Introduction to Text Analysis in R (DataCamp)
Ch. 1 - Wrangling Text
Text as data
[Video]
Loading packages
library(tidyverse)## ── Attaching packages ─────────────────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.3 ✓ dplyr 1.0.1
## ✓ tidyr 1.1.1 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0## ── Conflicts ────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()Importing review data
review_data <- read_csv("Roomba Reviews.csv")## Parsed with column specification:
## cols(
## date = col_character(),
## product = col_character(),
## stars = col_double(),
## title = col_character(),
## review = col_character()
## )review_data## # A tibble: 1,833 x 5
## date product stars title review
## <chr> <chr> <dbl> <chr> <chr>
## 1 2/28/15 iRobot Roomba… 5 Five Stars "You would not believe how w…
## 2 1/12/15 iRobot Roomba… 4 Four Stars "You just walk away and it d…
## 3 12/26/… iRobot Roomba… 5 Awesome love it. "You have to Roomba proof yo…
## 4 8/4/13 iRobot Roomba… 3 Love-hate this va… "Yes, it's a fascinating, al…
## 5 12/22/… iRobot Roomba… 5 This vacuum is fa… "Years ago I bought one of t…
## 6 12/27/… iRobot Roomba… 5 Wow! "Wow.Wow. I never knew my f…
## 7 8/17/15 iRobot Roomba… 1 Terrible Product … "Wow.. I don't know what to …
## 8 12/28/… iRobot Roomba… 5 Super-impressed b… "Wow, wow, WOW! I wanted to…
## 9 1/19/14 iRobot Roomba… 5 LOVE THIS "Wow, the Roomba is the best…
## 10 7/2/15 iRobot Roomba… 5 Stress is bad; Ro… "Wow, it changes your life. …
## # … with 1,823 more rowsUsing filter() and summarize()
review_data %>%
filter(product == "iRobot Roomba 650 for Pets") %>%
summarize(stars_mean = mean(stars))## # A tibble: 1 x 1
## stars_mean
## <dbl>
## 1 4.49review_data %>%
group_by(product) %>%
summarize(stars_mean = mean(stars))## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 2
## product stars_mean
## <chr> <dbl>
## 1 iRobot Roomba 650 for Pets 4.49
## 2 iRobot Roomba 880 for Pets and Allergies 4.42review_data %>%
group_by(product) %>%
summarize(review_mean = mean(review))## Warning in mean.default(review): argument is not numeric or logical: returning
## NA
## Warning in mean.default(review): argument is not numeric or logical: returning
## NA## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 2
## product review_mean
## <chr> <dbl>
## 1 iRobot Roomba 650 for Pets NA
## 2 iRobot Roomba 880 for Pets and Allergies NAAirline tweet data
# Load the tidyverse packages
library(tidyverse)
# Print twitter_data
twitter_data## # A tibble: 7,044 x 6
## tweet_id date complaint_label tweet_text usr_followers_c…
## <dbl> <dttm> <chr> <chr> <dbl>
## 1 4.77e17 2014-06-12 00:07:25 Non-Complaint 1. Haneda… 152
## 2 4.77e17 2014-06-12 00:12:30 Non-Complaint My plane … 184
## 3 4.77e17 2014-06-12 00:13:56 Complaint So appare… 136
## 4 4.77e17 2014-06-12 00:16:09 Non-Complaint Je suppor… 1
## 5 4.77e17 2014-06-12 00:17:37 Non-Complaint Dear @Ceb… 67
## 6 4.77e17 2014-06-12 00:18:49 Complaint Boo @Delt… 138
## 7 4.77e17 2014-06-12 00:26:42 Non-Complaint #PALFlies… 21
## 8 4.77e17 2014-06-12 00:31:08 Complaint @JetBlue … 133
## 9 4.77e17 2014-06-12 00:35:27 Non-Complaint Celebrati… 607
## 10 4.77e17 2014-06-12 00:46:47 Non-Complaint Don't do … 165
## # … with 7,034 more rows, and 1 more variable: usr_verified <lgl># Print just the complaints in twitter_data
twitter_data %>%
filter(complaint_label == "Complaint")## # A tibble: 1,676 x 6
## tweet_id date complaint_label tweet_text usr_followers_c…
## <dbl> <dttm> <chr> <chr> <dbl>
## 1 4.77e17 2014-06-12 00:13:56 Complaint So appare… 136
## 2 4.77e17 2014-06-12 00:18:49 Complaint Boo @Delt… 138
## 3 4.77e17 2014-06-12 00:31:08 Complaint @JetBlue … 133
## 4 4.77e17 2014-06-12 00:49:18 Complaint @TheRealK… 221
## 5 4.77e17 2014-06-12 00:54:32 Complaint @American… 10
## 6 4.77e17 2014-06-12 00:58:36 Complaint I strongl… 158
## 7 4.77e17 2014-06-12 01:08:40 Complaint @doncliff… 55
## 8 4.77e17 2014-06-12 01:27:36 Complaint @USAirway… 995
## 9 4.77e17 2014-06-12 02:17:21 Complaint Just aske… 7005
## 10 4.77e17 2014-06-12 02:18:16 Complaint @migs647 … 919
## # … with 1,666 more rows, and 1 more variable: usr_verified <lgl>Grouped summaries
# Start with the data frame
twitter_data %>%
# Group the whether or not the tweet is a complaint
group_by(complaint_label) %>%
# Compute the mean, min, and max follower counts
summarize(
avg_followers = mean(usr_followers_count),
min_followers = min(usr_followers_count),
max_followers = max(usr_followers_count)
)## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 4
## complaint_label avg_followers min_followers max_followers
## <chr> <dbl> <dbl> <dbl>
## 1 Complaint 3234. 0 1259803
## 2 Non-Complaint 4487. 0 2200851Counting categorical data
[Video]
Column types
review_data## # A tibble: 1,833 x 5
## date product stars title review
## <chr> <chr> <dbl> <chr> <chr>
## 1 2/28/15 iRobot Roomba… 5 Five Stars "You would not believe how w…
## 2 1/12/15 iRobot Roomba… 4 Four Stars "You just walk away and it d…
## 3 12/26/… iRobot Roomba… 5 Awesome love it. "You have to Roomba proof yo…
## 4 8/4/13 iRobot Roomba… 3 Love-hate this va… "Yes, it's a fascinating, al…
## 5 12/22/… iRobot Roomba… 5 This vacuum is fa… "Years ago I bought one of t…
## 6 12/27/… iRobot Roomba… 5 Wow! "Wow.Wow. I never knew my f…
## 7 8/17/15 iRobot Roomba… 1 Terrible Product … "Wow.. I don't know what to …
## 8 12/28/… iRobot Roomba… 5 Super-impressed b… "Wow, wow, WOW! I wanted to…
## 9 1/19/14 iRobot Roomba… 5 LOVE THIS "Wow, the Roomba is the best…
## 10 7/2/15 iRobot Roomba… 5 Stress is bad; Ro… "Wow, it changes your life. …
## # … with 1,823 more rowsSummarize with n()
review_data %>%
summarize(number_rows = n())## # A tibble: 1 x 1
## number_rows
## <int>
## 1 1833review_data %>%
group_by(product) %>%
summarize(number_rows = n())## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 2
## product number_rows
## <chr> <int>
## 1 iRobot Roomba 650 for Pets 633
## 2 iRobot Roomba 880 for Pets and Allergies 1200Summarize with count()
review_data %>%
count(product)## # A tibble: 2 x 2
## product n
## <chr> <int>
## 1 iRobot Roomba 650 for Pets 633
## 2 iRobot Roomba 880 for Pets and Allergies 1200review_data %>%
count(product) %>%
arrange(desc(n))## # A tibble: 2 x 2
## product n
## <chr> <int>
## 1 iRobot Roomba 880 for Pets and Allergies 1200
## 2 iRobot Roomba 650 for Pets 633Counting user types
# Load the tidyverse package
library(tidyverse)
twitter_data %>%
# Filter for just the complaints
filter(complaint_label == "Complaint") %>%
# Count the number of verified and non-verified users
count(usr_verified)## # A tibble: 2 x 2
## usr_verified n
## <lgl> <int>
## 1 FALSE 1650
## 2 TRUE 26Summarizing user types
library(tidyverse)
twitter_data %>%
# Group by whether or not a user is verified
group_by(usr_verified) %>%
summarize(
# Compute the average number of followers
avg_followers = mean(usr_followers_count),
# Count the number of users in each category
n = n()
)## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 3
## usr_verified avg_followers n
## <lgl> <dbl> <int>
## 1 FALSE 1999. 6927
## 2 TRUE 133849. 117Tokenizing and counting
[Video]
Tokenizing Text
- Bag of word: Words in a document are independent
- Every separate body of text is a document
- Every unique word is a term
- Every occurrence of a term is a token
- Creating a bag of words is called tokenizing
Using unnest_tokens()
tidy_review <- review_data %>%
unnest_tokens(word, review)
tidy_review## # A tibble: 229,481 x 5
## date product stars title word
## <chr> <chr> <dbl> <chr> <chr>
## 1 2/28/15 iRobot Roomba 650 for Pets 5 Five Stars you
## 2 2/28/15 iRobot Roomba 650 for Pets 5 Five Stars would
## 3 2/28/15 iRobot Roomba 650 for Pets 5 Five Stars not
## 4 2/28/15 iRobot Roomba 650 for Pets 5 Five Stars believe
## 5 2/28/15 iRobot Roomba 650 for Pets 5 Five Stars how
## 6 2/28/15 iRobot Roomba 650 for Pets 5 Five Stars well
## 7 2/28/15 iRobot Roomba 650 for Pets 5 Five Stars this
## 8 2/28/15 iRobot Roomba 650 for Pets 5 Five Stars works
## 9 1/12/15 iRobot Roomba 650 for Pets 4 Four Stars you
## 10 1/12/15 iRobot Roomba 650 for Pets 4 Four Stars just
## # … with 229,471 more rowsCounting words
tidy_review %>%
count(word) %>%
arrange(desc(n))## # A tibble: 10,310 x 2
## word n
## <chr> <int>
## 1 the 11785
## 2 it 7905
## 3 and 6794
## 4 to 6440
## 5 i 6034
## 6 a 5884
## 7 is 3347
## 8 of 3229
## 9 have 2470
## 10 that 2410
## # … with 10,300 more rowsUsing anti_join()
tidy_review2 <- review_data %>%
unnest_tokens(word, review) %>%
anti_join(stop_words)## Joining, by = "word"tidy_review2## # A tibble: 78,868 x 5
## date product stars title word
## <chr> <chr> <dbl> <chr> <chr>
## 1 1/12/15 iRobot Roomba 650 for Pets 4 Four Stars walk
## 2 1/12/15 iRobot Roomba 650 for Pets 4 Four Stars rest
## 3 12/26/13 iRobot Roomba 650 for Pets 5 Awesome love it. roomba
## 4 12/26/13 iRobot Roomba 650 for Pets 5 Awesome love it. proof
## 5 12/26/13 iRobot Roomba 650 for Pets 5 Awesome love it. house
## 6 12/26/13 iRobot Roomba 650 for Pets 5 Awesome love it. awesome
## 7 12/26/13 iRobot Roomba 650 for Pets 5 Awesome love it. pet
## 8 12/26/13 iRobot Roomba 650 for Pets 5 Awesome love it. cleans
## 9 8/4/13 iRobot Roomba 650 for Pets 3 Love-hate this vaccuum fascinating
## 10 8/4/13 iRobot Roomba 650 for Pets 3 Love-hate this vaccuum albeit
## # … with 78,858 more rowsCounting words again
tidy_review2 %>%
count(word) %>%
arrange(desc(n))## # A tibble: 9,672 x 2
## word n
## <chr> <int>
## 1 roomba 2286
## 2 clean 1204
## 3 vacuum 989
## 4 hair 900
## 5 cleaning 809
## 6 time 795
## 7 house 745
## 8 floors 657
## 9 day 578
## 10 floor 561
## # … with 9,662 more rowsTokenizing and counting
# Load the tidyverse and tidytext packages
library(tidyverse)
library(tidytext)
tidy_twitter <- twitter_data %>%
# Tokenize the twitter data
unnest_tokens(word, tweet_text)
tidy_twitter %>%
# Compute word counts
count(word) %>%
# Arrange the counts in descending order
arrange(desc(n))## # A tibble: 18,600 x 2
## word n
## <chr> <int>
## 1 to 2834
## 2 the 2212
## 3 a 1989
## 4 i 1752
## 5 t.co 1405
## 6 http 1361
## 7 for 1356
## 8 you 1345
## 9 on 1289
## 10 and 1153
## # … with 18,590 more rowsCleaning and counting
tidy_twitter <- twitter_data %>%
# Tokenize the twitter data
unnest_tokens(word, tweet_text) %>%
# Remove stop words
anti_join(stop_words)## Joining, by = "word"tidy_twitter %>%
# Filter to keep complaints only
filter(complaint_label == "Complaint") %>%
# Compute word counts and arrange in descending order
count(word) %>%
arrange(desc(n))## # A tibble: 3,863 x 2
## word n
## <chr> <int>
## 1 flight 459
## 2 united 362
## 3 americanair 294
## 4 usairways 207
## 5 time 167
## 6 delta 141
## 7 service 137
## 8 2 129
## 9 delayed 123
## 10 british_airways 121
## # … with 3,853 more rowsCh. 2 - Visualizing Text
Plotting word count
[Video]
Starting with tidy text
tidy_review <- review_data %>%
mutate(id = row_number()) %>%
unnest_tokens(word, review) %>%
anti_join(stop_words)## Joining, by = "word"tidy_review## # A tibble: 78,868 x 6
## date product stars title id word
## <chr> <chr> <dbl> <chr> <int> <chr>
## 1 1/12/15 iRobot Roomba 650 for P… 4 Four Stars 2 walk
## 2 1/12/15 iRobot Roomba 650 for P… 4 Four Stars 2 rest
## 3 12/26/13 iRobot Roomba 650 for P… 5 Awesome love it. 3 roomba
## 4 12/26/13 iRobot Roomba 650 for P… 5 Awesome love it. 3 proof
## 5 12/26/13 iRobot Roomba 650 for P… 5 Awesome love it. 3 house
## 6 12/26/13 iRobot Roomba 650 for P… 5 Awesome love it. 3 awesome
## 7 12/26/13 iRobot Roomba 650 for P… 5 Awesome love it. 3 pet
## 8 12/26/13 iRobot Roomba 650 for P… 5 Awesome love it. 3 cleans
## 9 8/4/13 iRobot Roomba 650 for P… 3 Love-hate this vacc… 4 fascinati…
## 10 8/4/13 iRobot Roomba 650 for P… 3 Love-hate this vacc… 4 albeit
## # … with 78,858 more rowsVisualizing count with geom_col()
word_counts <- tidy_review %>%
count(word) %>%
arrange(desc(n))
ggplot(
word_counts, aes(x = word, y = n)
) + geom_col()
filter() before visualizing
word_counts2 <- tidy_review %>%
count(word) %>%
filter(n > 300) %>%
arrange(desc(n))
word_counts2## # A tibble: 25 x 2
## word n
## <chr> <int>
## 1 roomba 2286
## 2 clean 1204
## 3 vacuum 989
## 4 hair 900
## 5 cleaning 809
## 6 time 795
## 7 house 745
## 8 floors 657
## 9 day 578
## 10 floor 561
## # … with 15 more rowsAnd flip coordinates
ggplot(word_counts2, aes(x = word, y = n)) +
geom_col() +
coord_flip() +
ggtitle("Review Word Counts")
Visualizing complaints
word_counts <- tidy_twitter %>%
filter(complaint_label == "Complaint") %>%
count(word) %>%
# Keep words with count greater than 100
filter(n > 100)
# Create a bar plot using word_counts with x = word
ggplot(word_counts, aes(x = word, y = n)) +
geom_col() +
# Flip the plot coordinates
coord_flip()
Visualizing non-compaints
word_counts <- tidy_twitter %>%
# Only keep the non-complaints
filter(complaint_label == "Non-Complaint") %>%
count(word) %>%
filter(n > 150)
# Create a bar plot using the new word_counts
ggplot(word_counts, aes(x = word, y = n)) +
geom_col() +
coord_flip() +
# Title the plot "Non-Complaint Word Counts"
ggtitle("Non-Complaint Word Counts")
Improving word count plots
[Video]
Custom stop words
stop_words## word lexicon
