Estudio Women’s E-commerce clothing reviews and ratings
Marcos Sanchez Garcia 128465 (UNAV carnet)
30 de noviembre de 2018
R Markdown
Analizaremos el dataset: “Women’s E-Commerce Clothing Reviews and Ratings”.
Este dataset contiene mas de 23,000 reviews online de ropa de mujeres
de varios retailers. Como se menciona en la seccion “Overview” de Kaggle.com
el dataset contiene las siguientes variables
Guía de colores: http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf
URL dataset de Kaggle: https://www.kaggle.com/nicapotato/womens-ecommerce-clothing-reviews
Variables:
Clothing ID
Age (of the reviewer)
Title (of review)
Review
Rating (out of 5-stars)
Recommendation index (i.e. whether customer would recommend this product to others: yes= 1/no = 0 )
Positive Feedback Count (the number of readers who found the review useful)
Division name (e.g. General Petite, Intimates)
Department name (e.g. Jackets, Tops, Bottoms)
Class name (e.g. Blouses, Casual bottoms, Skirts…)
# Comenzamos cargando los datos en R
url="https://raw.githubusercontent.com/msanchez50/Train/master/Womens%20Clothing%20E-Commerce%20Reviews.csv"
clothes=read.csv(url)Ahora los library
library(tidyverse)## -- Attaching packages ------------------------------------------------------------------- tidyverse 1.2.1 --## v ggplot2 3.1.0 v purrr 0.2.5
## v tibble 1.4.2 v dplyr 0.7.8
## v tidyr 0.8.2 v stringr 1.3.1
## v readr 1.2.1 v forcats 0.3.0## -- Conflicts ---------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(tidytext)
library(dplyr)
library(purrr)
library(stringr)
library(igraph)##
## Attaching package: 'igraph'## The following objects are masked from 'package:dplyr':
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## as_data_frame, groups, union## The following objects are masked from 'package:purrr':
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## compose, simplify## The following object is masked from 'package:tidyr':
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## crossing## The following object is masked from 'package:tibble':
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## as_data_frame## The following objects are masked from 'package:stats':
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## decompose, spectrum## The following object is masked from 'package:base':
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## unionlibrary(ggplot2)
library(wordcloud2)
library(ggraph)
library(topicmodels)
library(partykit)## Loading required package: grid## Loading required package: libcoin## Loading required package: mvtnormlibrary(gtrendsR)
library(tm)## Loading required package: NLP##
## Attaching package: 'NLP'## The following object is masked from 'package:ggplot2':
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## annotatelibrary(SnowballC)
library(wordcloud)## Loading required package: RColorBrewerlibrary(e1071)
library(syuzhet)Comenzamos observando nuestro dataset y realizamos algunos cambios:
str(clothes)## 'data.frame': 23486 obs. of 11 variables:
## $ X : int 0 1 2 3 4 5 6 7 8 9 ...
## $ Clothing.ID : int 767 1080 1077 1049 847 1080 858 858 1077 1077 ...
## $ Age : int 33 34 60 50 47 49 39 39 24 34 ...
## $ Title : Factor w/ 13994 levels "","\"beach business\"",..: 1 1 11451 8055 4365 8769 1973 10671 4299 11765 ...
## $ Review.Text : Factor w/ 22635 levels "","- this really is lovely. the overall design from the arms, front, and back makes this poncho unique. it's not t"| __truncated__,..: 247 13179 5545 8025 20324 7987 3330 8850 7378 2671 ...
## $ Rating : int 4 5 3 5 5 2 5 4 5 5 ...
## $ Recommended.IND : int 1 1 0 1 1 0 1 1 1 1 ...
## $ Positive.Feedback.Count: int 0 4 0 0 6 4 1 4 0 0 ...
## $ Division.Name : Factor w/ 4 levels "","General","General Petite",..: 4 2 2 3 2 2 3 3 2 2 ...
## $ Department.Name : Factor w/ 7 levels "","Bottoms","Dresses",..: 4 3 3 2 6 3 6 6 3 3 ...
## $ Class.Name : Factor w/ 21 levels "","Blouses","Casual bottoms",..: 7 5 5 15 2 5 10 10 5 5 ...clothes$X <- NULL
str(clothes)## 'data.frame': 23486 obs. of 10 variables:
## $ Clothing.ID : int 767 1080 1077 1049 847 1080 858 858 1077 1077 ...
## $ Age : int 33 34 60 50 47 49 39 39 24 34 ...
## $ Title : Factor w/ 13994 levels "","\"beach business\"",..: 1 1 11451 8055 4365 8769 1973 10671 4299 11765 ...
## $ Review.Text : Factor w/ 22635 levels "","- this really is lovely. the overall design from the arms, front, and back makes this poncho unique. it's not t"| __truncated__,..: 247 13179 5545 8025 20324 7987 3330 8850 7378 2671 ...
## $ Rating : int 4 5 3 5 5 2 5 4 5 5 ...
## $ Recommended.IND : int 1 1 0 1 1 0 1 1 1 1 ...
## $ Positive.Feedback.Count: int 0 4 0 0 6 4 1 4 0 0 ...
## $ Division.Name : Factor w/ 4 levels "","General","General Petite",..: 4 2 2 3 2 2 3 3 2 2 ...
## $ Department.Name : Factor w/ 7 levels "","Bottoms","Dresses",..: 4 3 3 2 6 3 6 6 3 3 ...
## $ Class.Name : Factor w/ 21 levels "","Blouses","Casual bottoms",..: 7 5 5 15 2 5 10 10 5 5 ...colnames(clothes) <- c('ID', 'Age', 'Title', 'Review', 'Rating', 'Recommend', 'Liked', 'Division', 'Dept', 'Class')
str(clothes)## 'data.frame': 23486 obs. of 10 variables:
## $ ID : int 767 1080 1077 1049 847 1080 858 858 1077 1077 ...
## $ Age : int 33 34 60 50 47 49 39 39 24 34 ...
## $ Title : Factor w/ 13994 levels "","\"beach business\"",..: 1 1 11451 8055 4365 8769 1973 10671 4299 11765 ...
## $ Review : Factor w/ 22635 levels "","- this really is lovely. the overall design from the arms, front, and back makes this poncho unique. it's not t"| __truncated__,..: 247 13179 5545 8025 20324 7987 3330 8850 7378 2671 ...
