>
## 19115 <u+5049><u+3044><u+4eba><u+304c><u+4f55><u+304b><u+8a00><u+3046><u+6642><u+306f><u+3001><u+3069><u+3046><u+89e3><u+91c8><u+3055><u+308c><u+308b><u+304b><u+826f><u+304f><u+8003><u+3048><u+305f><u+65b9><u+304c><u+826f><u+3044><u+3068><u+3044><u+3046><u+3001><u+5178><u+578b><u+7684><u+306a><u+4f8b><u+3002><u+672c><u+5f53><u+306b><u+4f55><u+8003><u+3048><u+3066><u+305f><u+306e><u+304b><u+306d><u+3002>
## 19116 <u+8857><u+306e><u+8c61><u+5fb4><u+3067><u+3042><u+308b><u+30d6><u+30eb><u+306b><u+4e57><u+308a><u+30d0><u+30fc><u+30df><u+30f3><u+30ac><u+30e0><u+306e><u+5e02><u+65d7><u+3092><u+63b2><u+3052><u+3001><u+533b><u+7642><u+5f93><u+4e8b><u+8005><u+3068><u+5171><u+306b>
## 19117 <u+fdf2>
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## 19119 04june2020
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## 19166 2<u+fe0f><u+20e3>
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## 19845 bellicose
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## 19847 below<u+0001f447>
## 19848 bemusement
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## 19850 benar2
## 19851 bench
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## 19854 bended
## 19855 bender
## 19856 benefactors
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## 19859 bengal
## 19860 benign
## 19861 benn
## 19862 benny
## 19863 bent
## 19864 bentwaters
## 19865 berdampak
## 19866 berlin
## 19867 besoin
## 19868 bestand
## 19869 bestdesigned
## 19870 best�
## 19871 betrayal
## 19872 better�
## 19873 betty
## 19874 beware
## 19875 bewhat
## 19876 beyond�
## 19877 bhaktpuppet
## 19878 bhatty
## 19879 bhunas
## 19880 bias
## 19881 biased
## 19882 bickington
## 19883 bicycles
## 19884 biggly
## 19885 bikesafe
## 19886 billionaires
## 19887 billions
## 19888 billy
## 19889 binary�
## 19890 bine
## 19891 bio<u+0001f446><u+0001f3fe>
## 19892 bioenergetic
## 19893 biologically
## 19894 biologics
## 19895 biology
## 19896 biopharmas
## 19897 biostatistics
## 19898 birds
## 19899 birnie
## 19900 birthdayour
## 19901 births
## 19902 biscuits
## 19903 bitch
## 19904 bits
## 19905 bits<u+0001f622><u+2764>
## 19906 bitter
## 19907 bittersweet
## 19908 bizarre
## 19909 bizarrely
## 19910 bjs
## 19911 bl
## 19912 blackall
## 19913 blackburn
## 19914 blames
## 19915 blame�
## 19916 blas�
## 19917 blatant
## 19918 bleach
## 19919 bleat
## 19920 bleating
## 19921 bleeder
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## 19923 blessings
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## 19925 blip
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## 19927 blocked
## 19928 blog<u+2066>
## 19929 blogger
## 19930 blogs
## 19931 blokes
## 19932 bloods
## 19933 bloor
## 19934 blowing
## 19935 blown
## 19936 blows
## 19937 blowy
## 19938 bls
## 19939 blunder
## 19940 blurred
## 19941 blush
## 19942 bluster
## 19943 blusters
## 19944 boarded
## 19945 boarders
## 19946 boarding
## 19947 boards
## 19948 boasted
## 19949 boats
## 19950 bobl
## 19951 bocboc
## 19952 bodied
## 19953 boeing
## 19954 bog
## 19955 boggles
## 19956 bogs
## 19957 boiled
## 19958 bolashodun
## 19959 bolder
## 19960 bollocksborisjohnson
## 19961 bolsonaristas
## 19962 bolsonaro
## 19963 bolsonaro�s
## 19964 bolt
## 19965 bolton
## 19966 bombing
## 19967 bonding
## 19968 bonehill
## 19969 bonfire
## 19970 bonnet
## 19971 bonnie
## 19972 bonus
## 19973 booklet
## 19974 boom<u+0001f4a5>
## 19975 boosted
## 19976 boosting
## 19977 boot
## 19978 boots
## 19979 boozer
## 19980 borderline
## 19981 borglike
## 19982 boris<u+0001f64b><u+0001f3fc><u+200d><u+2640><u+fe0f><u+0001f973>covid19
## 19983 borisjohnson
## 19984 boriss
## 19985 born
## 19986 borough�s
## 19987 borris
## 19988 borrow
## 19989 borrowing
## 19990 boston
## 19991 bot
## 19992 botanic
## 19993 botched
## 19994 bother
## 19995 bothers
## 19996 botones
## 19997