## 1 a SMART
## 2 a's SMART
## 3 able SMART
## 4 about SMART
## 5 above SMART
## 6 according SMART
## 7 accordingly SMART
## 8 across SMART
## 9 actually SMART
## 10 after SMART
## 11 afterwards SMART
## 12 again SMART
## 13 against SMART
## 14 ain't SMART
## 15 all SMART
## 16 allow SMART
## 17 allows SMART
## 18 almost SMART
## 19 alone SMART
## 20 along SMART
## 21 already SMART
## 22 also SMART
## 23 although SMART
## 24 always SMART
## 25 am SMART
## 26 among SMART
## 27 amongst SMART
## 28 an SMART
## 29 and SMART
## 30 another SMART
## 31 any SMART
## 32 anybody SMART
## 33 anyhow SMART
## 34 anyone SMART
## 35 anything SMART
## 36 anyway SMART
## 37 anyways SMART
## 38 anywhere SMART
## 39 apart SMART
## 40 appear SMART
## 41 appreciate SMART
## 42 appropriate SMART
## 43 are SMART
## 44 aren't SMART
## 45 around SMART
## 46 as SMART
## 47 aside SMART
## 48 ask SMART
## 49 asking SMART
## 50 associated SMART
## 51 at SMART
## 52 available SMART
## 53 away SMART
## 54 awfully SMART
## 55 b SMART
## 56 be SMART
## 57 became SMART
## 58 because SMART
## 59 become SMART
## 60 becomes SMART
## 61 becoming SMART
## 62 been SMART
## 63 before SMART
## 64 beforehand SMART
## 65 behind SMART
## 66 being SMART
## 67 believe SMART
## 68 below SMART
## 69 beside SMART
## 70 besides SMART
## 71 best SMART
## 72 better SMART
## 73 between SMART
## 74 beyond SMART
## 75 both SMART
## 76 brief SMART
## 77 but SMART
## 78 by SMART
## 79 c SMART
## 80 c'mon SMART
## 81 c's SMART
## 82 came SMART
## 83 can SMART
## 84 can't SMART
## 85 cannot SMART
## 86 cant SMART
## 87 cause SMART
## 88 causes SMART
## 89 certain SMART
## 90 certainly SMART
## 91 changes SMART
## 92 clearly SMART
## 93 co SMART
## 94 com SMART
## 95 come SMART
## 96 comes SMART
## 97 concerning SMART
## 98 consequently SMART
## 99 consider SMART
## 100 considering SMART
## 101 contain SMART
## 102 containing SMART
## 103 contains SMART
## 104 corresponding SMART
## 105 could SMART
## 106 couldn't SMART
## 107 course SMART
## 108 currently SMART
## 109 d SMART
## 110 definitely SMART
## 111 described SMART
## 112 despite SMART
## 113 did SMART
## 114 didn't SMART
## 115 different SMART
## 116 do SMART
## 117 does SMART
## 118 doesn't SMART
## 119 doing SMART
## 120 don't SMART
## 121 done SMART
## 122 down SMART
## 123 downwards SMART
## 124 during SMART
## 125 e SMART
## 126 each SMART
## 127 edu SMART
## 128 eg SMART
## 129 eight SMART
## 130 either SMART
## 131 else SMART
## 132 elsewhere SMART
## 133 enough SMART
## 134 entirely SMART
## 135 especially SMART
## 136 et SMART
## 137 etc SMART
## 138 even SMART
## 139 ever SMART
## 140 every SMART
## 141 everybody SMART
## 142 everyone SMART
## 143 everything SMART
## 144 everywhere SMART
## 145 ex SMART
## 146 exactly SMART
## 147 example SMART
## 148 except SMART
## 149 f SMART
## 150 far SMART
## 151 few SMART
## 152 fifth SMART
## 153 first SMART
## 154 five SMART
## 155 followed SMART
## 156 following SMART
## 157 follows SMART
## 158 for SMART
## 159 former SMART
## 160 formerly SMART
## 161 forth SMART
## 162 four SMART
## 163 from SMART
## 164 further SMART
## 165 furthermore SMART
## 166 g SMART
## 167 get SMART
## 168 gets SMART
## 169 getting SMART
## 170 given SMART
## 171 gives SMART
## 172 go SMART
## 173 goes SMART
## 174 going SMART
## 175 gone SMART
## 176 got SMART
## 177 gotten SMART
## 178 greetings SMART
## 179 h SMART
## 180 had SMART
## 181 hadn't SMART
## 182 happens SMART
## 183 hardly SMART
## 184 has SMART
## 185 hasn't SMART
## 186 have SMART
## 187 haven't SMART
## 188 having SMART
## 189 he SMART
## 190 he's SMART
## 191 hello SMART
## 192 help SMART
## 193 hence SMART
## 194 her SMART
## 195 here SMART
## 196 here's SMART
## 197 hereafter SMART
## 198 hereby SMART
## 199 herein SMART
## 200 hereupon SMART
## 201 hers SMART
## 202 herself SMART
## 203 hi SMART
## 204 him SMART
## 205 himself SMART
## 206 his SMART
## 207 hither SMART
## 208 hopefully SMART
## 209 how SMART
## 210 howbeit SMART
## 211 however SMART
## 212 i SMART
## 213 i'd SMART
## 214 i'll SMART
## 215 i'm SMART
## 216 i've SMART
## 217 ie SMART
## 218 if SMART
## 219 ignored SMART
## 220 immediate SMART
## 221 in SMART
## 222 inasmuch SMART
## 223 inc SMART
## 224 indeed SMART
## 225 indicate SMART
## 226 indicated SMART
## 227 indicates SMART
## 228 inner SMART
## 229 insofar SMART
## 230 instead SMART
## 231 into SMART
## 232 inward SMART
## 233 is SMART
## 234 isn't SMART
## 235 it SMART
## 236 it'd SMART
## 237 it'll SMART
## 238 it's SMART
## 239 its SMART
## 240 itself SMART
## 241 j SMART
## 242 just SMART
## 243 k SMART
## 244 keep SMART
## 245 keeps SMART
## 246 kept SMART
## 247 know SMART
## 248 knows SMART
## 249 known SMART
## 250 l SMART
## 251 last SMART
## 252 lately SMART
## 253 later SMART
## 254 latter SMART
## 255 latterly SMART
## 256 least SMART
## 257 less SMART
## 258 lest SMART
## 259 let SMART
## 260 let's SMART
## 261 like SMART
## 262 liked SMART
## 263 likely SMART
## 264 little SMART
## 265 look SMART
## 266 looking SMART
## 267 looks SMART
## 268 ltd SMART
## 269 m SMART
## 270 mainly SMART
## 271 many SMART
## 272 may SMART
## 273 maybe SMART
## 274 me SMART
## 275 mean SMART
## 276 meanwhile SMART
## 277 merely SMART
## 278 might SMART
## 279 more SMART
## 280 moreover SMART
## 281 most SMART
## 282 mostly SMART
## 283 much SMART
## 284 must SMART
## 285 my SMART
## 286 myself SMART
## 287 n SMART
## 288 name SMART
## 289 namely SMART
## 290 nd SMART
## 291 near SMART
## 292 nearly SMART
## 293 necessary SMART
## 294 need SMART
## 295 needs SMART
## 296 neither SMART
## 297 never SMART
## 298 nevertheless SMART
## 299 new SMART
## 300 next SMART
## 301 nine SMART
## 302 no SMART
## 303 nobody SMART
## 304 non SMART
## 305 none SMART
## 306 noone SMART
## 307 nor SMART
## 308 normally SMART
## 309 not SMART
## 310 nothing SMART
## 311 novel SMART
## 312 now SMART
## 313 nowhere SMART
## 314 o SMART
## 315 obviously SMART
## 316 of SMART
## 317 off SMART
## 318 often SMART
## 319 oh SMART
## 320 ok SMART
## 321 okay SMART
## 322 old SMART
## 323 on SMART
## 324 once SMART
## 325 one SMART
## 326 ones SMART
## 327 only SMART
## 328 onto SMART
## 329 or SMART
## 330 other SMART
## 331 others SMART
## 332 otherwise SMART
## 333 ought SMART
## 334 our SMART
## 335 ours SMART
## 336 ourselves SMART
## 337 out SMART
## 338 outside SMART
## 339 over SMART
## 340 overall SMART
## 341 own SMART
## 342 p SMART
## 343 particular SMART
## 344 particularly SMART
## 345 per SMART
## 346 perhaps SMART
## 347 placed SMART
## 348 please SMART
## 349 plus SMART
## 350 possible SMART
## 351 presumably SMART
## 352 probably SMART
## 353 provides SMART
## 354 q SMART
## 355 que SMART
## 356 quite SMART
## 357 qv SMART
## 358 r SMART
## 359 rather SMART
## 360 rd SMART
## 361 re SMART
## 362 really SMART
## 363 reasonably SMART
## 364 regarding SMART
## 365 regardless SMART
## 366 regards SMART
## 367 relatively SMART
## 368 respectively SMART
## 369 right SMART
## 370 s SMART
## 371 said SMART
## 372 same SMART
## 373 saw SMART
## 374 say SMART
## 375 saying SMART
## 376 says SMART
## 377 second SMART
## 378 secondly SMART
## 379 see SMART
## 380 seeing SMART
## 381 seem SMART
## 382 seemed SMART
## 383 seeming SMART
## 384 seems SMART
## 385 seen SMART
## 386 self SMART
## 387 selves SMART
## 388 sensible SMART
## 389 sent SMART
## 390 serious SMART
## 391 seriously SMART
## 392 seven SMART
## 393 several SMART
## 394 shall SMART
## 395 she SMART
## 396 should SMART
## 397 shouldn't SMART
## 398 since SMART
## 399 six SMART
## 400 so SMART
## 401 some SMART
## 402 somebody SMART
## 403 somehow SMART
## 404 someone SMART
## 405 something SMART
## 406 sometime SMART
## 407 sometimes SMART
## 408 somewhat SMART
## 409 somewhere SMART
## 410 soon SMART
## 411 sorry SMART
## 412 specified SMART
## 413 specify SMART
## 414 specifying SMART
## 415 still SMART
## 416 sub SMART
## 417 such SMART
## 418 sup SMART
## 419 sure SMART
## 420 t SMART
## 421 t's SMART
## 422 take SMART
## 423 taken SMART
## 424 tell SMART
## 425 tends SMART
## 426 th SMART
## 427 than SMART
## 428 thank SMART
## 429 thanks SMART
## 430 thanx SMART
## 431 that SMART
## 432 that's SMART
## 433 thats SMART
## 434 the SMART
## 435 their SMART
## 436 theirs SMART
## 437 them SMART
## 438 themselves SMART
## 439 then SMART
## 440 thence SMART
## 441 there SMART
## 442 there's SMART
## 443 thereafter SMART
## 444 thereby SMART
## 445 therefore SMART
## 446 therein SMART
## 447 theres SMART
## 448 thereupon SMART
## 449 these SMART
## 450 they SMART
## 451 they'd SMART
## 452 they'll SMART
## 453 they're SMART
## 454 they've SMART
## 455 think SMART
## 456 third SMART
## 457 this SMART
## 458 thorough SMART
## 459 thoroughly SMART
## 460 those SMART
## 461 though SMART
## 462 three SMART
## 463 through SMART
## 464 throughout SMART
## 465 thru SMART
## 466 thus SMART
## 467 to SMART
## 468 together SMART
## 469 too SMART
## 470 took SMART
## 471 toward SMART
## 472 towards SMART
## 473 tried SMART
## 474 tries SMART
## 475 truly SMART
## 476 try SMART
## 477 trying SMART
## 478 twice SMART
## 479 two SMART
## 480 u SMART
## 481 un SMART
## 482 under SMART
## 483 unfortunately SMART
## 484 unless SMART
## 485 unlikely SMART
## 486 until SMART
## 487 unto SMART
## 488 up SMART
## 489 upon SMART
## 490 us SMART
## 491 use SMART
## 492 used SMART
## 493 useful SMART
## 494 uses SMART
## 495 using SMART
## 496 usually SMART
## 497 uucp SMART
## 498 v SMART
## 499 value SMART
## 500 various SMART
## 501 very SMART
## 502 via SMART
## 503 viz SMART
## 504 vs SMART
## 505 w SMART
## 506 want SMART
## 507 wants SMART
## 508 was SMART
## 509 wasn't SMART
## 510 way SMART
## 511 we SMART
## 512 we'd SMART
## 513 we'll SMART
## 514 we're SMART
## 515 we've SMART
## 516 welcome SMART
## 517 well SMART
## 518 went SMART
## 519 were SMART
## 520 weren't SMART
## 521 what SMART
## 522 what's SMART
## 523 whatever SMART
## 524 when SMART
## 525 whence SMART
## 526 whenever SMART
## 527 where SMART
## 528 where's SMART
## 529 whereafter SMART
## 530 whereas SMART
## 531 whereby SMART
## 532 wherein SMART
## 533 whereupon SMART
## 534 wherever SMART
## 535 whether SMART
## 536 which SMART
## 537 while SMART
## 538 whither SMART
## 539 who SMART
## 540 who's SMART
## 541 whoever SMART
## 542 whole SMART
## 543 whom SMART
## 544 whose SMART
## 545 why SMART
## 546 will SMART
## 547 willing SMART
## 548 wish SMART
## 549 with SMART
## 550 within SMART
## 551 without SMART
## 552 won't SMART
## 553 wonder SMART
## 554 would SMART
## 555 would SMART
## 556 wouldn't SMART
## 557 x SMART
## 558 y SMART
## 559 yes SMART
## 560 yet SMART
## 561 you SMART
## 562 you'd SMART
## 563 you'll SMART
## 564 you're SMART
## 565 you've SMART
## 566 your SMART
## 567 yours SMART
## 568 yourself SMART
## 569 yourselves SMART
## 570 z SMART
## 571 zero SMART
## 572 i snowball
## 573 me snowball
## 574 my snowball
## 575 myself snowball
## 576 we snowball
## 577 our snowball
## 578 ours snowball
## 579 ourselves snowball
## 580 you snowball
## 581 your snowball
## 582 yours snowball
## 583 yourself snowball
## 584 yourselves snowball
## 585 he snowball
## 586 him snowball
## 587 his snowball
## 588 himself snowball
## 589 she snowball
## 590 her snowball
## 591 hers snowball
## 592 herself snowball
## 593 it snowball
## 594 its snowball
## 595 itself snowball
## 596 they snowball
## 597 them snowball
## 598 their snowball
## 599 theirs snowball
## 600 themselves snowball
## 601 what snowball
## 602 which snowball
## 603 who snowball
## 604 whom snowball
## 605 this snowball
## 606 that snowball
## 607 these snowball
## 608 those snowball
## 609 am snowball
## 610 is snowball
## 611 are snowball
## 612 was snowball
## 613 were snowball
## 614 be snowball
## 615 been snowball
## 616 being snowball
## 617 have snowball
## 618 has snowball
## 619 had snowball
## 620 having snowball
## 621 do snowball
## 622 does snowball
## 623 did snowball
## 624 doing snowball
## 625 would snowball
## 626 should snowball
## 627 could snowball
## 628 ought snowball
## 629 i'm snowball
## 630 you're snowball
## 631 he's snowball
## 632 she's snowball
## 633 it's snowball
## 634 we're snowball
## 635 they're snowball
## 636 i've snowball
## 637 you've snowball
## 638 we've snowball
## 639 they've snowball
## 640 i'd snowball
## 641 you'd snowball
## 642 he'd snowball
## 643 she'd snowball
## 644 we'd snowball
## 645 they'd snowball
## 646 i'll snowball
## 647 you'll snowball
## 648 he'll snowball
## 649 she'll snowball
## 650 we'll snowball
## 651 they'll snowball
## 652 isn't snowball
## 653 aren't snowball
## 654 wasn't snowball
## 655 weren't snowball
## 656 hasn't snowball
## 657 haven't snowball
## 658 hadn't snowball
## 659 doesn't snowball
## 660 don't snowball
## 661 didn't snowball
## 662 won't snowball
## 663 wouldn't snowball
## 664 shan't snowball
## 665 shouldn't snowball
## 666 can't snowball
## 667 cannot snowball
## 668 couldn't snowball
## 669 mustn't snowball
## 670 let's snowball
## 671 that's snowball
## 672 who's snowball
## 673 what's snowball
## 674 here's snowball
## 675 there's snowball
## 676 when's snowball
## 677 where's snowball
## 678 why's snowball
## 679 how's snowball
## 680 a snowball
## 681 an snowball
## 682 the snowball
## 683 and snowball
## 684 but snowball
## 685 if snowball
## 686 or snowball
## 687 because snowball
## 688 as snowball
## 689 until snowball
## 690 while snowball
## 691 of snowball
## 692 at snowball
## 693 by snowball
## 694 for snowball
## 695 with snowball
## 696 about snowball
## 697 against snowball
## 698 between snowball
## 699 into snowball
## 700 through snowball
## 701 during snowball
## 702 before snowball
## 703 after snowball
## 704 above snowball
## 705 below snowball
## 706 to snowball
## 707 from snowball
## 708 up snowball
## 709 down snowball
## 710 in snowball
## 711 out snowball
## 712 on snowball
## 713 off snowball
## 714 over snowball
## 715 under snowball
## 716 again snowball
## 717 further snowball
## 718 then snowball
## 719 once snowball
## 720 here snowball
## 721 there snowball
## 722 when snowball
## 723 where snowball
## 724 why snowball
## 725 how snowball
## 726 all snowball
## 727 any snowball
## 728 both snowball
## 729 each snowball
## 730 few snowball
## 731 more snowball
## 732 most snowball
## 733 other snowball
## 734 some snowball
## 735 such snowball
## 736 no snowball
## 737 nor snowball
## 738 not snowball
## 739 only snowball
## 740 own snowball
## 741 same snowball
## 742 so snowball
## 743 than snowball
## 744 too snowball
## 745 very snowball
## 746 a onix
## 747 about onix
## 748 above onix
## 749 across onix
## 750 after onix
## 751 again onix
## 752 against onix
## 753 all onix
## 754 almost onix
## 755 alone onix
## 756 along onix
## 757 already onix
## 758 also onix
## 759 although onix
## 760 always onix
## 761 among onix
## 762 an onix
## 763 and onix
## 764 another onix
## 765 any onix
## 766 anybody onix
## 767 anyone onix
## 768 anything onix
## 769 anywhere onix
## 770 are onix
## 771 area onix
## 772 areas onix
## 773 around onix
## 774 as onix
## 775 ask onix
## 776 asked onix
## 777 asking onix
## 778 asks onix
## 779 at onix
## 780 away onix
## 781 back onix
## 782 backed onix
## 783 backing onix
## 784 backs onix
## 785 be onix
## 786 became onix
## 787 because onix
## 788 become onix
## 789 becomes onix
## 790 been onix
## 791 before onix
## 792 began onix
## 793 behind onix
## 794 being onix
## 795 beings onix
## 796 best onix
## 797 better onix
## 798 between onix
## 799 big onix
## 800 both onix
## 801 but onix
## 802 by onix
## 803 came onix
## 804 can onix
## 805 cannot onix
## 806 case onix
## 807 cases onix
## 808 certain onix
## 809 certainly onix
## 810 clear onix
## 811 clearly onix
## 812 come onix
## 813 could onix
## 814 did onix
## 815 differ onix
## 816 different onix
## 817 differently onix
## 818 do onix
## 819 does onix
## 820 done onix
## 821 down onix
## 822 down onix
## 823 downed onix
## 824 downing onix
## 825 downs onix
## 826 during onix
## 827 each onix
## 828 early onix
## 829 either onix
## 830 end onix
## 831 ended onix
## 832 ending onix
## 833 ends onix
## 834 enough onix
## 835 even onix
## 836 