## $ Rating : int 4 5 3 5 5 2 5 4 5 5 ...
## $ Recommend: int 1 1 0 1 1 0 1 1 1 1 ...
## $ Liked : int 0 4 0 0 6 4 1 4 0 0 ...
## $ Division : Factor w/ 4 levels "","General","General Petite",..: 4 2 2 3 2 2 3 3 2 2 ...
## $ Dept : Factor w/ 7 levels "","Bottoms","Dresses",..: 4 3 3 2 6 3 6 6 3 3 ...
## $ Class : Factor w/ 21 levels "","Blouses","Casual bottoms",..: 7 5 5 15 2 5 10 10 5 5 ...summary(clothes)## ID Age Title
## Min. : 0.0 Min. :18.0 : 3810
## 1st Qu.: 861.0 1st Qu.:34.0 Love it! : 136
## Median : 936.0 Median :41.0 Beautiful : 95
## Mean : 918.1 Mean :43.2 Love : 88
## 3rd Qu.:1078.0 3rd Qu.:52.0 Love! : 84
## Max. :1205.0 Max. :99.0 Beautiful!: 72
## (Other) :19201
## Review
## : 845
## Perfect fit and i've gotten so many compliments. i buy all my suits from here now! : 3
## I bought this shirt at the store and after going home and trying it on, i promptly went online and ordered two more! i've gotten multiple compliments anytime i wear any of them. great for looking put together with no fuss. \npeople that have commented there's were destroyed in the wash didn't read the care label which says dry clean. : 2
## I purchased this and another eva franco dress during retailer's recent 20% off sale. i was looking for dresses that were work appropriate, but that would also transition well to happy hour or date night. they both seemed to be just what i was looking for. i ordered a 4 regular and a 6 regular, as i am usually in between sizes. the 4 was definitely too small. the 6 fit, technically, but was very ill fitting. not only is the dress itself short, but it is very short-waisted. i am only 5'3", but it fe: 2
## Lightweight, soft cotton top and shorts. i think it's meant to be a beach cover-up but i'm wearing it as a thin, light-weight summer outfit on these hot hot days. the top has a loose elastic around the bottom which i didn't realize when i ordered it, but i like it and it matches the look in the photos. and the shorts are very low-cut - don't expect them up around your waist. again, i like that. some might want to wear a cami underneath because it's a thin cotton but i'm fine as-is. i bought it i : 2
## Love, love these jeans. being short they come right to my ankle. super soft and don?t require any hemming. i ordered my typical jean size of 26 and they fit like a glove. would love to have these in black and grey. : 2
## (Other) :22630
## Rating Recommend Liked Division
## Min. :1.000 Min. :0.0000 Min. : 0.000 : 14
## 1st Qu.:4.000 1st Qu.:1.0000 1st Qu.: 0.000 General :13850
## Median :5.000 Median :1.0000 Median : 1.000 General Petite: 8120
## Mean :4.196 Mean :0.8224 Mean : 2.536 Initmates : 1502
## 3rd Qu.:5.000 3rd Qu.:1.0000 3rd Qu.: 3.000
## Max. :5.000 Max. :1.0000 Max. :122.000
##
## Dept Class
## : 14 Dresses :6319
## Bottoms : 3799 Knits :4843
## Dresses : 6319 Blouses :3097
## Intimate: 1735 Sweaters:1428
## Jackets : 1032 Pants :1388
## Tops :10468 Jeans :1147
## Trend : 119 (Other) :5264unlist(map(map(clothes, is.na), sum))## ID Age Title Review Rating Recommend Liked
## 0 0 0 0 0 0 0
## Division Dept Class
## 0 0 0Comenzamos aplicando todo lo visto de Text Mining:
#Realizaremos un PLOT que nos da el porcentaje de Reviews/Rating por cada Departamento.
ggplot(data.frame(prop.table(table(clothes$Dept))), aes(x=Var1, y = Freq*100)) + geom_bar(stat = 'identity') + xlab('Department Name') + ylab('Percentage of Reviews/Ratings (%)') + geom_text(aes(label=round(Freq*100,2)), vjust=-0.25) + ggtitle('Porcentaje de reviews por Departamento')
Como podemos observar, Tops tiene el porcentaje mas alto de Reviews y Ratings
en nuestro dataset, seguido de dresses. Productos como
el departamento de Jackets y Trend han recibido las peores reviews.
Ahora observaremos la distribución del rating dentro de los departamentos
sapply: Observamos si existen NA en nuestro dataset
sapply(clothes, function(x) sum(is.na(x)))## ID Age Title Review Rating Recommend Liked
## 0 0 0 0 0 0 0
## Division Dept Class
## 0 0 0str(clothes)## 'data.frame': 23486 obs. of 10 variables:
## $ ID : int 767 1080 1077 1049 847 1080 858 858 1077 1077 ...
## $ Age : int 33 34 60 50 47 49 39 39 24 34 ...
## $ Title : Factor w/ 13994 levels "","\"beach business\"",..: 1 1 11451 8055 4365 8769 1973 10671 4299 11765 ...
## $ Review : Factor w/ 22635 levels "","- this really is lovely. the overall design from the arms, front, and back makes this poncho unique. it's not t"| __truncated__,..: 247 13179 5545 8025 20324 7987 3330 8850 7378 2671 ...
## $ Rating : int 4 5 3 5 5 2 5 4 5 5 ...
## $ Recommend: int 1 1 0 1 1 0 1 1 1 1 ...
## $ Liked : int 0 4 0 0 6 4 1 4 0 0 ...
## $ Division : Factor w/ 4 levels "","General","General Petite",..: 4 2 2 3 2 2 3 3 2 2 ...
## $ Dept : Factor w/ 7 levels "","Bottoms","Dresses",..: 4 3 3 2 6 3 6 6 3 3 ...
## $ Class : Factor w/ 21 levels "","Blouses","Casual bottoms",..: 7 5 5 15 2 5 10 10 5 5 ...Analizaremos el % de Rating decada departamento (Bottoms, Dresses, Intimate, Jackets, Tops y Trend)
str(clothes)## 'data.frame': 23486 obs. of 10 variables:
## $ ID : int 767 1080 1077 1049 847 1080 858 858 1077 1077 ...
## $ Age : int 33 34 60 50 47 49 39 39 24 34 ...
## $ Title : Factor w/ 13994 levels "","\"beach business\"",..: 1 1 11451 8055 4365 8769 1973 10671 4299 11765 ...
## $ Review : Factor w/ 22635 levels "","- this really is lovely. the overall design from the arms, front, and back makes this poncho unique. it's not t"| __truncated__,..: 247 13179 5545 8025 20324 7987 3330 8850 7378 2671 ...