evenly onix
## 837 ever onix
## 838 every onix
## 839 everybody onix
## 840 everyone onix
## 841 everything onix
## 842 everywhere onix
## 843 face onix
## 844 faces onix
## 845 fact onix
## 846 facts onix
## 847 far onix
## 848 felt onix
## 849 few onix
## 850 find onix
## 851 finds onix
## 852 first onix
## 853 for onix
## 854 four onix
## 855 from onix
## 856 full onix
## 857 fully onix
## 858 further onix
## 859 furthered onix
## 860 furthering onix
## 861 furthers onix
## 862 gave onix
## 863 general onix
## 864 generally onix
## 865 get onix
## 866 gets onix
## 867 give onix
## 868 given onix
## 869 gives onix
## 870 go onix
## 871 going onix
## 872 good onix
## 873 goods onix
## 874 got onix
## 875 great onix
## 876 greater onix
## 877 greatest onix
## 878 group onix
## 879 grouped onix
## 880 grouping onix
## 881 groups onix
## 882 had onix
## 883 has onix
## 884 have onix
## 885 having onix
## 886 he onix
## 887 her onix
## 888 here onix
## 889 herself onix
## 890 high onix
## 891 high onix
## 892 high onix
## 893 higher onix
## 894 highest onix
## 895 him onix
## 896 himself onix
## 897 his onix
## 898 how onix
## 899 however onix
## 900 i onix
## 901 if onix
## 902 important onix
## 903 in onix
## 904 interest onix
## 905 interested onix
## 906 interesting onix
## 907 interests onix
## 908 into onix
## 909 is onix
## 910 it onix
## 911 its onix
## 912 itself onix
## 913 just onix
## 914 keep onix
## 915 keeps onix
## 916 kind onix
## 917 knew onix
## 918 know onix
## 919 known onix
## 920 knows onix
## 921 large onix
## 922 largely onix
## 923 last onix
## 924 later onix
## 925 latest onix
## 926 least onix
## 927 less onix
## 928 let onix
## 929 lets onix
## 930 like onix
## 931 likely onix
## 932 long onix
## 933 longer onix
## 934 longest onix
## 935 made onix
## 936 make onix
## 937 making onix
## 938 man onix
## 939 many onix
## 940 may onix
## 941 me onix
## 942 member onix
## 943 members onix
## 944 men onix
## 945 might onix
## 946 more onix
## 947 most onix
## 948 mostly onix
## 949 mr onix
## 950 mrs onix
## 951 much onix
## 952 must onix
## 953 my onix
## 954 myself onix
## 955 necessary onix
## 956 need onix
## 957 needed onix
## 958 needing onix
## 959 needs onix
## 960 never onix
## 961 new onix
## 962 new onix
## 963 newer onix
## 964 newest onix
## 965 next onix
## 966 no onix
## 967 nobody onix
## 968 non onix
## 969 noone onix
## 970 not onix
## 971 nothing onix
## 972 now onix
## 973 nowhere onix
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## 975 numbers onix
## 976 of onix
## 977 off onix
## 978 often onix
## 979 old onix
## 980 older onix
## 981 oldest onix
## 982 on onix
## 983 once onix
## 984 one onix
## 985 only onix
## 986 open onix
## 987 opened onix
## 988 opening onix
## 989 opens onix
## 990 or onix
## 991 order onix
## 992 ordered onix
## 993 ordering onix
## 994 orders onix
## 995 other onix
## 996 others onix
## 997 our onix
## 998 out onix
## 999 over onix
## 1000 part onix
## 1001 parted onix
## 1002 parting onix
## 1003 parts onix
## 1004 per onix
## 1005 perhaps onix
## 1006 place onix
## 1007 places onix
## 1008 point onix
## 1009 pointed onix
## 1010 pointing onix
## 1011 points onix
## 1012 possible onix
## 1013 present onix
## 1014 presented onix
## 1015 presenting onix
## 1016 presents onix
## 1017 problem onix
## 1018 problems onix
## 1019 put onix
## 1020 puts onix
## 1021 quite onix
## 1022 rather onix
## 1023 really onix
## 1024 right onix
## 1025 right onix
## 1026 room onix
## 1027 rooms onix
## 1028 said onix
## 1029 same onix
## 1030 saw onix
## 1031 say onix
## 1032 says onix
## 1033 second onix
## 1034 seconds onix
## 1035 see onix
## 1036 seem onix
## 1037 seemed onix
## 1038 seeming onix
## 1039 seems onix
## 1040 sees onix
## 1041 several onix
## 1042 shall onix
## 1043 she onix
## 1044 should onix
## 1045 show onix
## 1046 showed onix
## 1047 showing onix
## 1048 shows onix
## 1049 side onix
## 1050 sides onix
## 1051 since onix
## 1052 small onix
## 1053 smaller onix
## 1054 smallest onix
## 1055 some onix
## 1056 somebody onix
## 1057 someone onix
## 1058 something onix
## 1059 somewhere onix
## 1060 state onix
## 1061 states onix
## 1062 still onix
## 1063 still onix
## 1064 such onix
## 1065 sure onix
## 1066 take onix
## 1067 taken onix
## 1068 than onix
## 1069 that onix
## 1070 the onix
## 1071 their onix
## 1072 them onix
## 1073 then onix
## 1074 there onix
## 1075 therefore onix
## 1076 these onix
## 1077 they onix
## 1078 thing onix
## 1079 things onix
## 1080 think onix
## 1081 thinks onix
## 1082 this onix
## 1083 those onix
## 1084 though onix
## 1085 thought onix
## 1086 thoughts onix
## 1087 three onix
## 1088 through onix
## 1089 thus onix
## 1090 to onix
## 1091 today onix
## 1092 together onix
## 1093 too onix
## 1094 took onix
## 1095 toward onix
## 1096 turn onix
## 1097 turned onix
## 1098 turning onix
## 1099 turns onix
## 1100 two onix
## 1101 under onix
## 1102 until onix
## 1103 up onix
## 1104 upon onix
## 1105 us onix
## 1106 use onix
## 1107 used onix
## 1108 uses onix
## 1109 very onix
## 1110 want onix
## 1111 wanted onix
## 1112 wanting onix
## 1113 wants onix
## 1114 was onix
## 1115 way onix
## 1116 ways onix
## 1117 we onix
## 1118 well onix
## 1119 wells onix
## 1120 went onix
## 1121 were onix
## 1122 what onix
## 1123 when onix
## 1124 where onix
## 1125 whether onix
## 1126 which onix
## 1127 while onix
## 1128 who onix
## 1129 whole onix
## 1130 whose onix
## 1131 why onix
## 1132 will onix
## 1133 with onix
## 1134 within onix
## 1135 without onix
## 1136 work onix
## 1137 worked onix
## 1138 working onix
## 1139 works onix
## 1140 would onix
## 1141 year onix
## 1142 years onix
## 1143 yet onix
## 1144 you onix
## 1145 young onix
## 1146 younger onix
## 1147 youngest onix
## 1148 your onix
## 1149 yours onixUsing tribble()
tribble(
~word, ~lexicon,
"roomba", "CUSTOM",
"2", "CUSTOM"
)## # A tibble: 2 x 2
## word lexicon
## <chr> <chr>
## 1 roomba CUSTOM
## 2 2 CUSTOMUsing bind_rows()
custom_stop_words <- tribble(
~word, ~lexicon,
"roomba", "CUSTOM",
"2", "CUSTOM"
)
stop_words2 <- stop_words %>%
bind_rows(custom_stop_words)
stop_words2## word lexicon
## 1 a SMART
## 2 a's SMART
## 3 able SMART
## 4 about SMART
## 5 above SMART
## 6 according SMART
## 7 accordingly SMART
## 8 across SMART
## 9 actually SMART
## 10 after SMART
## 11 afterwards SMART
## 12 again SMART
## 13 against SMART
## 14 ain't SMART
## 15 all SMART
## 16 allow SMART
## 17 allows SMART
## 18 almost SMART
## 19 alone SMART
## 20 along SMART
## 21 already SMART
## 22 also SMART
## 23 although SMART
## 24 always SMART
## 25 am SMART
## 26 among SMART
## 27 amongst SMART
## 28 an SMART
## 29 and SMART
## 30 another SMART
## 31 any SMART
## 32 anybody SMART
## 33 anyhow SMART
## 34 anyone SMART
## 35 anything SMART
## 36 anyway SMART
## 37 anyways SMART
## 38 anywhere SMART
## 39 apart SMART
## 40 appear SMART
## 41 appreciate SMART
## 42 appropriate SMART
## 43 are SMART
## 44 aren't SMART
## 45 around SMART
## 46 as SMART
## 47 aside SMART
## 48 ask SMART
## 49 asking SMART
## 50 associated SMART
## 51 at SMART
## 52 available SMART
## 53 away SMART
## 54 awfully SMART
## 55 b SMART
## 56 be SMART
## 57 became SMART
## 58 because SMART
## 59 become SMART
## 60 becomes SMART
## 61 becoming SMART
## 62 been SMART
## 63 before SMART
## 64 beforehand SMART
## 65 behind SMART
## 66 being SMART
## 67 believe SMART
## 68 below SMART
## 69 beside SMART
## 70 besides SMART
## 71 best SMART
## 72 better SMART
## 73 between SMART
## 74 beyond SMART
## 75 both SMART
## 76 brief SMART
## 77 but SMART
## 78 by SMART
## 79 c SMART
## 80 c'mon SMART
## 81 c's SMART
## 82 came SMART
## 83 can SMART
## 84 can't SMART
## 85 cannot SMART
## 86 cant SMART
## 87 cause SMART
## 88 causes SMART
## 89 certain SMART
## 90 certainly SMART
## 91 changes SMART
## 92 clearly SMART
## 93 co SMART
## 94 com SMART
## 95 come SMART
## 96 comes SMART
## 97 concerning SMART
## 98 consequently SMART
## 99 consider SMART
## 100 considering SMART
## 101 contain SMART
## 102 containing SMART
## 103 contains SMART
## 104 corresponding SMART
## 105 could SMART
## 106 couldn't SMART
## 107 course SMART
## 108 currently SMART
## 109 d SMART
## 110 definitely SMART
## 111 described SMART
## 112 despite SMART
## 113 did SMART
## 114 didn't SMART
## 115 different SMART
## 116 do SMART
## 117 does SMART
## 118 doesn't SMART
## 119 doing SMART
## 120 don't SMART
## 121 done SMART
## 122 down SMART
## 123 downwards SMART
## 124 during SMART
## 125 e SMART
## 126 each SMART
## 127 edu SMART
## 128 eg SMART
## 129 eight SMART
## 130 either SMART
## 131 else SMART
## 132 elsewhere SMART
## 133 enough SMART
## 134 entirely SMART
## 135 especially SMART
## 136 et SMART
## 137 etc SMART
## 138 even SMART
## 139 ever SMART
## 140 every SMART
## 141 everybody SMART
## 142 everyone SMART
## 143 everything SMART
## 144 everywhere SMART
## 145 ex SMART
## 146 exactly SMART
## 147 example SMART
## 148 except SMART
## 149 f SMART
## 150 far SMART
## 151 few SMART
## 152 fifth SMART
## 153 first SMART
## 154 five SMART
## 155 followed SMART
## 156 following SMART
## 157 follows SMART
## 158 for SMART
## 159 former SMART
## 160 formerly SMART
## 161 forth SMART
## 162 four SMART
## 163 from SMART
## 164 further SMART
## 165 furthermore SMART
## 166 g SMART
## 167 get SMART
## 168 gets SMART
## 169 getting SMART
## 170 given SMART
## 171 gives SMART
## 172 go SMART
## 173 goes SMART
## 174 going SMART
## 175 gone SMART
## 176 got SMART
## 177 gotten SMART
## 178 greetings SMART
## 179 h SMART
## 180 had SMART
## 181 hadn't SMART
## 182 happens SMART
## 183 hardly SMART
## 184 has SMART
## 185 hasn't SMART
## 186 have SMART
## 187 haven't SMART
## 188 having SMART
## 189 he SMART
## 190 he's SMART
## 191 hello SMART
## 192 help SMART
## 193 hence SMART
## 194 her SMART
## 195 here SMART
## 196 here's SMART
## 197 hereafter SMART
## 198 hereby SMART
## 199 herein SMART
## 200 hereupon SMART
## 201 hers SMART
## 202 herself SMART
## 203 hi SMART
## 204 him SMART
## 205 himself SMART
## 206 his SMART
## 207 hither SMART
## 208 hopefully SMART
## 209 how SMART
## 210 howbeit SMART
## 211 however SMART
## 212 i SMART
## 213 i'd SMART
## 214 i'll SMART
## 215 i'm SMART
## 216 i've SMART
## 217 ie SMART
## 218 if SMART
## 219 ignored SMART
## 220 immediate SMART
## 221 in SMART
## 222 inasmuch SMART
## 223 inc SMART
## 224 indeed SMART
## 225 indicate SMART
## 226 indicated SMART
## 227 indicates SMART
## 228 inner SMART
## 229 insofar SMART
## 230 instead SMART
## 231 into SMART
## 232 inward SMART
## 233 is SMART
## 234 isn't SMART
## 235 it SMART
## 236 it'd SMART
## 237 it'll SMART
## 238 it's SMART
## 239 its SMART
## 240 itself SMART
## 241 j SMART
## 242 just SMART
## 243 k SMART
## 244 keep SMART
## 245 keeps SMART
## 246 kept SMART
## 247 know SMART
## 248 knows SMART
## 249 known SMART
## 250 l SMART
## 251 last SMART
## 252 lately SMART
## 253 later SMART
## 254 latter SMART
## 255 latterly SMART
## 256 least SMART
## 257 less SMART
## 258 lest SMART
## 259 let SMART
## 260 let's SMART
## 261 like SMART
## 262 liked SMART
## 263 likely SMART
## 264 little SMART
## 265 look SMART
## 266 looking SMART
## 267 looks SMART
## 268 ltd SMART
## 269 m SMART
## 270 mainly SMART
## 271 many SMART
## 272 may SMART
## 273 maybe SMART
## 274 me SMART
## 275 mean SMART
## 276 meanwhile SMART
## 277 merely SMART
## 278 might SMART
## 279 more SMART
## 280 moreover SMART
## 281 most SMART
## 282 mostly SMART
## 283 much SMART
## 284 must SMART
## 285 my SMART
## 286 myself SMART
## 287 n SMART
## 288 name SMART
## 289 namely SMART
## 290 nd SMART
## 291 near SMART
## 292 nearly SMART
## 293 necessary SMART
## 294 need SMART
## 295 needs SMART
## 296 neither SMART
## 297 never SMART
## 298 nevertheless SMART
## 299 new SMART
## 300 next SMART
## 301 nine SMART
## 302 no SMART
## 303 nobody SMART
## 304 non SMART
## 305 none SMART
## 306 noone SMART
## 307 nor SMART
## 308 normally SMART
## 309 not SMART
## 310 nothing SMART
## 311 novel SMART
## 312 now SMART
## 313 nowhere SMART
## 314 o SMART
## 315 obviously SMART
## 316 of SMART
## 317 off SMART
## 318 often SMART
## 319 oh SMART
## 320 ok SMART
## 321 okay SMART
## 322 old SMART
## 323 on SMART
## 324 once SMART
## 325 one SMART
## 326 ones SMART
## 327 only SMART
## 328 onto SMART
## 329 or SMART
## 330 other SMART
## 331 others SMART
## 332 otherwise SMART
## 333 ought SMART
## 334 our SMART
## 335 ours SMART
## 336 ourselves SMART
## 337 out SMART
## 338 outside SMART
## 339 over SMART
## 340 overall SMART
## 341 own SMART
## 342 p SMART
## 343 particular SMART
## 344 particularly SMART
## 345 per SMART
## 346 perhaps SMART
## 347 placed SMART
## 348 please SMART
## 349 plus SMART
## 350 possible SMART
## 351 presumably SMART
## 352 probably SMART
## 353 provides SMART
## 354 q SMART
## 355 que SMART
## 356 quite SMART
## 357 qv SMART
## 358 r SMART
## 359 rather SMART
## 360 rd SMART
## 361 re SMART
## 362 really SMART
## 363 reasonably SMART
## 364 regarding SMART
## 365 regardless SMART
## 366 regards SMART
## 367 relatively SMART
## 368 respectively SMART
## 369 right SMART
## 370 s SMART
## 371 said SMART
## 372 same SMART
## 373 saw SMART
## 374 say SMART
## 375 saying SMART
## 376 says SMART
## 377 second SMART
## 378 secondly SMART
## 379 see SMART
## 380 seeing SMART
## 381 seem SMART
## 382 seemed SMART
## 383 seeming SMART
## 384 seems SMART
## 385 seen SMART
## 386 self SMART
## 387 selves SMART
## 388 sensible SMART
## 389 sent SMART
## 390 serious SMART
## 391 seriously SMART
## 392 seven SMART
## 393 several SMART
## 394 shall SMART
## 395 she SMART
## 396 should SMART
## 397 shouldn't SMART
## 398 since SMART
## 399 six SMART
## 400 so SMART
## 401 some SMART
## 402 somebody SMART
## 403 somehow SMART
## 404 someone SMART
## 405 something SMART
## 406 sometime SMART
## 407 sometimes SMART
## 408 somewhat SMART
## 409 somewhere SMART
## 410 soon SMART
## 411 sorry SMART
## 412 specified SMART
## 413 specify SMART
## 414 specifying SMART
## 415 still SMART
## 416 sub SMART
## 417 such SMART
## 418 sup SMART
## 419 sure SMART
## 420 t SMART
## 421 t's SMART
## 422 take SMART
## 423 taken SMART
## 424 tell SMART
## 425 tends SMART
## 426 th SMART
## 427 than SMART
## 428 thank SMART
## 429 thanks SMART
## 430 thanx SMART
## 431 that SMART
## 432 that's SMART
## 433 thats SMART
## 434 the SMART
## 435 their SMART
## 436 theirs SMART
## 437 them SMART
## 438 themselves SMART
## 439 then SMART
## 440 thence SMART
## 441 there SMART
## 442 there's SMART
## 443 thereafter SMART
## 444 thereby SMART
## 445 therefore SMART
## 446 therein SMART
## 447 theres SMART
## 448 thereupon SMART
## 