## $ Rating : int 4 5 3 5 5 2 5 4 5 5 ...
## $ Recommend: int 1 1 0 1 1 0 1 1 1 1 ...
## $ Liked : int 0 4 0 0 6 4 1 4 0 0 ...
## $ Division : Factor w/ 4 levels "","General","General Petite",..: 4 2 2 3 2 2 3 3 2 2 ...
## $ Dept : Factor w/ 7 levels "","Bottoms","Dresses",..: 4 3 3 2 6 3 6 6 3 3 ...
## $ Class : Factor w/ 21 levels "","Blouses","Casual bottoms",..: 7 5 5 15 2 5 10 10 5 5 ...summary(clothes$Dept)## Bottoms Dresses Intimate Jackets Tops Trend
## 14 3799 6319 1735 1032 10468 119Observamos que Trends es una mezcla de productos, y a su vez 119/23486 = 0.51%, este peso es mínimo
por lo que he decidido descartarlo.
departamento <- clothes %>% filter(!is.na(Dept), Dept != 'Trend') %>% mutate(Dept = factor(Dept)) %>% group_by(Dept) %>% count(Rating) %>% mutate(perc = n/sum(n))
departamento %>% ggplot(aes(x=Rating, y = perc*100, fill = Dept)) + geom_bar(stat = 'identity', show.legend = FALSE) + facet_wrap(~Dept) + ylab('Porcentaje de Reviews (%)') + geom_text(aes(label=round(perc*100,2)))
En cada departamento predomina un rating de 5 estrellas con un mayor porcentaje. El que mas rating 5
estrellas tiene es el departamento de Jackets (61,14%), seguido de Bottoms (59,83%) y Intimate (59,54%).
Procederemos a ver como se relacionan los departamentos y las edades mediante graficos:
NOTA: volvemos a sacar Trend ya que como hemos explicado anteriormente no lo utilizaremos.
Los rangos de edades: 18-25, 26-39, 40-55, 55-65. Que queremos estudiar
anos <- clothes %>% filter(!is.na(Age), !is.na(Dept), Dept != 'Trend') %>% select(ID, Age, Dept) %>% mutate(Age_group = ifelse(Age < 26, '18-25', ifelse(Age < 40, '26-39', ifelse(Age < 56, '40-55', ifelse(Age < 66, '56-65', ifelse(Age < 101, '66-99'))))))
anos <- anos %>% mutate(Age_group = factor(Age_group), Dept = factor(Dept, levels = rev(c('Tops', 'Dresses', 'Bottoms', 'Intimate', 'Jackets'))))Borramos los valores NA que se generen
n <- anos[!is.na(anos$Dept),]Realizamos un gráfica con nuestros rangos de edad hasta <66
anos %>% filter(Age < 66) %>% group_by(Age_group) %>% count(Dept) %>% ggplot(aes(Dept, n, fill = Age_group)) + geom_bar(stat='identity', show.legend = FALSE) + facet_wrap(~Age_group, scales = 'free') + xlab('Department') + ylab('Number of Reviews') + geom_text(aes(label = n), hjust = .73) + coord_flip()
# Podemos observar como la gente de 26-39 deja un mayor numero de reviews, seguido de # el grupo 40-55. No he estudiado el grupo de 66 - 99, ya que el nº de reviews no las considero # significativas para las conclusiones.
TEXT MINING: GOOGLE TRENDS
str(clothes)## 'data.frame': 23486 obs. of 10 variables:
## $ ID : int 767 1080 1077 1049 847 1080 858 858 1077 1077 ...
## $ Age : int 33 34 60 50 47 49 39 39 24 34 ...
## $ Title : Factor w/ 13994 levels "","\"beach business\"",..: 1 1 11451 8055 4365 8769 1973 10671 4299 11765 ...
## $ Review : Factor w/ 22635 levels "","- this really is lovely. the overall design from the arms, front, and back makes this poncho unique. it's not t"| __truncated__,..: 247 13179 5545 8025 20324 7987 3330 8850 7378 2671 ...
## $ Rating : int 4 5 3 5 5 2 5 4 5 5 ...
## $ Recommend: int 1 1 0 1 1 0 1 1 1 1 ...
## $ Liked : int 0 4 0 0 6 4 1 4 0 0 ...
## $ Division : Factor w/ 4 levels "","General","General Petite",..: 4 2 2 3 2 2 3 3 2 2 ...
## $ Dept : Factor w/ 7 levels "","Bottoms","Dresses",..: 4 3 3 2 6 3 6 6 3 3 ...
## $ Class : Factor w/ 21 levels "","Blouses","Casual bottoms",..: 7 5 5 15 2 5 10 10 5 5 ...summary(clothes$Class)## Blouses Casual bottoms Chemises Dresses
## 14 3097 2 1 6319
## Fine gauge Intimates Jackets Jeans Knits
## 1100 154 704 1147 4843
## Layering Legwear Lounge Outerwear Pants
## 146 165 691 328 1388
## Shorts Skirts Sleep Sweaters Swim
## 317 945 228 1428 350
## Trend
## 119library(gtrendsR)Hemos seleccionado 4 palabras que he considerado que tenian mas registros en el dataset:
“dresses”, “knits”, “jeans” y “pants”.
Cargando el library de gtrendsR podemos realizar un estudio de la busqueda en Google de estas palabras
clothes_trend <- gtrends(c("dresses", "knits", "jeans", "pants"))
plot(clothes_trend)