449 these SMART
## 450 they SMART
## 451 they'd SMART
## 452 they'll SMART
## 453 they're SMART
## 454 they've SMART
## 455 think SMART
## 456 third SMART
## 457 this SMART
## 458 thorough SMART
## 459 thoroughly SMART
## 460 those SMART
## 461 though SMART
## 462 three SMART
## 463 through SMART
## 464 throughout SMART
## 465 thru SMART
## 466 thus SMART
## 467 to SMART
## 468 together SMART
## 469 too SMART
## 470 took SMART
## 471 toward SMART
## 472 towards SMART
## 473 tried SMART
## 474 tries SMART
## 475 truly SMART
## 476 try SMART
## 477 trying SMART
## 478 twice SMART
## 479 two SMART
## 480 u SMART
## 481 un SMART
## 482 under SMART
## 483 unfortunately SMART
## 484 unless SMART
## 485 unlikely SMART
## 486 until SMART
## 487 unto SMART
## 488 up SMART
## 489 upon SMART
## 490 us SMART
## 491 use SMART
## 492 used SMART
## 493 useful SMART
## 494 uses SMART
## 495 using SMART
## 496 usually SMART
## 497 uucp SMART
## 498 v SMART
## 499 value SMART
## 500 various SMART
## 501 very SMART
## 502 via SMART
## 503 viz SMART
## 504 vs SMART
## 505 w SMART
## 506 want SMART
## 507 wants SMART
## 508 was SMART
## 509 wasn't SMART
## 510 way SMART
## 511 we SMART
## 512 we'd SMART
## 513 we'll SMART
## 514 we're SMART
## 515 we've SMART
## 516 welcome SMART
## 517 well SMART
## 518 went SMART
## 519 were SMART
## 520 weren't SMART
## 521 what SMART
## 522 what's SMART
## 523 whatever SMART
## 524 when SMART
## 525 whence SMART
## 526 whenever SMART
## 527 where SMART
## 528 where's SMART
## 529 whereafter SMART
## 530 whereas SMART
## 531 whereby SMART
## 532 wherein SMART
## 533 whereupon SMART
## 534 wherever SMART
## 535 whether SMART
## 536 which SMART
## 537 while SMART
## 538 whither SMART
## 539 who SMART
## 540 who's SMART
## 541 whoever SMART
## 542 whole SMART
## 543 whom SMART
## 544 whose SMART
## 545 why SMART
## 546 will SMART
## 547 willing SMART
## 548 wish SMART
## 549 with SMART
## 550 within SMART
## 551 without SMART
## 552 won't SMART
## 553 wonder SMART
## 554 would SMART
## 555 would SMART
## 556 wouldn't SMART
## 557 x SMART
## 558 y SMART
## 559 yes SMART
## 560 yet SMART
## 561 you SMART
## 562 you'd SMART
## 563 you'll SMART
## 564 you're SMART
## 565 you've SMART
## 566 your SMART
## 567 yours SMART
## 568 yourself SMART
## 569 yourselves SMART
## 570 z SMART
## 571 zero SMART
## 572 i snowball
## 573 me snowball
## 574 my snowball
## 575 myself snowball
## 576 we snowball
## 577 our snowball
## 578 ours snowball
## 579 ourselves snowball
## 580 you snowball
## 581 your snowball
## 582 yours snowball
## 583 yourself snowball
## 584 yourselves snowball
## 585 he snowball
## 586 him snowball
## 587 his snowball
## 588 himself snowball
## 589 she snowball
## 590 her snowball
## 591 hers snowball
## 592 herself snowball
## 593 it snowball
## 594 its snowball
## 595 itself snowball
## 596 they snowball
## 597 them snowball
## 598 their snowball
## 599 theirs snowball
## 600 themselves snowball
## 601 what snowball
## 602 which snowball
## 603 who snowball
## 604 whom snowball
## 605 this snowball
## 606 that snowball
## 607 these snowball
## 608 those snowball
## 609 am snowball
## 610 is snowball
## 611 are snowball
## 612 was snowball
## 613 were snowball
## 614 be snowball
## 615 been snowball
## 616 being snowball
## 617 have snowball
## 618 has snowball
## 619 had snowball
## 620 having snowball
## 621 do snowball
## 622 does snowball
## 623 did snowball
## 624 doing snowball
## 625 would snowball
## 626 should snowball
## 627 could snowball
## 628 ought snowball
## 629 i'm snowball
## 630 you're snowball
## 631 he's snowball
## 632 she's snowball
## 633 it's snowball
## 634 we're snowball
## 635 they're snowball
## 636 i've snowball
## 637 you've snowball
## 638 we've snowball
## 639 they've snowball
## 640 i'd snowball
## 641 you'd snowball
## 642 he'd snowball
## 643 she'd snowball
## 644 we'd snowball
## 645 they'd snowball
## 646 i'll snowball
## 647 you'll snowball
## 648 he'll snowball
## 649 she'll snowball
## 650 we'll snowball
## 651 they'll snowball
## 652 isn't snowball
## 653 aren't snowball
## 654 wasn't snowball
## 655 weren't snowball
## 656 hasn't snowball
## 657 haven't snowball
## 658 hadn't snowball
## 659 doesn't snowball
## 660 don't snowball
## 661 didn't snowball
## 662 won't snowball
## 663 wouldn't snowball
## 664 shan't snowball
## 665 shouldn't snowball
## 666 can't snowball
## 667 cannot snowball
## 668 couldn't snowball
## 669 mustn't snowball
## 670 let's snowball
## 671 that's snowball
## 672 who's snowball
## 673 what's snowball
## 674 here's snowball
## 675 there's snowball
## 676 when's snowball
## 677 where's snowball
## 678 why's snowball
## 679 how's snowball
## 680 a snowball
## 681 an snowball
## 682 the snowball
## 683 and snowball
## 684 but snowball
## 685 if snowball
## 686 or snowball
## 687 because snowball
## 688 as snowball
## 689 until snowball
## 690 while snowball
## 691 of snowball
## 692 at snowball
## 693 by snowball
## 694 for snowball
## 695 with snowball
## 696 about snowball
## 697 against snowball
## 698 between snowball
## 699 into snowball
## 700 through snowball
## 701 during snowball
## 702 before snowball
## 703 after snowball
## 704 above snowball
## 705 below snowball
## 706 to snowball
## 707 from snowball
## 708 up snowball
## 709 down snowball
## 710 in snowball
## 711 out snowball
## 712 on snowball
## 713 off snowball
## 714 over snowball
## 715 under snowball
## 716 again snowball
## 717 further snowball
## 718 then snowball
## 719 once snowball
## 720 here snowball
## 721 there snowball
## 722 when snowball
## 723 where snowball
## 724 why snowball
## 725 how snowball
## 726 all snowball
## 727 any snowball
## 728 both snowball
## 729 each snowball
## 730 few snowball
## 731 more snowball
## 732 most snowball
## 733 other snowball
## 734 some snowball
## 735 such snowball
## 736 no snowball
## 737 nor snowball
## 738 not snowball
## 739 only snowball
## 740 own snowball
## 741 same snowball
## 742 so snowball
## 743 than snowball
## 744 too snowball
## 745 very snowball
## 746 a onix
## 747 about onix
## 748 above onix
## 749 across onix
## 750 after onix
## 751 again onix
## 752 against onix
## 753 all onix
## 754 almost onix
## 755 alone onix
## 756 along onix
## 757 already onix
## 758 also onix
## 759 although onix
## 760 always onix
## 761 among onix
## 762 an onix
## 763 and onix
## 764 another onix
## 765 any onix
## 766 anybody onix
## 767 anyone onix
## 768 anything onix
## 769 anywhere onix
## 770 are onix
## 771 area onix
## 772 areas onix
## 773 around onix
## 774 as onix
## 775 ask onix
## 776 asked onix
## 777 asking onix
## 778 asks onix
## 779 at onix
## 780 away onix
## 781 back onix
## 782 backed onix
## 783 backing onix
## 784 backs onix
## 785 be onix
## 786 became onix
## 787 because onix
## 788 become onix
## 789 becomes onix
## 790 been onix
## 791 before onix
## 792 began onix
## 793 behind onix
## 794 being onix
## 795 beings onix
## 796 best onix
## 797 better onix
## 798 between onix
## 799 big onix
## 800 both onix
## 801 but onix
## 802 by onix
## 803 came onix
## 804 can onix
## 805 cannot onix
## 806 case onix
## 807 cases onix
## 808 certain onix
## 809 certainly onix
## 810 clear onix
## 811 clearly onix
## 812 come onix
## 813 could onix
## 814 did onix
## 815 differ onix
## 816 different onix
## 817 differently onix
## 818 do onix
## 819 does onix
## 820 done onix
## 821 down onix
## 822 down onix
## 823 downed onix
## 824 downing onix
## 825 downs onix
## 826 during onix
## 827 each onix
## 828 early onix
## 829 either onix
## 830 end onix
## 831 ended onix
## 832 ending onix
## 833 ends onix
## 834 enough onix
## 835 even onix
## 836 evenly onix
## 837 ever onix
## 838 every onix
## 839 everybody onix
## 840 everyone onix
## 841 everything onix
## 842 everywhere onix
## 843 face onix
## 844 faces onix
## 845 fact onix
## 846 facts onix
## 847 far onix
## 848 felt onix
## 849 few onix
## 850 find onix
## 851 finds onix
## 852 first onix
## 853 for onix
## 854 four onix
## 855 from onix
## 856 full onix
## 857 fully onix
## 858 further onix
## 859 furthered onix
## 860 furthering onix
## 861 furthers onix
## 862 gave onix
## 863 general onix
## 864 generally onix
## 865 get onix
## 866 gets onix
## 867 give onix
## 868 given onix
## 869 gives onix
## 870 go onix
## 871 going onix
## 872 good onix
## 873 goods onix
## 874 got onix
## 875 great onix
## 876 greater onix
## 877 greatest onix
## 878 group onix
## 879 grouped onix
## 880 grouping onix
## 881 groups onix
## 882 had onix
## 883 has onix
## 884 have onix
## 885 having onix
## 886 he onix
## 887 her onix
## 888 here onix
## 889 herself onix
## 890 high onix
## 891 high onix
## 892 high onix
## 893 higher onix
## 894 highest onix
## 895 him onix
## 896 himself onix
## 897 his onix
## 898 how onix
## 899 however onix
## 900 i onix
## 901 if onix
## 902 important onix
## 903 in onix
## 904 interest onix
## 905 interested onix
## 906 interesting onix
## 907 interests onix
## 908 into onix
## 909 is onix
## 910 it onix
## 911 its onix
## 912 itself onix
## 913 just onix
## 914 keep onix
## 915 keeps onix
## 916 kind onix
## 917 knew onix
## 918 know onix
## 919 known onix
## 920 knows onix
## 921 large onix
## 922 largely onix
## 923 last onix
## 924 later onix
## 925 latest onix
## 926 least onix
## 927 less onix
## 928 let onix
## 929 lets onix
## 930 like onix
## 931 likely onix
## 932 long onix
## 933 longer onix
## 934 longest onix
## 935 made onix
## 936 make onix
## 937 making onix
## 938 man onix
## 939 many onix
## 940 may onix
## 941 me onix
## 942 member onix
## 943 members onix
## 944 men onix
## 945 might onix
## 946 more onix
## 947 most onix
## 948 mostly onix
## 949 mr onix
## 950 mrs onix
## 951 much onix
## 952 must onix
## 953 my onix
## 954 myself onix
## 955 necessary onix
## 956 need onix
## 957 needed onix
## 958 needing onix
## 959 needs onix
## 960 never onix
## 961 new onix
## 962 new onix
## 963 newer onix
## 964 newest onix
## 965 next onix
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## 967 nobody onix
## 968 non onix
## 969 noone onix
## 970 not onix
## 971 nothing onix
## 972 now onix
## 973 nowhere onix
## 974 number onix
## 975 numbers onix
## 976 of onix
## 977 off onix
## 978 often onix
## 979 old onix
## 980 older onix
## 981 oldest onix
## 982 on onix
## 983 once onix
## 984 one onix
## 985 only onix
## 986 open onix
## 987 opened onix
## 988 opening onix
## 989 opens onix
## 990 or onix
## 991 order onix
## 992 ordered onix
## 993 ordering onix
## 994 orders onix
## 995 other onix
## 996 others onix
## 997 our onix
## 998 out onix
## 999 over onix
## 1000 part onix
## 1001 parted onix
## 1002 parting onix
## 1003 parts onix
## 1004 per onix
## 1005 perhaps onix
## 1006 place onix
## 1007 places onix
## 1008 point onix
## 1009 pointed onix
## 1010 pointing onix
## 1011 points onix
## 1012 possible onix
## 1013 present onix
## 1014 presented onix
## 1015 presenting onix
## 1016 presents onix
## 1017 problem onix
## 1018 problems onix
## 1019 put onix
## 1020 puts onix
## 1021 quite onix
## 1022 rather onix
## 1023 really onix
## 1024 right onix
## 1025 right onix
## 1026 room onix
## 1027 rooms onix
## 1028 said onix
## 1029 same onix
## 1030 saw onix
## 1031 say onix
## 1032 says onix
## 1033 second onix
## 1034 seconds onix
## 1035 see onix
## 1036 seem onix
## 1037 seemed onix
## 1038 seeming onix
## 1039 seems onix
## 1040 sees onix
## 1041 several onix
## 1042 shall onix
## 1043 she onix
## 1044 should onix
## 1045 show onix
## 1046 showed onix
## 1047 showing onix
## 1048 shows onix
## 1049 side onix
## 1050 sides onix
## 1051 since onix
## 1052 small onix
## 1053 smaller onix
## 1054 smallest onix
## 1055 some onix
## 1056 somebody onix
## 1057 someone onix
## 1058 something onix
## 1059 somewhere onix
## 1060 state onix
## 1061 states onix
## 1062 still onix
## 1063 still onix
## 1064 such onix
## 1065 sure onix
## 1066 take onix
## 1067 taken onix
## 1068 than onix
## 1069 that onix
## 1070 the onix
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## 1073 then onix
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## 1076 these onix
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## 1078 thing onix
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## 1080 think onix
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## 1082 this onix
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## 1087 three onix
## 1088 through onix
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## 1090 to onix
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## 1092 together onix
## 1093 too onix
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## 1096 turn onix
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## 1099 turns onix
## 1100 two onix
## 1101 under onix
## 1102 until onix
## 1103 up onix
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## 1106 use onix
## 1107 used onix
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## 1110 want onix
## 1111 wanted onix
## 1112 wanting onix
## 1113 wants onix
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## 1117 we onix
## 1118 well onix
## 1119 wells onix
## 1120 went onix
## 1121 were onix
## 1122 what onix
## 1123 when onix
## 1124 where onix
## 1125 whether onix
## 1126 which onix
## 1127 while onix
## 1128 who onix
## 1129 whole onix
## 1130 whose onix
## 1131 why onix
## 1132 will onix
## 1133 with onix
## 1134 within onix
## 1135 without onix
## 1136 work onix
## 1137 worked onix
## 1138 working onix
## 1139 works onix
## 1140 would onix
## 1141 year onix
## 1142 years onix
## 1143 yet onix
## 1144 you onix
## 1145 young onix
## 1146 younger onix
## 1147 youngest onix
## 1148 your onix
## 1149 yours onix
## 1150 roomba CUSTOM
## 1151 2 CUSTOMRemoving stop words again
tidy_review <- review_data %>%
mutate(id = row_number()) %>%
select(id, date, product, stars, review) %>%
unnest_tokens(word, review) %>%
anti_join(stop_words2)## Joining, by = "word"tidy_review %>%
filter(word == "roomba")## # A tibble: 0 x 5
## # … with 5 variables: id <int>, date <chr>, product <chr>, stars <dbl>,
## # word <chr>Factors
Using fct_reorder()
word_counts <- tidy_review %>%
count(word) %>%
filter(n > 300) %>%
mutate(word2 = fct_reorder(word, n))
word_counts## # A tibble: 23 x 3
## word n word2
## <chr> <int> <fct>
## 1 880 525 880
## 2 bin 428 bin
## 3 carpet 368 carpet
## 4 clean 1204 clean
## 5 cleaning 809 cleaning
## 6 day 578 day
## 7 dirt 384 dirt
## 8 dog 407 dog
## 9 dust 543 dust
## 10 floor 561 floor
## # … with 13 more rowsArranging the bar plot
ggplot(word_counts, aes(x = word2, y = n)) +
geom_col() +
coord_flip() +
ggtitle("Review Word Counts")