# Observamos como dresses y jeans tienen un alto grado de interes, seguido de cerca. Mientras que Knits
# tiene un interes casi nulo.
str(clothes_trend)## List of 7
## $ interest_over_time :'data.frame': 1044 obs. of 6 variables:
## ..$ date : POSIXct[1:1044], format: "2013-12-01" ...
## ..$ hits : chr [1:1044] "69" "67" "58" "59" ...
## ..$ keyword : chr [1:1044] "dresses" "dresses" "dresses" "dresses" ...
## ..$ geo : chr [1:1044] "world" "world" "world" "world" ...
## ..$ gprop : chr [1:1044] "web" "web" "web" "web" ...
## ..$ category: int [1:1044] 0 0 0 0 0 0 0 0 0 0 ...
## $ interest_by_country:'data.frame': 1000 obs. of 5 variables:
## ..$ location: chr [1:1000] "Guyana" "Antigua & Barbuda" "Anguilla" "Botswana" ...
## ..$ hits : chr [1:1000] "" "" "" "" ...
## ..$ keyword : chr [1:1000] "dresses" "dresses" "dresses" "dresses" ...
## ..$ geo : chr [1:1000] "world" "world" "world" "world" ...
## ..$ gprop : chr [1:1000] "web" "web" "web" "web" ...
## $ interest_by_region : NULL
## $ interest_by_dma :'data.frame': 1224 obs. of 5 variables:
## ..$ location: chr [1:1224] "Greenwood-Greenville MS" "Albany GA" "Monroe LA-El Dorado AR" "Lafayette LA" ...
## ..$ hits : chr [1:1224] "100" "79" "78" "77" ...
## ..$ keyword : chr [1:1224] "dresses" "dresses" "dresses" "dresses" ...
## ..$ geo : chr [1:1224] "world" "world" "world" "world" ...
## ..$ gprop : chr [1:1224] "web" "web" "web" "web" ...
## $ interest_by_city :'data.frame': 281 obs. of 5 variables:
## ..$ location: chr [1:281] "Faisalabad" "Rawalpindi" "Belfast" "Dublin" ...
## ..$ hits : int [1:281] NA NA NA 100 100 NA NA 88 85 NA ...
## ..$ keyword : chr [1:281] "dresses" "dresses" "dresses" "dresses" ...
## ..$ geo : chr [1:281] "world" "world" "world" "world" ...
## ..$ gprop : chr [1:281] "web" "web" "web" "web" ...
## $ related_topics : NULL
## $ related_queries :'data.frame': 200 obs. of 5 variables:
## ..$ subject : chr [1:200] "100" "100" "58" "34" ...
## ..$ related_queries: chr [1:200] "top" "top" "top" "top" ...
## ..$ value : chr [1:200] "wedding dresses" "dress" "prom dresses" "bridesmaid dresses" ...
## ..$ keyword : chr [1:200] "dresses" "dresses" "dresses" "dresses" ...
## ..$ category : int [1:200] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "reshapeLong")=List of 4
## .. ..$ varying:List of 1
## .. .. ..$ value: chr "top"
## .. .. ..- attr(*, "v.names")= chr "value"
## .. .. ..- attr(*, "times")= chr "top"
## .. ..$ v.names: chr "value"
## .. ..$ idvar : chr "id"
## .. ..$ timevar: chr "related_queries"
## - attr(*, "class")= chr [1:2] "gtrends" "list"# Realizaremos algunas graficas segun varios intereses:
sp1 <- ggplot(clothes_trend$interest_by_country)
sp1 + geom_boxplot(aes(keyword, as.numeric(hits)))## Warning in FUN(X[[i]], ...): NAs introducidos por coerción
## Warning in FUN(X[[i]], ...): NAs introducidos por coerción## Warning: Removed 811 rows containing non-finite values (stat_boxplot).
sp2 <- ggplot(clothes_trend$interest_by_dma)
sp2 + geom_boxplot(aes(keyword, hits))
sp3 <- ggplot(subset(clothes_trend$interest_by_city, !is.na(clothes_trend$interest_by_city$hits)))
sp3 + geom_boxplot(aes(location, hits)) + theme(axis.text.x = element_text(angle = 90, hjust = 1))
SENTIMENT ANALYSIS
Analisis de sentimiento
Cambiamos el nombre a nuestro dataframe
dfn2 <- clothes
str(dfn2)## 'data.frame': 23486 obs. of 10 variables:
## $ ID : int 767 1080 1077 1049 847 1080 858 858 1077 1077 ...
## $ Age : int 33 34 60 50 47 49 39 39 24 34 ...
## $ Title : Factor w/ 13994 levels "","\"beach business\"",..: 1 1 11451 8055 4365 8769 1973 10671 4299 11765 ...
## $ Review : Factor w/ 22635 levels "","- this really is lovely. the overall design from the arms, front, and back makes this poncho unique. it's not t"| __truncated__,..: 247 13179 5545 8025 20324 7987 3330 8850 7378 2671 ...
## $ Rating : int 4 5 3 5 5 2 5 4 5 5 ...
## $ Recommend: int 1 1 0 1 1 0 1 1 1 1 ...
## $ Liked : int 0 4 0 0 6 4 1 4 0 0 ...
## $ Division : Factor w/ 4 levels "","General","General Petite",..: 4 2 2 3 2 2 3 3 2 2 ...
## $ Dept : Factor w/ 7 levels "","Bottoms","Dresses",..: 4 3 3 2 6 3 6 6 3 3 ...
## $ Class : Factor w/ 21 levels "","Blouses","Casual bottoms",..: 7 5 5 15 2 5 10 10 5 5 ...Analizaremos las Reviews:
Pasamos nuestras Reviews de factor a character para poder analizar
dfn2$Review <- as.character(dfn2$Review)
str(dfn2)## 'data.frame': 23486 obs. of 10 variables:
## $ ID : int 767 1080 1077 1049 847 1080 858 858 1077 1077 ...
## $ Age : int 33 34 60 50 47 49 39 39 24 34 ...
## $ Title : Factor w/ 13994 levels "","\"beach business\"",..: 1 1 11451 8055 4365 8769 1973 10671 4299 11765 ...
## $ Review : chr "Absolutely wonderful - silky and sexy and comfortable" "Love this dress! it's sooo pretty. i happened to find it in a store, and i'm glad i did bc i never would have"| __truncated__ "I had such high hopes for this dress and really wanted it to work for me. i initially ordered the petite small "| __truncated__ "I love, love, love this jumpsuit. it's fun, flirty, and fabulous! every time i wear it, i get nothing but great compliments!" ...