Adding custom stop words
custom_stop_words <- tribble(
# Column names should match stop_words
~word, ~lexicon,
# Add http, win, and t.co as custom stop words
"http", "CUSTOM",
"win", "CUSTOM",
"t.co", "CUSTOM"
)
# Bind the custom stop words to stop_words
stop_words2 <- stop_words %>%
bind_rows(custom_stop_words)Visualizing word count using factors
word_counts <- tidy_twitter %>%
filter(complaint_label == "Non-Complaint") %>%
count(word) %>%
# Keep terms that occur more than 100 times
filter(n > 100) %>%
# Reorder word as an ordered factor by word counts
mutate(word2 = fct_reorder(word, n))
# Plot the new word column with type factor
ggplot(word_counts, aes(x = word2, y = n)) +
geom_col() +
coord_flip() +
ggtitle("Non-Complaint Word Counts")
Faceting word count plots
[Video]
Counting by product
tidy_review %>%
count(word, product) %>%
arrange(desc(n))## # A tibble: 12,719 x 3
## word product n
## <chr> <chr> <int>
## 1 clean iRobot Roomba 880 for Pets and Allergies 815
## 2 vacuum iRobot Roomba 880 for Pets and Allergies 678
## 3 hair iRobot Roomba 880 for Pets and Allergies 595
## 4 cleaning iRobot Roomba 880 for Pets and Allergies 560
## 5 880 iRobot Roomba 880 for Pets and Allergies 518
## 6 house iRobot Roomba 880 for Pets and Allergies 494
## 7 time iRobot Roomba 880 for Pets and Allergies 494
## 8 floors iRobot Roomba 880 for Pets and Allergies 405
## 9 love iRobot Roomba 880 for Pets and Allergies 403
## 10 dust iRobot Roomba 880 for Pets and Allergies 399
## # … with 12,709 more rowsUsing top_n()
tidy_review %>%
count(word, product) %>%
group_by(product) %>%
top_n(10, n)## # A tibble: 20 x 3
## # Groups: product [2]
## word product n
## <chr> <chr> <int>
## 1 880 iRobot Roomba 880 for Pets and Allergies 518
## 2 clean iRobot Roomba 650 for Pets 389
## 3 clean iRobot Roomba 880 for Pets and Allergies 815
## 4 cleaning iRobot Roomba 650 for Pets 249
## 5 cleaning iRobot Roomba 880 for Pets and Allergies 560
## 6 day iRobot Roomba 650 for Pets 209
## 7 dust iRobot Roomba 880 for Pets and Allergies 399
## 8 floor iRobot Roomba 650 for Pets 207
## 9 floors iRobot Roomba 650 for Pets 252
## 10 floors iRobot Roomba 880 for Pets and Allergies 405
## 11 hair iRobot Roomba 650 for Pets 305
## 12 hair iRobot Roomba 880 for Pets and Allergies 595
## 13 house iRobot Roomba 650 for Pets 251
## 14 house iRobot Roomba 880 for Pets and Allergies 494
## 15 love iRobot Roomba 880 for Pets and Allergies 403
## 16 run iRobot Roomba 650 for Pets 180
## 17 time iRobot Roomba 650 for Pets 301
## 18 time iRobot Roomba 880 for Pets and Allergies 494
## 19 vacuum iRobot Roomba 650 for Pets 311
## 20 vacuum iRobot Roomba 880 for Pets and Allergies 678Using ungroup()
tidy_review %>%
count(word, product) %>%
group_by(product) %>%
top_n(10, n) %>%
ungroup()## # A tibble: 20 x 3
## word product n
## <chr> <chr> <int>
## 1 880 iRobot Roomba 880 for Pets and Allergies 518
## 2 clean iRobot Roomba 650 for Pets 389
## 3 clean iRobot Roomba 880 for Pets and Allergies 815
## 4 cleaning iRobot Roomba 650 for Pets 249
## 5 cleaning iRobot Roomba 880 for Pets and Allergies 560
## 6 day iRobot Roomba 650 for Pets 209
## 7 dust iRobot Roomba 880 for Pets and Allergies 399
## 8 floor iRobot Roomba 650 for Pets 207
## 9 floors iRobot Roomba 650 for Pets 252
## 10 floors iRobot Roomba 880 for Pets and Allergies 405
## 11 hair iRobot Roomba 650 for Pets 305
## 12 hair iRobot Roomba 880 for Pets and Allergies 595
## 13 house iRobot Roomba 650 for Pets 251
## 14 house iRobot Roomba 880 for Pets and Allergies 494
## 15 love iRobot Roomba 880 for Pets and Allergies 403
## 16 run iRobot Roomba 650 for Pets 180
## 17 time iRobot Roomba 650 for Pets 301
## 18 time iRobot Roomba 880 for Pets and Allergies 494
## 19 vacuum iRobot Roomba 650 for Pets 311
## 20 vacuum iRobot Roomba 880 for Pets and Allergies 678Using fct_reorder()
tidy_review %>%
count(word, product) %>%
group_by(product) %>%
top_n(10, n) %>%
ungroup() %>%
mutate(word2 = fct_reorder(word, n))## # A tibble: 20 x 4
## word product n word2
## <chr> <chr> <int> <fct>
## 1 880 iRobot Roomba 880 for Pets and Allergies 518 880
## 2 clean iRobot Roomba 650 for Pets 389 clean
## 3 clean iRobot Roomba 880 for Pets and Allergies 815 clean
## 4 cleaning iRobot Roomba 650 for Pets 249 cleaning
## 5 cleaning iRobot Roomba 880 for Pets and Allergies 560 cleaning
## 6 day iRobot Roomba 650 for Pets 209 day
## 7 dust iRobot Roomba 880 for Pets and Allergies 399 dust
## 8 floor iRobot Roomba 650 for Pets 207 floor
## 9 floors iRobot Roomba 650 for Pets 252 floors
## 10 floors iRobot Roomba 880 for Pets and Allergies 405 floors
## 11 hair iRobot Roomba 650 for Pets 305 hair
## 12 hair iRobot Roomba 880 for Pets and Allergies 595 hair
## 13 house iRobot Roomba 650 for Pets 251 house
## 14 house iRobot Roomba 880 for Pets and Allergies 494 house
## 15 love iRobot Roomba 880 for Pets and Allergies 403 love
## 16 run iRobot Roomba 650 for Pets 180 run
## 17 time iRobot Roomba 650 for Pets 301 time
## 18 time iRobot Roomba 880 for Pets and Allergies 494 time
## 19 vacuum iRobot Roomba 650 for Pets 311 vacuum
## 20 vacuum iRobot Roomba 880 for Pets and Allergies 678 vacuumUsing facet_wrap()
# ggplot(word_counts, aes(x = word2, y = n, fill = product)) +
# geom_col(show.legend = FALSE) +
# facet_wrap(~ product, scales = "free_y") +
# coord_flip() +
# ggtitle("Review Word Counts")Counting by product and reordering
word_counts <- tidy_twitter %>%
# Count words by whether or not its a complaint
count(word, complaint_label) %>%
# Group by whether or not its a complaint
group_by(complaint_label) %>%
# Keep the top 20 words
top_n(20, n) %>%
# Ungroup before reordering word as a factor by the count
ungroup() %>%
mutate(word2 = fct_reorder(word, n))Visualizing word count with facets
# Include a color aesthetic tied to whether or not its a complaint
ggplot(word_counts, aes(x = word2, y = n, fill = complaint_label)) +
# Don't include the lengend for the column plot
geom_col(show.legend = FALSE) +
# Facet by whether or not its a complaint and make the y-axis free
facet_wrap(~ complaint_label, scales = "free_y") +
# Flip the coordinates and add a title: "Twitter Word Counts"
coord_flip() +
ggtitle("Twitter Word Counts")
Plotting word clouds
Using wordcloud()
library(wordcloud)## Loading required package: RColorBrewerword_counts <- tidy_review %>%
count(word)
wordcloud(
words = word_counts$word,
freq = word_counts$n,
max.words = 30
)
Fixed size and random start points
wordcloud(
words = word_counts$word,
freq = word_counts$n,
max.words = 30
)
Number of words in cloud
wordcloud(
words = word_counts$word,
freq = word_counts$n,
max.words = 70
)
Using colors
wordcloud(
words = word_counts$word,
freq = word_counts$n,
max.words = 30,
colors = "blue"
)
Creating a word cloud
# Load the wordcloud package
library(wordcloud)
# Compute word counts and assign to word_counts
word_counts <- tidy_twitter %>%
count(word)
wordcloud(
# Assign the word column to words
words = word_counts$word,
# Assign the count column to freq
freq = word_counts$n,
max.words = 30
)
Adding a splash of color
# Compute complaint word counts and assign to word_counts
word_counts <- tidy_twitter %>%
filter(complaint_label == "Complaint") %>%
count(word)
# Create a complaint word cloud of the top 50 terms, colored red
wordcloud(
words = word_counts$word,
freq = word_counts$n,
max.words = 50,
colors = "red"
)
Ch. 3 - Sentiment Analysis
Sentiment dictionaries
[Video]
Bing dictionary
get_sentiments("bing")## # A tibble: 6,786 x 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 rowsget_sentiments("bing") %>%
count(sentiment)## # A tibble: 2 x 2
## sentiment n
## <chr> <int>
## 1 negative 4781
## 2 positive 2005Afinn dictionary
get_sentiments("afinn")## # A tibble: 2,477 x 2
## word value
## <chr> <dbl>
## 1 abandon -2
## 2 abandoned -2
## 3 abandons -2
## 4 abducted -2
## 5 abduction -2
## 6 abductions -2
## 7 abhor -3
## 8 abhorred -3
## 9 abhorrent -3
## 10 abhors -3
## # … with 2,467 more rowsget_sentiments("afinn") %>%
summarize(
min = min(value),
max = max(value)
)## # A tibble: 1 x 2
## min max
## <dbl> <dbl>
## 1 -5 5Loughran dictionary
sentiment_counts <- get_sentiments("loughran") %>%
count(sentiment) %>%
mutate(sentiment2 = fct_reorder(sentiment, n))
ggplot(sentiment_counts, aes(x = sentiment2, y = n)) +
geom_col() +
coord_flip() +
labs(
title = "Sentiment Counts in Loughran",
x = "Counts",
y = "Sentiment"
)
Counting the NRC sentiments
# Load the tidyverse and tidytext packages
library(tidyverse)
library(tidytext)
# Count the number of words associated with each sentiment in nrc
get_sentiments("nrc") %>%
count(sentiment) %>%
# Arrange the counts in descending order
arrange(desc(n))## # A tibble: 10 x 2
## sentiment n
## <chr> <int>
## 1 negative 3324
## 2 positive 2312
## 3 fear 1476
## 4 anger 1247
## 5 trust 1231
## 6 sadness 1191
## 7 disgust 1058
## 8 anticipation 839
## 9 joy 689
## 10 surprise 534Visualizing the NRC sentiments
# Pull in the nrc dictionary, count the sentiments and reorder them by count
sentiment_counts <- get_sentiments("nrc") %>%
count(sentiment) %>%
mutate(sentiment2 = fct_reorder(sentiment, n))
# Visualize sentiment_counts using the new sentiment factor column
ggplot(sentiment_counts, aes(x = sentiment2, y = n)) +
geom_col() +
coord_flip() +
# Change the title to "Sentiment Counts in NRC", x-axis to "Sentiment", and y-axis to "Counts"
labs(
title = "Sentiment Counts in NRC",
x = "Sentiment",
y = "Counts"
)
Appending dictionaries
[Video]
Using inner_join()
tidy_review %>% inner_join(get_sentiments("loughran"))## Joining, by = "word"## # A tibble: 3,960 x 6
## id date product stars word sentiment
## <int> <chr> <chr> <dbl> <chr> <chr>
## 1 5 12/22/15 iRobot Roomba 650 for Pets 5 slow negative
## 2 5 12/22/15 iRobot Roomba 650 for Pets 5 easily positive
## 3 5 12/22/15 iRobot Roomba 650 for Pets 5 random uncertainty
## 4 5 12/22/15 iRobot Roomba 650 for Pets 5 easy positive
## 5 5 12/22/15 iRobot Roomba 650 for Pets 5 easy positive
## 6 5 12/22/15 iRobot Roomba 650 for Pets 5 easy positive
## 7 6 12/27/15 iRobot Roomba 650 for Pets 5 invention positive
## 8 7 8/17/15 iRobot Roomba 650 for Pets 1 damage negative
## 9 7 8/17/15 iRobot Roomba 650 for Pets 1 damage negative
## 10 7 8/17/15 iRobot Roomba 650 for Pets 1 justice litigious
## # … with 3,950 more rowsCounting sentiment
sentiment_review <- tidy_review %>% inner_join(get_sentiments("loughran"))## Joining, by = "word"sentiment_review %>%
count(sentiment)## # A tibble: 6 x 2
## sentiment n
## <chr> <int>
## 1 constraining 170
## 2 litigious 53
## 3 negative 1795
## 4 positive 1568
## 5 superfluous 1
## 6 uncertainty 373sentiment_review %>%
count(word, sentiment) %>%
arrange(desc(n))## # A tibble: 598 x 3
## word sentiment n
## <chr> <chr> <int>
## 1 easy positive 297
## 2 happy positive 107
## 3 easier positive 97
## 4 easily positive 92
## 5 perfect positive 87
## 6 random uncertainty 81
## 7 impressed positive 77
## 8 excellent positive 58
## 9 trouble negative 58
## 10 fantastic positive 56
## # … with 588 more rowsVisualizing sentiment
sentiment_review2 <- sentiment_review %>%
filter(sentiment %in% c("positive", "negative"))
word_counts <- sentiment_review2 %>%
count(word, sentiment) %>%
group_by(sentiment) %>%
top_n(10, n) %>%
ungroup() %>%
mutate(
word2 = fct_reorder(word, n)
)
ggplot(word_counts, aes(x = word2, y = n, fill = sentiment)) +
geom_col(show.legend = FALSE) +
facet_wrap(~ sentiment, scales = "free") +
coord_flip() +
labs(
title = "Sentiment Word Counts",
x = "Words"
)
Counting sentiment
# Join tidy_twitter and the NRC sentiment dictionary
sentiment_twitter <- tidy_twitter %>%
inner_join(get_sentiments("nrc"))## Joining, by = "word"# Count the sentiments in tidy_twitter
sentiment_twitter %>%
count(sentiment) %>%
# Arrange the sentiment counts in descending order
arrange(desc(n))## # A tibble: 10 x 2
## sentiment n
## <chr> <int>
## 1 positive 4415
## 2 trust 2873
## 3 negative 2747
## 4 anticipation 2124
## 5 joy 1480
## 6 sadness 1426
## 7 fear 1357
## 8 anger 1156
## 9 surprise 992
## 10 disgust 880Visualizing sentiment
word_counts <- tidy_twitter %>%
# Append the NRC dictionary and filter for positive, fear, and trust
inner_join(get_sentiments("nrc")) %>%
filter(sentiment %in% c("positive", "fear", "trust")) %>%
# Count by word and sentiment and keep the top 10 of each
count(word, sentiment) %>%
group_by(sentiment) %>%
top_n(10, n) %>%
ungroup() %>%
# Create a factor called word2 that has each word ordered by the count
mutate(
word2 = fct_reorder(word, n)
)## Joining, by = "word"# Create a bar plot out of the word counts colored by sentiment
ggplot(word_counts, aes(x = word2, y = n, fill = sentiment)) +
geom_col(show.legend = FALSE) +
# Create a separate facet for each sentiment with free axes
facet_wrap(~ sentiment, scales = "free") +
coord_flip() +
# Title the plot "Sentiment Word Counts" with "Words" for the x-axis
labs(
title = "Sentiment Word Counts",
x = "Words"
)
Improving sentiment analysis
[Video]
Count sentiment by rating
tidy_review %>%
inner_join(get_sentiments("bing")) %>%
count(stars, sentiment)## Joining, by = "word"## # A tibble: 10 x 3
## stars sentiment n
## <dbl> <chr> <int>
## 1 1 negative 381
## 2 1 positive 241
## 3 2 negative 384
## 4 2 positive 247
## 5 3 negative 485
## 6 3 positive 432
## 7 4 negative 984
## 8 4 positive 973
## 9 5 negative 3705
## 10 5 positive 5083Using spread()
tidy_review %>%
inner_join(get_sentiments("bing")) %>%
count(stars, sentiment) %>%
spread(sentiment, n)## Joining, by = "word"## # A tibble: 5 x 3
## stars negative positive
## <dbl> <int> <int>
## 1 1 381 241
## 2 2 384 247
## 3 3 485 432
## 4 4 984 973
## 5 5 3705 5083Computing overall sentiment
tidy_review %>%
inner_join(get_sentiments("bing")) %>%
count(stars, sentiment) %>%
spread(sentiment, n) %>%
mutate(overall_sentiment = positive - negative)## Joining, by = "word"## # A tibble: 5 x 4
## stars negative positive overall_sentiment
## <dbl> <int> <int> <int>
## 1 1 381 241 -140
## 2 2 384 247 -137
## 3 3 485 432 -53
## 4 4 984 973 -11
## 5 5 3705 5083 1378Visualize sentiment by rating
sentiment_stars <- tidy_review %>%
inner_join(get_sentiments("bing")) %>%
count(stars, sentiment) %>%
spread(sentiment, n) %>%
mutate(
overall_sentiment = positive - negative,
stars = fct_reorder(as.factor(stars), overall_sentiment)
)## Joining, by = "word"ggplot(sentiment_stars, aes(x = stars, y = overall_sentiment, fill = as.factor(stars))) +
geom_col(show.legend = FALSE) +
coord_flip() +
labs(
title = "Overall Sentiment by Stars",
subtitle = "Reviews for Robotic Vacuums",
x = "Stars",
y = "Overall Sentiment"
)
Practicing reshaping data
tidy_twitter %>%
# Append the NRC sentiment dictionary
inner_join(get_sentiments("nrc")) %>%
# Count by complaint label and sentiment
count(complaint_label, sentiment) %>%
# Spread the sentiment and count columns
spread(sentiment, n)## Joining, by = "word"## # A tibble: 2 x 11
## complaint_label anger anticipation disgust fear joy negative positive
## <chr> <int> <int> <int> <int> <int> <int> <int>
## 1 Complaint 559 730 439 552 372 1272 1392
## 2 Non-Complaint 597 1394 441 805 1108 1475 3023
## # … with 3 more variables: sadness <int>, surprise <int>, trust <int>Practicing with grouped summaries
tidy_twitter %>%
# Append the afinn sentiment dictionary
inner_join(get_sentiments("afinn")) %>%
# Group by both complaint label and whether or not the user is verified
group_by(complaint_label, usr_verified) %>%
# Summarize the data with an aggregate_value = sum(value)
summarize(aggregate_value = sum(value)) %>%
# Spread the complaint_label and aggregate_value columns
spread(complaint_label, aggregate_value) %>%
mutate(overall_sentiment = Complaint + `Non-Complaint`)## Joining, by = "word"## `summarise()` regrouping output by 'complaint_label' (override with `.groups` argument)## # A tibble: 2 x 4
## usr_verified Complaint `Non-Complaint` overall_sentiment
## <lgl> <dbl> <dbl> <dbl>
## 1 FALSE -1556 2348 792
## 2 TRUE -12 63 51Visualizing sentiment by complaint type
sentiment_twitter <- tidy_twitter %>%
# Append the bing sentiment dictionary
inner_join(get_sentiments("bing")) %>%
# Count by complaint label and sentiment
count(complaint_label, sentiment) %>%
# Spread the sentiment and count columns
spread(sentiment, n) %>%
# Compute overall_sentiment = positive - negative
mutate(overall_sentiment = positive - negative)## Joining, by = "word"# Create a bar plot out of overall sentiment by complaint level, colored by a complaint label factor
ggplot(
sentiment_twitter,
aes(x = complaint_label, y = overall_sentiment, fill = as.factor(complaint_label))
) +
geom_col(show.legend = FALSE) +
coord_flip() +
# Title the plot "Overall Sentiment by Complaint Type," with an "Airline Twitter Data" subtitle
labs(
title = "Overall Sentiment by Complaint Type",
subtitle = "Airline Twitter Data"
)
Ch. 4 - Topic Modeling
Latent Dirichlet allocation
[Video]
Topics as word probabilities
# Start with the topics output from the LDA run
lda_topics## topic term beta
## 1 1 _adowaa_ 0.000003570
## 2 2 _adowaa_ 0.000040200
## 3 1 _arzar 0.000003570
## 4 2 _arzar 0.000040200
## 5 1 _austrian 0.000003570
## 6 2 _austrian 0.000405874
## 7 1 _bbbb_ 0.000003570
## 8 2 _bbbb_ 0.000040200
## 9 1 _cierratindall 0.000039300
## 10 2 _cierratindall 0.000003660
## 11 1 _confucksia 0.000003570
## 12 2 _confucksia 0.000040200
## 13 1 _for_ 0.000039300
## 14 2 _for_ 0.000003660
## 15 1 _hkhodary 0.000039300
## 16 2 _hkhodary 0.000003660
## 17 1 _jchapman_ 0.000003570
## 18 2 _jchapman_ 0.000040200
## 19 1 _kellydale_ 0.000039300
## 20 2 _kellydale_ 0.000003660
## 21 1 _mikemo 0.000003570
## 22 2 _mikemo 0.000040200
## 23 1 _r2dc 0.000003570
## 24 2 _r2dc 0.000040200
## 25 1 _rickdale_ 0.000003570
## 26 2 _rickdale_ 0.000040200
## 27 1 _so_ 0.000039300
## 28 2 _so_ 0.000003660
## 29 1 _unshaykable_ 0.000003570
## 30 2 _unshaykable_ 0.000040200
## 31 1 0 0.000181984
## 32 2 0 0.000003660
## 33 1 0 0.000003570
## 34 2 0 0.000332743
## 35 1 0 0.000039300
## 36 2 0 0.000003660
## 37 1 000s 0.000003570
## 38 2 000s 0.000040200
## 39 1 16 0.000039300
## 40 2 16 0.000003660
## 41 1 31 0.000039300
## 42 2 31 0.000003660
## 43 1 00am 0.000003570
## 44 2 00am 0.000040200
## 45 1 00hrs 0.000039300
## 46 2 00hrs 0.000003660
## 47 1 1 0.000003570
## 48 2 1 0.000186483
## 49 1 110 0.000039300
## 50 2 110 0.000003660
## 51 1 187 0.000039300
## 52 2 187 0.000003660
## 53 1 01vzwyk6m6 0.000003570
## 54 2 01vzwyk6m6 0.000040200
## 55 1 2 0.000039300
## 56 2 2 0.000040200
## 57 1 262488093 0.000039300
## 58 2 262488093 0.000003660
## 59 1 3 0.000110618
## 60 2 3 0.000003660
## 61 1 345 0.000003570
## 62 2 345 0.000040200
## 63 1 03cb0ltpds 0.000003570
## 64 2 03cb0ltpds 0.000040200
## 65 1 03td0ayras 0.000003570
## 66 2 03td0ayras 0.000076800
## 67 1 04p 0.000003570
## 68 2 04p 0.000040200
## 69 1 506 0.000039300
## 70 2 506 0.000003660
## 71 1 535 0.000003570
## 72 2 535 0.000040200
## 73 1 54 0.000003570
## 74 2 54 0.000040200
## 75 1 05am 0.000003570
## 76 2 05am 0.000040200
## 77 1 6 0.000003570
## 78 2 6 0.000369309
## 79 1 6.3 0.000003570
## 80 2 6.3 0.000040200
## 81 1 6130 0.000039300
## 82 2 6130 0.000003660
## 83 1 6-Jul-14 0.000003570
## 84 2 6-Jul-14 0.000040200
## 85 1 7 0.000110618
## 86 2 7 0.000003660
## 87 1 072com 0.000003570
## 88 2 072com 0.000040200
## 89 1 800 0.000003570
## 90 2 800 0.000040200
## 91 1 830 0.000039300
## 92 2 830 0.000003660
## 93 1 844 0.000003570
## 94 2 844 0.000040200
## 95 1 9 0.000003570
## 96 2 9 0.000076800
## 97 1 930 0.000003570
## 98 2 930 0.000040200
## 99 1 09r 0.000003570
## 100 2 09r 0.000040200
## 101 1 0a6hhr4kgz 0.000003570
## 102 2 0a6hhr4kgz 0.000040200
## 103 1 0ehlmxettz 0.000039300
## 104 2 0ehlmxettz 0.000003660
## 105 1 0emezp02p9 0.000003570
## 106 2 0emezp02p9 0.000040200
## 107 1 0gnbllycjy 0.000003570
## 108 2 0gnbllycjy 0.000040200
## 109 1 0krfqzg3kv 0.000003570
## 110 2 0krfqzg3kv 0.000040200
## 111 1 0nykqvwoqz 0.000039300
## 112 2 0nykqvwoqz 0.000003660
## 113 1 0o4qfqxbta 0.000003570
## 114 2 0o4qfqxbta 0.000040200
## 115 1 0orabux4vy 0.000039300
## 116 2 0orabux4vy 0.000003660
## 117 1 0oxtsysmhe 0.000039300
## 118 2 0oxtsysmhe 0.000003660
## 119 1 0smbckz5cw 0.000039300
## 120 2 0smbckz5cw 0.000003660
## 121 1 0v2dsv3whk 0.000039300
## 122 2 0v2dsv3whk 0.000003660
## 123 1 0xqezfqhbv 0.000003570
## 124 2 0xqezfqhbv 0.000040200
## 125 1 1,000 0.000003570
## 126 2 1,000 0.000040200
## 127 1 1,200 0.000039300
## 128 2 1,200 0.000003660
## 129 1 1,362rt 0.000003570
## 130 2 1,362rt 0.000040200
## 131 1 1,5 0.000003570
## 132 2 1,5 0.000040200
## 133 1 1,741 0.000039300
## 134 2 1,741 0.000003660
## 135 1 1,759 0.000039300
## 136 2 1,759 0.000003660
## 137 1 1.2 0.000039300
## 138 2 1.2 0.000003660
## 139 1 1.5 0.000253351
## 140 2 1.5 0.000003660
## 141 1 1.5hrs 0.000039300
## 142 2 1.5hrs 0.000003660
## 143 1 10 0.000146301
## 144 2 10 0.001905047
## 145 1 10,000 0.000074900
## 146 2 10,000 0.000003660
## 147 1 10,700 0.000003570
## 148 2 10,700 0.000040200
## 149 1 10.1 0.000039300
## 150 2 10.1 0.000003660
## 151 1 100 0.000645866
## 152 2 100 0.000003660
## 153 1 100,000 0.000003570
## 154 2 100,000 0.000040200
## 155 1 1000 0.000003570
## 156 2 1000 0.000552135
## 157 1 100s 0.000039300
## 158 2 100s 0.000003660
## 159 1 100th 0.000003570
## 160 2 100th 0.000040200
## 161 1 101 0.000003570
## 162 2 101 0.000040200
## 163 1 1017 0.000039300
## 164 2 1017 0.000003660
## 165 1 103 0.000003570
## 166 2 103 0.000040200
## 167 1 1042 0.000039300
## 168 2 1042 0.000003660
## 169 1 1043 0.000003570
## 170 2 1043 0.000040200
## 171 1 1070noticias 0.000039300
## 172 2 1070noticias 0.000003660
## 173 1 108 0.000003570
## 174 2 108 0.000040200
## 175 1 10a 0.000039300
## 176 2 10a 0.000003660
## 177 1 10am 0.000074900
## 178 2 10am 0.000040200
## 179 1 10mins 0.000039300
## 180 2 10mins 0.000003660
## 181 1 10qghqwlou 0.000003570
## 182 2 10qghqwlou 0.000040200
## 183 1 10th 0.000003570
## 184 2 10th 0.000223048
## 185 1 10x 0.000039300
## 186 2 10x 0.000003660
## 187 1 10x16x24 0.000003570
## 188 2 10x16x24 0.000040200
## 189 1 10yrs 0.000003570
## 190 2 10yrs 0.000076800
## 191 1 11 0.000110618
## 192 2 11 0.000917787
## 193 1 11.15am 0.000003570
## 194 2 11.15am 0.000040200
## 195 1 111 0.000039300
## 196 2 111 0.000040200
## 197 1 1116 0.000039300
## 198 2 1116 0.000003660
## 199 1 112 0.000039300
## 200 2 112 0.000003660
## 201 1 1130 0.000003570
## 202 2 1130 0.000040200
## 203 1 1133 0.000003570
## 204 2 1133 0.000040200
## 205 1 1147 0.000039300
## 206 2 1147 0.000003660
## 207 1 1148 0.000003570
## 208 2 1148 0.000040200
## 209 1 1150 0.000003570
## 210 2 1150 0.000040200
## 211 1 1151 0.000003570
## 212 2 1151 0.000040200
## 213 1 116 0.000039300
## 214 2 116 0.000003660
## 215 1 117 0.000074900
## 216 2 117 0.000003660
## 217 1 1173 0.000039300
## 218 2 1173 0.000003660
## 219 1 1180 0.000003570
## 220 2 1180 0.000040200
## 221 1 11am 0.000074900
## 222 2 11am 0.000003660
## 223 1 11c 0.000003570
## 224 2 11c 0.000040200
## 225 1 11m 0.000003570
## 226 2 11m 0.000040200
## 227 1 11pm 0.000003570
## 228 2 11pm 0.000040200
## 229 1 12 0.000859965
## 230 2 12 0.000186483
## 231 1 120 0.000003570
## 232 2 120 0.000113352
## 233 1 120,000 0.000146301
## 234 2 120,000 0.000003660
## 235 1 1200 0.000074900
## 236 2 1200 0.000003660
## 237 1 12000 0.000039300
## 238 2 12000 0.000003660
## 239 1 121 0.000003570
## 240 2 121 0.000040200
## 241 1 1211 0.000039300
## 242 2 1211 0.000003660
## 243 1 1223 0.000039300
## 244 2 1223 0.000003660
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## 13397 1 gaxtgea4xb 0.000039300
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## 13411 1 gdclearedtoland 0.000039300
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## 13558 2 ger 0.000076800
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## 13592 2 gestuurd 0.000076800
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## 13594 2 getalife 0.000076800
## 13595 1 getaway 0.004071452
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## 13604 2 getittogether 0.000076800
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## 13609 1 gettingonmynerves 0.000039300
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## 13611 1 geuvmq 0.000039300
## 13612 2 geuvmq 0.000003660
## 13613 1 geval 0.000003570
## 13614 2 geval 0.000076800
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## 13617 1 gewacht 0.000039300
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## 13619 1 geweest 0.000003570
## 13620 2 geweest 0.000076800
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## 13626 2 gezien 0.000040200
## 13627 1 geziyodur 0.000003570
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## 13632 2 gfctnfbge2 0.000040200
## 13633 1 gfrgecrqmc 0.000039300
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## 13635 1 gg7jhlvmlg 0.000003570
## 13636 2 gg7jhlvmlg 0.000040200
## 13637 1 ggomezbaides 0.000039300
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## 13639 1 ggrrr 0.000039300
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## 13641 1 gh 0.000074900
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## 13643 1 gh2artfz5y 0.000003570
## 13644 2 gh2artfz5y 0.000040200
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## 13649 1 gheijkoop 0.000003570
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## 13653 1 ghoshanurag 0.000039300
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## 13659 1 gibraltar 0.000003570
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## 13669 1 gidionpeters 0.000039300
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## 13671 1 gidiyor 0.000003570
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## 13674 2 gift 0.000149917
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## 13681 1 gigatownnsn 0.000039300
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## 13683 1 gijyljadag 0.000003570
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## 13691 1 ginger 0.000003570
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## 13693 1 gingerhardage 0.000039300
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## 13697 1 giraffe 0.000003570
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## 13705 1 giraffes 0.000289034
## 13706 2 giraffes 0.000149917
## 13707 1 giraffs 0.000039300
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## 13711 1 girl 0.000217667
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## 13725 1 girrafes 0.000003570
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## 13757 1 gl 0.000003570
## 13758 2 gl 0.000040200
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## 13760 2 gl1ffbr9th 0.000003660
## 13761 1 gla 0.000003570
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## 13772 2 glasgow 0.000076800
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## 13775 1 glasses 0.000039300
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## 13777 1 glbc_cleveland 0.000003570
## 13778 2 glbc_cleveland 0.000040200
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## 13784 2 glennwilliams20 0.000040200
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## 13787 1 glint 0.000039300
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## 13789 1 glitch 0.000146301
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## 13792 2 glittery 0.000003660
## 13793 1 glka8tcfuv 0.000039300
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## 13795 1 gloating 0.000039300
## 13796 2 gloating 0.000003660
## 13797 1 global 0.000146301
## 13798 2 global 0.000003660
## 13799 1 global_academic 0.000003570
## 13800 2 global_academic 0.000040200
## 13801 1 globalsocialm2 0.000003570
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## 13807 1 gloves 0.000003570
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## 13816 2 gluten 0.000076800
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## 13819 1 glynaikins 0.000003570
## 13820 2 glynaikins 0.000040200
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## 13823 1 gm0onumfaa 0.000039300
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## 13825 1 gmail 0.000039300
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## 13827 1 gmail.com 0.000003570
## 13828 2 gmail.com 0.000259613
## 13829 1 gnnec1390 0.000003570
## 13830 2 gnnec1390 0.000040200
## 13831 1 gnpc2g2hwu 0.000003570
## 13832 2 gnpc2g2hwu 0.000040200
## 13833 1 gnrepuqmpz 0.000003570
## 13834 2 gnrepuqmpz 0.000040200
## 13835 1 gnuk 0.000003570
## 13836 2 gnuk 0.000040200
## 13837 1 gnvtanrwnw 0.000003570
## 13838 2 gnvtanrwnw 0.000040200
## 13839 1 goa 0.000039300
## 13840 2 goa 0.000003660
## 13841 1 goab 0.000003570
## 13842 2 goab 0.000040200
## 13843 1 goairlinesindia 0.000039300
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## 13845 1 goal 0.000003570
## 13846 2 goal 0.000734961
## 13847 1 goals 0.000110618
## 13848 2 goals 0.000003660
## 13849 1 gobierno 0.000003570
## 13850 2 gobierno 0.000076800
## 13851 1 gocubs 0.000039300
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## 13853 1 god 0.000003570
## 13854 2 god 0.000588700
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## 13857 1 goddamn 0.000039300
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## 13859 1 godforsaken 0.000074900
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## 13861 1 godiva 0.000039300
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## 13863 1 godomrep 0.000003570