## $ Rating : int 4 5 3 5 5 2 5 4 5 5 ...
## $ Recommend: int 1 1 0 1 1 0 1 1 1 1 ...
## $ Liked : int 0 4 0 0 6 4 1 4 0 0 ...
## $ Division : Factor w/ 4 levels "","General","General Petite",..: 4 2 2 3 2 2 3 3 2 2 ...
## $ Dept : Factor w/ 7 levels "","Bottoms","Dresses",..: 4 3 3 2 6 3 6 6 3 3 ...
## $ Class : Factor w/ 21 levels "","Blouses","Casual bottoms",..: 7 5 5 15 2 5 10 10 5 5 ...dfn2 %>%
unnest_tokens(word, `Review`) %>%
anti_join(stop_words) %>%
count(word, sort = TRUE) %>%
head(10) %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(word, n)) +
geom_col() +
labs(x = NULL, title = "Most used words in Reviews") +
coord_flip()## Joining, by = "word"
dfn2 %>%
unnest_tokens(word, `Review`) %>%
anti_join(stop_words) %>%
count(word, Rating, sort = TRUE) %>%
select(n, word, Rating) %>%
group_by(Rating) %>%
top_n(n = 5, wt = n) %>%
ggplot(aes(word, n)) +
geom_col() +
xlab(NULL) +
coord_flip() +
facet_grid(. ~ Rating)## Joining, by = "word"
dfn3 <- dfn2$Review
str(dfn3)## chr [1:23486] "Absolutely wonderful - silky and sexy and comfortable" ...dfn4 <- dfn3[1:20]
dfn5 <- dfn3[21:40]
dfn6 <- dfn3[1000:1020]
dfn4_v <- get_tokens(dfn4, pattern = "\\W")
dfn4_s <- get_nrc_sentiment(dfn4_v)
par(mfrow=c(1,3))
dfn5_v <- get_tokens(dfn5, pattern = "\\W")
dfn5_s <- get_nrc_sentiment(dfn5_v)
par(mfrow=c(1,3))
dfn6_v <- get_tokens(dfn6, pattern = "\\W")
dfn6_s <- get_nrc_sentiment(dfn6_v)
par(mfrow=c(1,3))
barplot(sort(colSums(prop.table(dfn4_s[, 1:8]))),
horiz = TRUE, cex.names = 0.7, las = 1,
main = "Emotions comments about rating product", xlab="Percentage")
barplot(sort(colSums(prop.table(dfn5_s[, 1:8]))),
horiz = TRUE, cex.names = 0.7, las = 1,
main = "Emotions comments about rating product", xlab="Percentage")
barplot( sort(colSums(prop.table(dfn6_s[, 1:8]))),
horiz = TRUE, cex.names = 0.7, las = 1,
main = "Emotions comments about rating product", xlab="Percentage" )
Con ayuda de algunos usuarios y kernels de Kaggle y
este link:https://www.tidytextmining.com/tidytext.html#word-frequencies he intentado realizar un
wordcloud, ademas de otros aspectos del Text Mining
En primer lugar, buscamos reviews sin titulo y con titulo, ya que existen dentro del dataset:
clothescloud <- clothes %>% filter(!is.na(Review))
sin_titulo <- clothescloud %>% filter(is.na(Title)) %>% select(-Title)
con_titulo <- clothescloud %>% filter(!is.na(Title)) %>% unite(Review, c(Title, Review), sep = ' ')
clothes1 <- bind_rows(sin_titulo, con_titulo)## Warning in bind_rows_(x, .id): binding factor and character vector,
## coercing into character vector## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vectorstr(clothes1)## 'data.frame': 23486 obs. of 9 variables:
## $ ID : int 767 1080 1077 1049 847 1080 858 858 1077 1077 ...
## $ Age : int 33 34 60 50 47 49 39 39 24 34 ...
## $ Review : chr " Absolutely wonderful - silky and sexy and comfortable" " Love this dress! it's sooo pretty. i happened to find it in a store, and i'm glad i did bc i never would hav"| __truncated__ "Some major design flaws I had such high hopes for this dress and really wanted it to work for me. i initially o"| __truncated__ "My favorite buy! I love, love, love this jumpsuit. it's fun, flirty, and fabulous! every time i wear it, i get "| __truncated__ ...
## $ Rating : int 4 5 3 5 5 2 5 4 5 5 ...
## $ Recommend: int 1 1 0 1 1 0 1 1 1 1 ...
## $ Liked : int 0 4 0 0 6 4 1 4 0 0 ...
## $ Division : Factor w/ 4 levels "","General","General Petite",..: 4 2 2 3 2 2 3 3 2 2 ...
## $ Dept : Factor w/ 7 levels "","Bottoms","Dresses",..: 4 3 3 2 6 3 6 6 3 3 ...
## $ Class : Factor w/ 21 levels "","Blouses","Casual bottoms",..: 7 5 5 15 2 5 10 10 5 5 ...bigramming <- function(data){
cbigram <- data %>% unnest_tokens(bigram, Review, token = 'ngrams', n = 2)
cbigram_sep <- cbigram %>% separate(bigram, c('first', 'second'), sep = ' ')
cbigram2 <- cbigram_sep %>% filter(!first %in% stop_words$word, !second %in% stop_words$word, !str_detect(first, '\\d'), !str_detect(second, '\\d')) %>% unite(bigram, c(first, second), sep = ' ')
return(cbigram2)
}
top_bigrams <- bigramming(clothes1) %>% mutate(Rating = factor(Rating, levels <- c(5:1))) %>% mutate(bigram = factor(bigram, levels = rev(unique(bigram)))) %>% group_by(Rating) %>% count(bigram, sort=TRUE) %>% top_n(10, n) %>% ungroup()
top_bigrams %>% ggplot(aes(bigram, n, fill = Rating)) + geom_col(show.legend = FALSE) + facet_wrap(~Rating, ncol = 3, scales = 'free') + labs(x=NULL, y = 'frequency') + ggtitle('Most Common Bigrams (By Ratings)') + coord_flip()
Ahora filtramos nuestro dataset, según el rating:
cincorating <- clothes1 %>% filter(Rating == 5)
unorating <- clothes1 %>% filter(Rating == 1)
cinco <- bigramming(cincorating) %>% count(bigram, sort = TRUE)
uno <- bigramming(unorating) %>% count(bigram, sort = TRUE)
grafico_cinco <- cinco %>% separate(bigram, c('first','second'), sep = ' ') %>% filter(n > 70) %>% graph_from_data_frame()
grafico_uno <- uno %>% separate(bigram, c('first','second'), sep = ' ') %>% filter(n > 5) %>% graph_from_data_frame()
set.seed(11111)
ggraph(grafico_uno, layout = "fr") +
geom_edge_link() +
geom_node_point(color = 'aquamarine1', size = 4) +
geom_node_text(aes(label = name), vjust = 1.1, hjust = 1.1) + scale_x_continuous(limits = c(2, 18)) + theme_void()## Warning: Removed 12 rows containing non-finite values (stat_edge_link).## Warning: Removed 22 rows containing missing values (geom_point).## Warning: Removed 22 rows containing missing values (geom_text).
set.seed(55555)
ggraph(grafico_cinco, layout = "fr") +
geom_edge_link() +
geom_node_point(color = 'aquamarine1', size = 4) +
geom_node_text(aes(label = name), vjust = 1.1, hjust = 1.1) + scale_x_continuous(limits = c(2, 18)) + theme_void()
Wordcloud test:
Wordcloud del rating 1 (considero que es el negativo)
wordcloud2(uno %>% filter(n>5) %>% mutate(n = sqrt(n)), size = .4)Wordcloud del rating 5 (considero que es el positivo)
wordcloud2(cinco %>% filter(n>10) %>% mutate(n = sqrt(n)), size = .4)TREND REVIEWS (LDA):
str(clothes)## 'data.frame': 23486 obs. of 10 variables:
## $ ID : int 767 1080 1077 1049 847 1080 858 858 1077 1077 ...
## $ Age : int 33 34 60 50 47 49 39 39 24 34 ...
## $ Title : Factor w/ 13994 levels "","\"beach business\"",..: 1 1 11451 8055 4365 8769 1973 10671 4299 11765 ...
## $ Review : Factor w/ 22635 levels "","- this really is lovely. the overall design from the arms, front, and back makes this poncho unique. it's not t"| __truncated__,..: 247 13179 5545 8025 20324 7987 3330 8850 7378 2671 ...
## $ Rating : int 4 5 3 5 5 2 5 4 5 5 ...
## $ Recommend: int 1 1 0 1 1 0 1 1 1 1 ...
## $ Liked : int 0 4 0 0 6 4 1 4 0 0 ...
## $ Division : Factor w/ 4 levels "","General","General Petite",..: 4 2 2 3 2 2 3 3 2 2 ...
## $ Dept : Factor w/ 7 levels "","Bottoms","Dresses",..: 4 3 3 2 6 3 6 6 3 3 ...
## $ Class : Factor w/ 21 levels "","Blouses","Casual bottoms",..: 7 5 5 15 2 5 10 10 5 5 ...summary(clothes$Dept)## Bottoms Dresses Intimate Jackets Tops Trend
## 14 3799 6319 1735 1032 10468 119trend_count <- clothes1 %>% filter(Dept == 'Trend') %>% unnest_tokens(word, Review) %>% anti_join(stop_words, by = 'word') %>% filter(!str_detect(word, '\\d')) %>% count(ID, word, sort = TRUE) %>% ungroup()
trend_dtm <- trend_count %>% cast_dtm(ID, word, n)
trendy <- tidy(LDA(trend_dtm, k = 5, method = 'GIBBS', control = list(seed = 4444, alpha = 1)), matrix = 'beta')
top_trendy <- trendy %>% group_by(topic) %>% top_n(5, beta) %>% ungroup() %>% arrange(topic, desc(beta))
top_trendy %>% mutate(term = reorder(term, beta)) %>% ggplot(aes(term, beta, fill = factor(topic))) + geom_col(show.legend = FALSE) + facet_wrap(~ topic, scales = "free") + ggtitle('LDA Analysis (k = 5)') + coord_flip()
WORDCLOUD TEST del script Text Mining ADI
# Read the data
url="https://raw.githubusercontent.com/msanchez50/Train/master/Womens%20Clothing%20E-Commerce%20Reviews.csv"
dfn=read.csv(url, header=F, stringsAsFactors = F)# Take a look
str(dfn)## 'data.frame': 23487 obs. of 11 variables:
## $ V1 : int NA 0 1 2 3 4 5 6 7 8 ...
## $ V2 : chr "Clothing ID" "767" "1080" "1077" ...
## $ V3 : chr "Age" "33" "34" "60" ...
## $ V4 : chr "Title" "" "" "Some major design flaws" ...
## $ V5 : chr "Review Text" "Absolutely wonderful - silky and sexy and comfortable" "Love this dress! it's sooo pretty. i happened to find it in a store, and i'm glad i did bc i never would have"| __truncated__ "I had such high hopes for this dress and really wanted it to work for me. i initially ordered the petite small "| __truncated__ ...
## $ V6 : chr "Rating" "4" "5" "3" ...
## $ V7 : chr "Recommended IND" "1" "1" "0" ...
## $ V8 : chr "Positive Feedback Count" "0" "4" "0" ...
## $ V9 : chr "Division Name" "Initmates" "General" "General" ...
## $ V10: chr "Department Name" "Intimate" "Dresses" "Dresses" ...
## $ V11: chr "Class Name" "Intimates" "Dresses" "Dresses" ...dfn <- dfn %>% filter(!is.na(V5))
notitle1 <- dfn %>% filter(is.na(V4)) %>% select(-V4)
wtitle1 <- dfn %>% filter(!is.na(V4)) %>% unite(V5, c(V4, V5), sep = ' ')
main1 <- bind_rows(notitle1, wtitle1)
dfn1 <- main1
str(dfn1)## 'data.frame': 23487 obs. of 10 variables:
## $ V1 : int NA 0 1 2 3 4 5 6 7 8 ...
## $ V2 : chr "Clothing ID" "767" "1080" "1077" ...
## $ V3 : chr "Age" "33" "34" "60" ...
## $ V5 : chr "Title Review Text" " Absolutely wonderful - silky and sexy and comfortable" " Love this dress! it's sooo pretty. i happened to find it in a store, and i'm glad i did bc i never would hav"| __truncated__ "Some major design flaws I had such high hopes for this dress and really wanted it to work for me. i initially o"| __truncated__ ...
## $ V6 : chr "Rating" "4" "5" "3" ...
## $ V7 : chr "Recommended IND" "1" "1" "0" ...
## $ V8 : chr "Positive Feedback Count" "0" "4" "0" ...
## $ V9 : chr "Division Name" "Initmates" "General" "General" ...
## $ V10: chr "Department Name" "Intimate" "Dresses" "Dresses" ...
## $ V11: chr "Class Name" "Intimates" "Dresses" "Dresses" ...dfn1$V1 <- NULL
dfn1$V2 <- NULL
dfn1$V3 <- NULL
dfn1$V4 <- NULL
dfn1$V7 <- NULL
dfn1$V8 <- NULL
dfn1$V9 <- NULL
dfn1$V10 <- NULL
dfn1$V11 <- NULL
str(dfn1)## 'data.frame': 23487 obs. of 2 variables:
## $ V5: chr "Title Review Text" " Absolutely wonderful - silky and sexy and comfortable" " Love this dress! it's sooo pretty. i happened to find it in a store, and i'm glad i did bc i never would hav"| __truncated__ "Some major design flaws I had such high hopes for this dress and really wanted it to work for me. i initially o"| __truncated__ ...