## 13864 2 godomrep 0.000076800
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## 13866 2 goed 0.000223048
## 13867 1 goede 0.000003570
## 13868 2 goede 0.000040200
## 13869 1 goeie 0.000003570
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## 13871 1 gogo 0.000217667
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## 13873 1 goin 0.000003570
## 13874 2 goin 0.000040200
## 13875 1 goingplacescle 0.000039300
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## 13877 1 gokhanmumcu 0.000039300
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## 13879 1 gokongwei 0.000039300
## 13880 2 gokongwei 0.000003660
## 13881 1 gol 0.000003570
## 13882 2 gol 0.000040200
## 13883 1 gold 0.000003570
## 13884 2 gold 0.000625265
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## 13886 2 golden 0.000040200
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## 13888 2 golden4r 0.000040200
## 13889 1 goldengateblond 0.000039300
## 13890 2 goldengateblond 0.000003660
## 13891 1 goldenrolen 0.000003570
## 13892 2 goldenrolen 0.000040200
## 13893 1 golf 0.000003570
## 13894 2 golf 0.000369309
## 13895 1 golfe 0.000039300
## 13896 2 golfe 0.000003660
## 13897 1 golondrinas 0.000039300
## 13898 2 golondrinas 0.000003660
## 13899 1 golpes 0.000003570
## 13900 2 golpes 0.000040200
## 13901 1 gome 0.000003570
## 13902 2 gome 0.000040200
## 13903 1 gönderdiğini 0.000003570
## 13904 2 gönderdiğini 0.000040200
## 13905 1 gonderirsiniz 0.000039300
## 13906 2 gonderirsiniz 0.000003660
## 13907 1 göndermemiş 0.000003570
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## 13909 1 gonna 0.000003570
## 13910 2 gonna 0.000808091
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## 13912 2 goodbye 0.000003660
## 13913 1 goodie 0.000039300
## 13914 2 goodie 0.000003660
## 13915 1 goodies 0.000003570
## 13916 2 goodies 0.000076800
## 13917 1 goodlifegab 0.000003570
## 13918 2 goodlifegab 0.000040200
## 13919 1 goodness 0.000003570
## 13920 2 goodness 0.000040200
## 13921 1 goodservice 0.000039300
## 13922 2 goodservice 0.000003660
## 13923 1 google 0.000003570
## 13924 2 google 0.000223048
## 13925 1 google's 0.000003570
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## 13927 1 googleen 0.000039300
## 13928 2 googleen 0.000003660
## 13929 1 goonerjenkoo 0.000003570
## 13930 2 goonerjenkoo 0.000040200
## 13931 1 goose 0.000003570
## 13932 2 goose 0.000040200
## 13933 1 goosefliesunited 0.000039300
## 13934 2 goosefliesunited 0.000003660
## 13935 1 gooseisland 0.000003570
## 13936 2 gooseisland 0.000113352
## 13937 1 gopies 0.000039300
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## 13939 1 gordon 0.000039300
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## 13941 1 gördüğüme 0.000039300
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## 13943 1 gören 0.000003570
## 13944 2 gören 0.000040200
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## 13948 2 gorg 0.000040200
## 13949 1 gorgeous 0.000003570
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## 17435 1 karimyscreamy 0.000003570
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## 17469 1 katılabiliyor 0.000003570
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## 17511 1 kciairport 0.000039300
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## 17527 1 kdhxlabkwo 0.000039300
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## 17548 2 keeping 0.000296178
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## 17552 2 keer 0.000113352
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## 17669 1 kholmeslive 0.000003570
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## 17676 2 kick 0.000113352
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## 17719 1 kimgarst 0.000003570
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## 17835 1 klga 0.000039300
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## 17837 1 klia 0.000003570
## 17838 2 klia 0.000076800
## 17839 1 klm_id 0.000003570
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## 17841 1 klm_mx 0.000181984
## 17842 2 klm_mx 0.000405874
## 17843 1 klm_press 0.000039300
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## 17845 1 klm_uk 0.000574499
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## 17847 1 klm.com 0.000003570
## 17848 2 klm.com 0.000076800
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## 17853 1 klmblog 0.000181984
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## 17861 1 klmindia 0.001038381
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## 17863 1 klmracist 0.000003570
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## 17871 1 km 0.000039300
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## 17875 1 kmel 0.000003570
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## 17905 1 knuffelen 0.000039300
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## 17909 1 ko 0.000039300
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## 19785 1 más 0.000110618
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## 19805 1 massachusetts 0.000003570
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## 19877 1 mavzx 0.000039300
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## 19879 1 max 0.000003570
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## 19881 1 maximaslet 0.000003570
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## 19883 1 maximizing 0.000039300
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## 19885 1 maximum 0.000110618
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## 19891 1 mayo 0.000039300
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## 19897 1 maysapusa 0.000003570
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## 19901 1 mazdaki 0.000039300
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## 19903 1 mb5ubknnqf 0.000003570
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## 20953 1 mrobbinsdesign 0.000039300
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## 20955 1 mrsamandaprowse 0.000003570
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## 21077 1 multiplus 0.000003570
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## 21213 1 n66828 0.000039300
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## 21215 1 n6g1wifblg 0.000003570
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## 21541 1 neveragain 0.000396083
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## 21555 1 newark 0.000681549
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## 21563 1 newfavoriteairline 0.000039300
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## 21565 1 newfleet 0.000003570
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## 21569 1 newleaf 0.000039300
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## 21577 1 newport 0.000039300
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## 21579 1 news 0.000003570
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## 21581 1 news.your 0.000039300
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## 21585 1 newspaper 0.000003570
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## 21587 1 newsreputation 0.000039300
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## 21589 1 newticket 0.000003570
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## 21591 1 newyork 0.000074900
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## 21593 1 newyorkbound 0.000039300
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## 21595 1 newyorkcity 0.000003570
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## 21597 1 nexgen 0.000039300
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## 21601 1 neymar 0.000003570
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## 21603 1 nezina 0.000039300
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## 21605 1 nff4h7 0.000003570
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## 21607 1 nfhaosd95w 0.000039300
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## 21609 1 nfld 0.000003570
## 21610 2 nfld 0.000040200
## 21611 1 ng 0.000003570
## 21612 2 ng 0.000369309
## 21613 1 ngemis:p 0.000039300
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## 21615 1 ngga 0.000003570
## 21616 2 ngga 0.000076800
## 21617 1 nggak 0.000003570
## 21618 2 nggak 0.000040200
## 21619 1 ngile 0.000003570
## 21620 2 ngile 0.000040200
## 21621 1 ngpoo 0.000039300
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## 21623 1 nh 0.000039300
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## 21625 1 nh242rptip 0.000003570
## 21626 2 nh242rptip 0.000040200
## 21627 1 nhhotelesnl 0.000003570
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## 21629 1 nhlawards 0.000003570
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## 21631 1 nhmpublishing 0.000003570
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## 21633 1 nhradio 0.000003570
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## 21635 1 nhu9kcx9ma 0.000003570
## 21636 2 nhu9kcx9ma 0.000040200
## 21637 1 nhv 0.000003570
## 21638 2 nhv 0.000040200
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## 21640 2 ni 0.000588700
## 21641 1 ni7nwctekr 0.000003570
## 21642 2 ni7nwctekr 0.000040200
## 21643 1 niagara 0.000039300
## 21644 2 niagara 0.000003660
## 21645 1 niallcorbally94 0.000003570
## 21646 2 niallcorbally94 0.000040200
## 21647 1 nice 0.000039300
## 21648 2 nice 0.003367656
## 21649 1 nicely 0.000003570
## 21650 2 nicely 0.000149917
## 21651 1 nicer 0.000074900
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## 21653 1 nicetrybuddy 0.000039300
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## 21655 1 nicht 0.000039300
## 21656 2 nicht 0.000003660
## 21657 1 nicka_72 0.000039300
## 21658 2 nicka_72 0.000003660
## 21659 1 nickel 0.000039300
## 21660 2 nickel 0.000003660
## 21661 1 nickjayres 0.000039300
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## 21663 1 nickkeefe 0.000039300
## 21664 2 nickkeefe 0.000076800
## 21665 1 nickname 0.000003570
## 21666 2 nickname 0.000040200
## 21667 1 nicolasmaduro 0.000003570
## 21668 2 nicolasmaduro 0.000040200
## 21669 1 nicole_genter 0.000003570
## 21670 2 nicole_genter 0.000040200
## 21671 1 nicolenewblack 0.000039300
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## 21673 1 nicoleyershon 0.000003570
## 21674 2 nicoleyershon 0.000040200
## 21675 1 nie 0.000003570
## 21676 2 nie 0.000076800
## 21677 1 niet 0.000003570
## 21678 2 niet 0.001064048
## 21679 1 niets 0.000003570
## 21680 2 niets 0.000076800
## 21681 1 nieuw 0.000003570
## 21682 2 nieuw 0.000040200
## 21683 1 nieuwe 0.000003570
## 21684 2 nieuwe 0.000040200
## 21685 1 nieuws 0.000003570
## 21686 2 nieuws 0.000040200
## 21687 1 nifty 0.000003570
## 21688 2 nifty 0.000040200
## 21689 1 nigelhollis 0.000039300
## 21690 2 nigelhollis 0.000003660
## 21691 1 nigeria 0.000003570
## 21692 2 nigeria 0.000040200
## 21693 1 nigga 0.000003570
## 21694 2 nigga 0.000040200
## 21695 1 niggas 0.000003570
## 21696 2 niggas 0.000040200
## 21697 1 night 0.001752045
## 21698 2 night 0.000003660
## 21699 1 night's 0.000003570
## 21700 2 night's 0.000040200
## 21701 1 nightclub 0.000039300
## 21702 2 nightclub 0.000003660
## 21703 1 nightmare 0.000110618
## 21704 2 nightmare 0.000186483
## 21705 1 nightmarish 0.000003570
## 21706 2 nightmarish 0.000040200
## 21707 1 nights 0.000003570
## 21708 2 nights 0.000076800
## 21709 1 nih 0.000003570
## 21710 2 nih 0.000040200
## 21711 1 nihalaydingor 0.000003570
## 21712 2 nihalaydingor 0.000040200
## 21713 1 nikkay_sherwood 0.000039300
## 21714 2 nikkay_sherwood 0.000003660
## 21715 1 nikki_ferrell 0.000074900
## 21716 2 nikki_ferrell 0.000003660
## 21717 1 niña 0.000039300
## 21718 2 niña 0.000003660
## 21719 1 ningún 0.000039300
## 21720 2 ningún 0.000003660
## 21721 1 niños 0.000039300
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## 21723 1 ninvszqarf 0.000039300
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## 21725 1 nio6rciwrm 0.000003570
## 21726 2 nio6rciwrm 0.000040200
## 21727 1 niqhqdmj6z 0.000039300
## 21728 2 niqhqdmj6z 0.000003660
## 21729 1 nirahyman 0.000039300
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## 21731 1 nishajg 0.000039300
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## 21733 1 nite 0.000039300
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## 21735 1 nivedithalva 0.000039300
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## 21737 1 nivel 0.000003570
## 21738 2 nivel 0.000040200
## 21739 1 niver9 0.000003570
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## 21741 1 nje6esooli 0.000039300
## 21742 2 nje6esooli 0.000003660
## 21743 1 njtransit 0.000003570
## 21744 2 njtransit 0.000040200
## 21745 1 njtransit_delay 0.000003570
## 21746 2 njtransit_delay 0.000040200
## 21747 1 nk 0.000003570
## 21748 2 nk 0.000040200
## 21749 1 nkgidlxmpt 0.000003570
## 21750 2 nkgidlxmpt 0.000040200
## 21751 1 nkotb 0.000752915
## 21752 2 nkotb 0.000003660
## 21753 1 nkotbpartyplane 0.000253351
## 21754 2 nkotbpartyplane 0.000040200
## 21755 1 nkuqnjuyys 0.000003570
## 21756 2 nkuqnjuyys 0.000040200
## 21757 1 nkzxc8cp6x 0.000003570
## 21758 2 nkzxc8cp6x 0.000040200
## 21759 1 nl 0.000003570
## 21760 2 nl 0.000223048
## 21761 1 nl1u2vs2bh 0.000003570
## 21762 2 nl1u2vs2bh 0.000040200
## 21763 1 nla 0.000003570
## 21764 2 nla 0.000040200
## 21765 1 nllisnjwv7 0.000039300
## 21766 2 nllisnjwv7 0.000003660
## 21767 1 nlx78 0.000039300
## 21768 2 nlx78 0.000003660
## 21769 1 nm 0.000003570
## 21770 2 nm 0.000040200
## 21771 1 nm1wzskm6t 0.000039300
## 21772 2 nm1wzskm6t 0.000003660
## 21773 1 nman 0.000003570
## 21774 2 nman 0.000040200
## 21775 1 nmlj7mjvw5 0.000039300
## 21776 2 nmlj7mjvw5 0.000003660
## 21777 1 nmn 0.000039300
## 21778 2 nmn 0.000003660
## 21779 1 nnkfopziut 0.000039300
## 21780 2 nnkfopziut 0.000003660
## 21781 1 nnyuni3fkj 0.000003570
## 21782 2 nnyuni3fkj 0.000040200
## 21783 1 nº 0.000003570
## 21784 2 nº 0.000076800
## 21785 1 no_airport 0.000003570
## 21786 2 no_airport 0.000040200
## 21787 1 no's 0.000039300
## 21788 2 no's 0.000003660
## 21789 1 noahcyrus 0.000003570
## 21790 2 noahcyrus 0.000040200
## 21791 1 nob1feevm0 0.000003570
## 21792 2 nob1feevm0 0.000040200
## 21793 1 noch 0.000003570
## 21794 2 noch 0.000040200
## 21795 1 noche 0.000003570
## 21796 2 noche 0.000040200
## 21797 1 noches 0.000003570
## 21798 2 noches 0.000040200
## 21799 1 nocustomerservice 0.000074900
## 21800 2 nocustomerservice 0.000003660
## 21801 1 nodig 0.000039300
## 21802 2 nodig 0.000076800
## 21803 1 noe 0.000039300
## 21804 2 noe 0.000003660
## 21805 1 noen 0.000003570
## 21806 2 noen 0.000040200
## 21807 1 noendinsight 0.000039300
## 21808 2 noendinsight 0.000003660
## 21809 1 noerapenal 0.000003570
## 21810 2 noerapenal 0.000040200
## 21811 1 noexcuses 0.000039300
## 21812 2 noexcuses 0.000003660
## 21813 1 noffa 0.000039300
## 21814 2 noffa 0.000003660
## 21815 1 noformets 0.000039300
## 21816 2 noformets 0.000003660
## 21817 1 nofrets 0.000039300
## 21818 2 nofrets 0.000003660
## 21819 1 nog 0.000039300
## 21820 2 nog 0.000332743
## 21821 1 noggeenlid 0.000039300
## 21822 2 noggeenlid 0.000003660
## 21823 1 noh 0.000003570
## 21824 2 noh 0.000040200
## 21825 1 noitsnotrefundable 0.000039300
## 21826 2 noitsnotrefundable 0.000003660
## 21827 1 nola 0.000003570
## 21828 2 nola 0.000040200
## 21829 1 noluv 0.000039300
## 21830 2 noluv 0.000003660
## 21831 1 nomamar 0.000003570
## 21832 2 nomamar 0.000040200
## 21833 1 nombre 0.000003570
## 21834 2 nombre 0.000040200
## 21835 1 nomenclature 0.000039300
## 21836 2 nomenclature 0.000003660
## 21837 1 nonalanicottage 0.000039300
## 21838 2 nonalanicottage 0.000003660
## 21839 1 nondirect 0.000039300
## 21840 2 nondirect 0.000003660
## 21841 1 nonetheless 0.000003570
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## 21843 1 nonjok 0.000039300