## $ V6: chr "Rating" "4" "5" "3" ...sapply(dfn1, function(x) sum(is.na(x)))## V5 V6
## 0 0fivestar1 <- dfn1 %>% filter(V6 == 5)
onestar1 <- dfn1 %>% filter(V6 == 1)
dfn1 <- bind_rows(onestar1, fivestar1)
str(dfn1)## 'data.frame': 13973 obs. of 2 variables:
## $ V5: chr "Itchy tags 3 tags sewn in, 2 small (about 1'' long) and 1 huge (about 2'' x 3''). very itchy so i cut them out."| __truncated__ " I was really hoping to like this, but it did not look the way it does on the model, at least not on me. the sh"| __truncated__ " I usually wear a medium and bought a small. it fit ok, but had no shape and was not flattering. i love baby do"| __truncated__ "Huge I was very excited to order this top in red xs. so cute, but it was huge, shapeless and support thin! it h"| __truncated__ ...
## $ V6: chr "1" "1" "1" "1" ...# Change the name of variables and the levels of target
names(dfn1) <- c("text", "mood")
dfn1$mood <- as.factor(dfn1$mood)
str(dfn1)## 'data.frame': 13973 obs. of 2 variables:
## $ text: chr "Itchy tags 3 tags sewn in, 2 small (about 1'' long) and 1 huge (about 2'' x 3''). very itchy so i cut them out."| __truncated__ " I was really hoping to like this, but it did not look the way it does on the model, at least not on me. the sh"| __truncated__ " I usually wear a medium and bought a small. it fit ok, but had no shape and was not flattering. i love baby do"| __truncated__ "Huge I was very excited to order this top in red xs. so cute, but it was huge, shapeless and support thin! it h"| __truncated__ ...
## $ mood: Factor w/ 2 levels "1","5": 1 1 1 1 1 1 1 1 1 1 ...levels(dfn1$mood) <- c("negative",p="positive")
dfn1$mood## [1] negative negative negative negative negative negative negative
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## [13973] positive
## Levels: negative positivestr(dfn1)## 'data.frame': 13973 obs. of 2 variables:
## $ text: chr "Itchy tags 3 tags sewn in, 2 small (about 1'' long) and 1 huge (about 2'' x 3''). very itchy so i cut them out."| __truncated__ " I was really hoping to like this, but it did not look the way it does on the model, at least not on me. the sh"| __truncated__ " I usually wear a medium and bought a small. it fit ok, but had no shape and was not flattering. i love baby do"| __truncated__ "Huge I was very excited to order this top in red xs. so cute, but it was huge, shapeless and support thin! it h"| __truncated__ ...
## $ mood: Factor w/ 2 levels "negative","positive": 1 1 1 1 1 1 1 1 1 1 ...# Take a look again to see what happened
str(dfn1)## 'data.frame': 13973 obs. of 2 variables:
## $ text: chr "Itchy tags 3 tags sewn in, 2 small (about 1'' long) and 1 huge (about 2'' x 3''). very itchy so i cut them out."| __truncated__ " I was really hoping to like this, but it did not look the way it does on the model, at least not on me. the sh"| __truncated__ " I usually wear a medium and bought a small. it fit ok, but had no shape and was not flattering. i love baby do"| __truncated__ "Huge I was very excited to order this top in red xs. so cute, but it was huge, shapeless and support thin! it h"| __truncated__ ...
## $ mood: Factor w/ 2 levels "negative","positive": 1 1 1 1 1 1 1 1 1 1 ...table(dfn1$mood)##
## negative positive
## 842 13131head(dfn1)## text
## 1 Itchy tags 3 tags sewn in, 2 small (about 1'' long) and 1 huge (about 2'' x 3''). very itchy so i cut them out. then the thread left behind was plasticy and even more itchy! how can you make an intimates item with such itchy tags? not comfortable at all! also - i love bralettes and wear them all the time including to work. i am a b cup. however, this one is so thin and flimsy that it gives no support even to a b cup - so for me this would only be a lounging bralette - if it wasn't so itchy!
## 2 I was really hoping to like this, but it did not look the way it does on the model, at least not on me. the sharkbite hem is much more pronounced and looser. the one in the photo looks like it was pinned back. i am 5'8" and usually wear a medium or large. i got a medium and there was a lot more material on the bottom half than the photo shows. it made me look bigger and was not flattering. material has a nice weave but it's thin and delicate. i bought the (holly) deep olive and the blue. colors
## 3 I usually wear a medium and bought a small. it fit ok, but had no shape and was not flattering. i love baby doll dresses and tops, but this was a tent. my daughter saw me try it on and said "that's a piece of tablecloth." it's going back.
## 4 Huge I was very excited to order this top in red xs. so cute, but it was huge, shapeless and support thin! it had to go back. i should've looked at other reviews.
## 5 Poor quality This is so thin and poor quality. especially for the price. it felt like a thin pajama top. the buttons are terrible little shell buttons. this could not have been returned faster.
## 6 Scratchy, uncomfortable top The title says it all....this fabric of this top is both the best and worst part of the design. the colors are vibrant and the combination of materials (shoulder is a knit, sweater-like navy fabric) is interesting. however, that is where the positive comments end on this one. the top is so scratchy,, stiff, and, frankly, uncomfortable. i cannot imagine wanting to wear it. it could benefit from a lining, and that might have solved the problem of scratchy, itchy fabric.\n\nthe stiff fabric of the bo
## mood
## 1 negative
## 2 negative
## 3 negative
## 4 negative
## 5 negative
## 6 negative-WORDCLOUDS
# Let us separate positive from negative
negative <- subset(dfn1, mood == "negative")
positive <- subset(dfn1, mood == "positive")
# Create a corpus of text for each one
corpus_p <- VCorpus(VectorSource(positive$text))
corpus_n <- VCorpus(VectorSource(negative$text))
# Cleaning the positive text
clean_p <- tm_map(corpus_p, content_transformer(tolower))
clean_p <- tm_map(clean_p, removeNumbers)
clean_p <- tm_map(clean_p, removeWords, stopwords())
clean_p <- tm_map(clean_p, removePunctuation)
# plot de wordcloud
wordcloud(clean_p, max.words = 40, random.order = F, random.color = F, colors=brewer.pal(8, "Dark2"), family = "mono", font = 6)
# Cleaning the negative text
clean_n <- tm_map(corpus_n, content_transformer(tolower))
clean_n <- tm_map(clean_n, removeNumbers)
clean_n <- tm_map(clean_n, removeWords, stopwords())
clean_n <- tm_map(clean_n, removePunctuation)
# plot de wordcloud
wordcloud(clean_n, max.words = 70, random.order = F, random.color = F, colors=brewer.pal(8, "Dark2"))
## Let us go back to analysis
# Create a corpus of all text in the dataset
corpus <- VCorpus(VectorSource(dfn1$text))
print(corpus)## <<VCorpus>>
## Metadata: corpus specific: 0, document level (indexed): 0
## Content: documents: 13973inspect(corpus[1:2])## <<VCorpus>>
## Metadata: corpus specific: 0, document level (indexed): 0
## Content: documents: 2
##
## [[1]]
## <<PlainTextDocument>>
## Metadata: 7
## Content: chars: 495
##
## [[2]]
## <<PlainTextDocument>>
## Metadata: 7
## Content: chars: 500as.character(corpus[[1]])## [1] "Itchy tags 3 tags sewn in, 2 small (about 1'' long) and 1 huge (about 2'' x 3''). very itchy so i cut them out. then the thread left behind was plasticy and even more itchy! how can you make an intimates item with such itchy tags? not comfortable at all! also - i love bralettes and wear them all the time including to work. i am a b cup. however, this one is so thin and flimsy that it gives no support even to a b cup - so for me this would only be a lounging bralette - if it wasn't so itchy!"# Cleaning and standardizing all text data and
# creating a DocumentTermMatrix in one shot
dtm <- DocumentTermMatrix(corpus, control = list(tolower = TRUE, removeNumbers = TRUE, stopwords = TRUE, removePunctuation = TRUE, stemming = TRUE))
dtm## <<DocumentTermMatrix (documents: 13973, terms: 10426)>>
## Non-/sparse entries: 349562/145332936
## Sparsity : 100%
## Maximal term length: 30
## Weighting : term frequency (tf)#Creating training and test datasets
spl <- sample(nrow(dtm), 0.7*nrow(dtm))
tr <- dtm[spl, ]
te <- dtm[-spl, ]
# We take the labels from the df data frame
tr_labels <- dfn1[spl, ]$mood
te_labels <- dfn1[-spl, ]$mood
# And see the proportion of positive and negative
prop.table(table(tr_labels))## tr_labels
## negative positive
## 0.05929864 0.94070136prop.table(table(te_labels))## te_labels
## negative positive
## 0.0625 0.9375# La proporcion en ambos casos es casi toda positiva, la negativa
# es practicamente inexistente (5,9 y 6,25) % respectivamente.
# Words that are in more than 40 and less 60 documents
findFreqTerms(tr, 40, 60)## [1] "accentu" "afraid" "age" "alin" "alon"
## [6] "alter" "appreci" "ask" "attent" "attract"
## [11] "bag" "bare" "bath" "beig" "believ"
## [16] "birthday" "bonus" "both" "bow" "boyfriend"
## [21] "breezi" "bring" "busi" "carri" "chanc"
## [26] "check" "chino" "choic" "chose" "christma"
## [31] "cling" "combin" "compar" "complaint" "complement"
## [36] "construct" "contrast" "cord" "creat" "cup"
## [41] "current" "darl" "date" "daughter" "deal"
## [46] "descript" "despit" "difficult" "done" "dressier"
## [51] "earli" "effortless" "embroid" "everywher" "expens"
## [56] "fade" "fallwint" "fashion" "flair" "flaw"
## [61] "flirti" "frumpi" "function" "funki" "gal"
## [66] "garment" "generous" "gift" "half" "happen"
## [71] "have" "head" "heavier" "height" "here"
## [76] "hour" "hous" "hung" "imagin" "initi"
## [81] "instant" "iron" "jewelri" "late" "leav"
## [86] "let" "life" "lighter" "lose" "loung"
## [91] "luxuri" "machin" "matern" "mean" "meant"
## [96] "mid" "middl" "minut" "miss" "mix"
## [101] "modern" "mom" "money" "moss" "most"
## [106] "movement" "multipl" "narrow" "natur" "near"
## [111] "number" "odd" "offer" "oliv" "opinion"
## [116] "overwhelm" "pajama" "pale" "particular" "pass"
## [121] "penni" "perhap" "plain" "play" "portion"
## [126] "possibl" "pricey" "quick" "rare" "realiz"
## [131] "regret" "remind" "requir" "reveal" "rib"
## [136] "scarf" "search" "section" "seethrough" "sell"
## [141] "send" "serious" "set" "sewn" "shrink"
## [146] "silver" "skim" "sleep" "slender" "snap"
## [151] "sock" "solid" "somewhat" "sort" "springsumm"
## [156] "star" "state" "statement" "stick" "stock"
## [161] "stop" "strapless" "suppos" "swimsuit" "swingi"
## [166] "tag" "taller" "taup" "teal" "terribl"
## [171] "the" "there" "thrill" "tone" "trim"
## [176] "trip" "trouser" "turquois" "turtleneck" "twice"
## [181] "twist" "unless" "unusu" "vacat" "velvet"
## [186] "vibe" "vintag" "vneck" "waistband" "waistlin"
## [191] "warmer" "water" "websit" "weigh" "wellmad"
## [196] "which" "whim" "wine" "with" "woman"
## [201] "write" "xsp"# Words that appear in more that 5 documents: 557
freq_words <- findFreqTerms(tr, 5)
str(freq_words)## chr [1:2414] "abil" "abl" "about" "abov" "absolut" "abt" "accent" ...# We will use only documents in train and test with
# these words to avoid waiting too long.
tr1 <- tr[, freq_words]
te1 <- te[, freq_words]
# We convert to matrix
convert_counts <- function(x) {x <- ifelse(x > 0, "Yes", "No") }
train <- apply(tr1, 2, convert_counts)
test <- apply(te1, 2, convert_counts)
#Training a model on the data
m.nb <- naiveBayes(train, tr_labels)
p.nb <- predict(m.nb, test)
(tt <- table(p.nb, te_labels))## te_labels
## p.nb negative positive
## negative 206 67
## positive 56 3863sum(diag(tt))/sum(tt)## [1] 0.9706584