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## 21845 1 nonsense 0.000003570
## 21846 2 nonsense 0.000076800
## 21847 1 nonstop 0.000039300
## 21848 2 nonstop 0.000076800
## 21849 1 nooit 0.000074900
## 21850 2 nooit 0.000040200
## 21851 1 noon 0.000003570
## 21852 2 noon 0.000076800
## 21853 1 noooo 0.000003570
## 21854 2 noooo 0.000076800
## 21855 1 noooooo 0.000003570
## 21856 2 noooooo 0.000040200
## 21857 1 noor_bank 0.000039300
## 21858 2 noor_bank 0.000003660
## 21859 1 nop 0.000003570
## 21860 2 nop 0.000040200
## 21861 1 nope 0.000039300
## 21862 2 nope 0.000296178
## 21863 1 nopoint 0.000039300
## 21864 2 nopoint 0.000003660
## 21865 1 nopuyxxzsb 0.000039300
## 21866 2 nopuyxxzsb 0.000003660
## 21867 1 noralow 0.000003570
## 21868 2 noralow 0.000040200
## 21869 1 nordheim 0.000003570
## 21870 2 nordheim 0.000040200
## 21871 1 noreason 0.000039300
## 21872 2 noreason 0.000003660
## 21873 1 noremamar 0.000039300
## 21874 2 noremamar 0.000003660
## 21875 1 norfolk 0.000003570
## 21876 2 norfolk 0.000040200
## 21877 1 norm 0.000003570
## 21878 2 norm 0.000040200
## 21879 1 normaal 0.000039300
## 21880 2 normaal 0.000003660
## 21881 1 normal 0.000146301
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## 21883 1 normanmclaren 0.000003570
## 21884 2 normanmclaren 0.000040200
## 21885 1 north 0.000146301
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## 21887 1 northeast 0.000003570
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## 21893 1 norwegian 0.000003570
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## 21896 2 nos 0.000369309
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## 21898 2 nose 0.000040200
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## 21900 2 nosotros 0.000149917
## 21901 1 nostalgic 0.000003570
## 21902 2 nostalgic 0.000040200
## 21903 1 not.otherwise 0.000039300
## 21904 2 not.otherwise 0.000003660
## 21905 1 notamment 0.000039300
## 21906 2 notamment 0.000003660
## 21907 1 notch 0.000003570
## 21908 2 notch 0.000076800
## 21909 1 notched 0.000039300
## 21910 2 notched 0.000003660
## 21911 1 notcoachlikens 0.000039300
## 21912 2 notcoachlikens 0.000003660
## 21913 1 notcool 0.000110618
## 21914 2 notcool 0.000003660
## 21915 1 note 0.000289034
## 21916 2 note 0.000003660
## 21917 1 noted 0.000003570
## 21918 2 noted 0.000040200
## 21919 1 notes 0.000074900
## 21920 2 notes 0.000003660
## 21921 1 noteveneconomyplus 0.000003570
## 21922 2 noteveneconomyplus 0.000040200
## 21923 1 notgoodenough 0.000003570
## 21924 2 notgoodenough 0.000040200
## 21925 1 nothappy 0.000003570
## 21926 2 nothappy 0.000076800
## 21927 1 nothappyclient 0.000003570
## 21928 2 nothappyclient 0.000040200
## 21929 1 nothelping 0.000003570
## 21930 2 nothelping 0.000040200
## 21931 1 nother 0.000039300
## 21932 2 nother 0.000003660
## 21933 1 nothing.need 0.000003570
## 21934 2 nothing.need 0.000040200
## 21935 1 nothings 0.000003570
## 21936 2 nothings 0.000040200
## 21937 1 notice 0.000003570
## 21938 2 notice 0.000296178
## 21939 1 noticed 0.000003570
## 21940 2 noticed 0.000149917
## 21941 1 notices 0.000039300
## 21942 2 notices 0.000003660
## 21943 1 noticiasrcn 0.000003570
## 21944 2 noticiasrcn 0.000040200
## 21945 1 notiek 0.000039300
## 21946 2 notiek 0.000003660
## 21947 1 notification 0.000217667
## 21948 2 notification 0.000003660
## 21949 1 notified 0.000110618
## 21950 2 notified 0.000003660
## 21951 1 notify 0.000074900
## 21952 2 notify 0.000003660
## 21953 1 notifying 0.000039300
## 21954 2 notifying 0.000003660
## 21955 1 notmyrideyet 0.000039300
## 21956 2 notmyrideyet 0.000003660
## 21957 1 notoriously 0.000003570
## 21958 2 notoriously 0.000040200
## 21959 1 notoversize 0.000003570
## 21960 2 notoversize 0.000040200
## 21961 1 notracist 0.000039300
## 21962 2 notracist 0.000003660
## 21963 1 notseats 0.000039300
## 21964 2 notseats 0.000003660
## 21965 1 notspeirs 0.000003570
## 21966 2 notspeirs 0.000040200
## 21967 1 notting 0.000003570
## 21968 2 notting 0.000040200
## 21969 1 notwinning 0.000039300
## 21970 2 notwinning 0.000003660
## 21971 1 nou 0.000039300
## 21972 2 nou 0.000076800
## 21973 1 nous 0.000110618
## 21974 2 nous 0.000040200
## 21975 1 nouvel 0.000003570
## 21976 2 nouvel 0.000040200
## 21977 1 nov 0.000074900
## 21978 2 nov 0.000003660
## 21979 1 nova 0.000003570
## 21980 2 nova 0.000040200
## 21981 1 novamente 0.000003570
## 21982 2 novamente 0.000040200
## 21983 1 novedad 0.000003570
## 21984 2 novedad 0.000040200
## 21985 1 november 0.000003570
## 21986 2 november 0.000040200
## 21987 1 novembro 0.000039300
## 21988 2 novembro 0.000003660
## 21989 1 novità 0.000039300
## 21990 2 novità 0.000003660
## 21991 1 novolarporklm 0.000039300
## 21992 2 novolarporklm 0.000003660
## 21993 1 nowaytotreatacustomer 0.000003570
## 21994 2 nowaytotreatacustomer 0.000040200
## 21995 1 nowfriday 0.000039300
## 21996 2 nowfriday 0.000003660
## 21997 1 npmbbdt28j 0.000003570
## 21998 2 npmbbdt28j 0.000040200
## 21999 1 nprscottsimon 0.000003570
## 22000 2 nprscottsimon 0.000040200
## 22001 1 npumford 0.000039300
## 22002 2 npumford 0.000003660
## 22003 1 nr 0.000039300
## 22004 2 nr 0.000003660
## 22005 1 nrsa 0.000039300
## 22006 2 nrsa 0.000003660
## 22007 1 nrt 0.000110618
## 22008 2 nrt 0.000040200
## 22009 1 nsa14 0.000003570
## 22010 2 nsa14 0.000040200
## 22011 1 nsbe 0.000039300
## 22012 2 nsbe 0.000003660
## 22013 1 nscftwkngq 0.000039300
## 22014 2 nscftwkngq 0.000003660
## 22015 1 nsv9fhlgcb 0.000003570
## 22016 2 nsv9fhlgcb 0.000040200
## 22017 1 nt 0.000039300
## 22018 2 nt 0.000003660
## 22019 1 nt0xskbkj0 0.000003570
## 22020 2 nt0xskbkj0 0.000040200
## 22021 1 ntqfy0v6ni 0.000039300
## 22022 2 ntqfy0v6ni 0.000003660
## 22023 1 nu 0.000146301
## 22024 2 nu 0.000369309
## 22025 1 nuages 0.000039300
## 22026 2 nuages 0.000003660
## 22027 1 nuestra 0.000039300
## 22028 2 nuestra 0.000003660
## 22029 1 nuestras 0.000003570
## 22030 2 nuestras 0.000076800
## 22031 1 nuestro 0.000003570
## 22032 2 nuestro 0.000076800
## 22033 1 nuestros 0.000039300
## 22034 2 nuestros 0.000003660
## 22035 1 nueva 0.000110618
## 22036 2 nueva 0.000003660
## 22037 1 nuevas 0.000074900
## 22038 2 nuevas 0.000003660
## 22039 1 nuevo 0.000003570
## 22040 2 nuevo 0.000076800
## 22041 1 nuevos 0.000003570
## 22042 2 nuevos 0.000040200
## 22043 1 nuffsaid 0.000039300
## 22044 2 nuffsaid 0.000003660
## 22045 1 nulbeyuk9m 0.000003570
## 22046 2 nulbeyuk9m 0.000040200
## 22047 1 numaralariniz 0.000003570
## 22048 2 numaralariniz 0.000040200
## 22049 1 numéro 0.000039300
## 22050 2 numéro 0.000003660
## 22051 1 números 0.000003570
## 22052 2 números 0.000040200
## 22053 1 numerous 0.000074900
## 22054 2 numerous 0.000003660
## 22055 1 nummer 0.000039300
## 22056 2 nummer 0.000003660
## 22057 1 nun 0.000003570
## 22058 2 nun 0.000040200
## 22059 1 nunca 0.000003570
## 22060 2 nunca 0.000076800
## 22061 1 nunezarturo 0.000003570
## 22062 2 nunezarturo 0.000040200
## 22063 1 nunzio424 0.000039300
## 22064 2 nunzio424 0.000003660
## 22065 1 nuovi 0.000039300
## 22066 2 nuovi 0.000003660
## 22067 1 nuqv0u2yhw 0.000039300
## 22068 2 nuqv0u2yhw 0.000003660
## 22069 1 nur 0.000003570
## 22070 2 nur 0.000040200
## 22071 1 nusrjal6te 0.000039300
## 22072 2 nusrjal6te 0.000003660
## 22073 1 nut 0.000003570
## 22074 2 nut 0.000040200
## 22075 1 nuts 0.000003570
## 22076 2 nuts 0.000040200
## 22077 1 nuxrmqoguk 0.000003570
## 22078 2 nuxrmqoguk 0.000040200
## 22079 1 nv 0.000003570
## 22080 2 nv 0.000076800
## 22081 1 nvnbbo 0.000039300
## 22082 2 nvnbbo 0.000003660
## 22083 1 nvvm39xxp8 0.000039300
## 22084 2 nvvm39xxp8 0.000003660
## 22085 1 nvyeri83bg 0.000003570
## 22086 2 nvyeri83bg 0.000040200
## 22087 1 nvz6bveebt 0.000003570
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## 22097 1 nwb7dyuzjo 0.000003570
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## 22497 1 ome_haam 0.000003570
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## 22499 1 omfg 0.000146301
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## 22503 1 omgadamsaleh 0.000003570
## 22504 2 omgadamsaleh 0.000186483
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## 22527 1 onboard 0.000217667
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## 22547 1 online 0.000039300
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## 22551 1 ons 0.000181984
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## 31197 1 travel_hack 0.000003570
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## 31229 1 travelproblems 0.000003570
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## 31231 1 travels 0.000003570
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## 31593 1 typical 0.000110618
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## 31646 2 uae 0.000076800
## 31647 1 uai_ou 0.000039300
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## 31651 1 uanoway 0.000003570
## 31652 2 uanoway 0.000040200
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## 31654 2 uapikee 0.000040200
## 31655 1 ub_unofficial 0.000003570
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## 31658 2 uber 0.000223048
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## 31670 2 uçağı 0.000076800
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## 31672 2 uçağında 0.000040200
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## 32840 2 voicemail 0.000040200
## 32841 1 voici 0.000003570
## 32842 2 voici 0.000040200
## 32843 1 void 0.000039300
## 32844 2 void 0.000003660
## 32845 1 voie 0.000039300
## 32846 2 voie 0.000003660
## 32847 1 vois 0.000039300
## 32848 2 vois 0.000003660
## 32849 1 vokwgqdoeo 0.000003570
## 32850 2 vokwgqdoeo 0.000040200
## 32851 1 vol 0.000003570
## 32852 2 vol 0.000479004
## 32853 1 vola 0.000003570
## 32854 2 vola 0.000040200
## 32855 1 volando 0.000039300
## 32856 2 volando 0.000003660
## 32857 1 volar 0.000181984
## 32858 2 volar 0.000003660
## 32859 1 volará 0.000039300
## 32860 2 volará 0.000003660
## 32861 1 volarán 0.000039300
## 32862 2 volarán 0.000003660
## 32863 1 volarespasion 0.000003570
## 32864 2 volarespasion 0.000040200
## 32865 1 voldoende 0.000003570
## 32866 2 voldoende 0.000040200
## 32867 1 volendam 0.000003570
## 32868 2 volendam 0.000040200
## 32869 1 volgende 0.000003570
## 32870 2 volgende 0.000149917
## 32871 1 voli 0.000074900
## 32872 2 voli 0.000003660
## 32873 1 volle 0.000003570
## 32874 2 volle 0.000040200
## 32875 1 volo 0.000003570
## 32876 2 volo 0.000149917
## 32877 1 vololowcost 0.000039300
## 32878 2 vololowcost 0.000003660
## 32879 1 volop 0.000003570
## 32880 2 volop 0.000040200
## 32881 1 volpinprops 0.000003570
## 32882 2 volpinprops 0.000040200
## 32883 1 vols 0.000003570
## 32884 2 vols 0.000076800
## 32885 1 volta 0.000074900
## 32886 2 volta 0.000003660
## 32887 1 volume 0.000003570
## 32888 2 volume 0.000076800
## 32889 1 volunteer 0.000074900
## 32890 2 volunteer 0.000003660
## 32891 1 volunteered 0.000003570
## 32892 2 volunteered 0.000040200
## 32893 1 volunteers 0.000003570
## 32894 2 volunteers 0.000076800
## 32895 1 volver 0.000039300
## 32896 2 volver 0.000003660
## 32897 1 volvera 0.000003570
## 32898 2 volvera 0.000040200
## 32899 1 volvere 0.000039300
## 32900 2 volvere 0.000003660
## 32901 1 volveremos 0.000039300
## 32902 2 volveremos 0.000003660
## 32903 1 vomitted 0.000003570
## 32904 2 vomitted 0.000040200
## 32905 1 von 0.000003570
## 32906 2 von 0.000040200
## 32907 1 vonage 0.000003570
## 32908 2 vonage 0.000040200
## 32909 1 vond 0.000039300
## 32910 2 vond 0.000003660
## 32911 1 vontade 0.000003570
## 32912 2 vontade 0.000040200
## 32913 1 voo 0.000003570
## 32914 2 voo 0.000076800
## 32915 1 vooctc4xf3 0.000003570
## 32916 2 vooctc4xf3 0.000040200
## 32917 1 voor 0.000003570
## 32918 2 voor 0.000844656
## 32919 1 voorbeeldig 0.000039300
## 32920 2 voorbeeldig 0.000003660
## 32921 1 voordelen 0.000003570
## 32922 2 voordelen 0.000040200
## 32923 1 voorstelt 0.000039300
## 32924 2 voorstelt 0.000003660
## 32925 1 voren 0.000039300
## 32926 2 voren 0.000003660
## 32927 1 vorige 0.000074900
## 32928 2 vorige 0.000003660
## 32929 1 vorm 0.000003570
## 32930 2 vorm 0.000040200
## 32931 1 vos 0.000039300
## 32932 2 vos 0.000003660
## 32933 1 vote 0.000110618
## 32934 2 vote 0.000003660
## 32935 1 voted 0.000003570
## 32936 2 voted 0.000040200
## 32937 1 voter 0.000039300
## 32938 2 voter 0.000003660
## 32939 1 votre 0.000003570
## 32940 2 votre 0.000113352
## 32941 1 vou 0.000074900
## 32942 2 vou 0.000003660
## 32943 1 vouch 0.000003570
## 32944 2 vouch 0.000076800
## 32945 1 voucher 0.000467450
## 32946 2 voucher 0.000003660
## 32947 1 vouchers 0.000431767
## 32948 2 vouchers 0.000003660
## 32949 1 vous 0.000003570
## 32950 2 vous 0.000442439
## 32951 1 vowing 0.000003570
## 32952 2 vowing 0.000040200
## 32953 1 voy 0.000003570
## 32954 2 voy 0.000113352
## 32955 1 voyage 0.000146301
## 32956 2 voyage 0.000003660
## 32957 1 voyagé 0.000039300
## 32958 2 voyagé 0.000003660
## 32959 1 vpmvkdtfz0 0.000003570
## 32960 2 vpmvkdtfz0 0.000040200
## 32961 1 vpohndcwc9 0.000003570
## 32962 2 vpohndcwc9 0.000040200
## 32963 1 vqdjg4qbui 0.000039300
## 32964 2 vqdjg4qbui 0.000003660
## 32965 1 vqpb2uyzww 0.000003570
## 32966 2 vqpb2uyzww 0.000040200
## 32967 1 vrc150 0.000003570
## 32968 2 vrc150 0.000040200
## 32969 1 vrienden 0.000003570
## 32970 2 vrienden 0.000186483
## 32971 1 vrij 0.000003570
## 32972 2 vrij 0.000040200
## 32973 1 vrisq2kwep 0.000003570
## 32974 2 vrisq2kwep 0.000040200
## 32975 1 vroeger 0.000003570
## 32976 2 vroeger 0.000040200
## 32977 1 vrpdk5oq1k 0.000003570
## 32978 2 vrpdk5oq1k 0.000040200
## 32979 1 vsi 0.000039300
## 32980 2 vsi 0.000003660
## 32981 1 vsuau23 0.000039300
## 32982 2 vsuau23 0.000003660
## 32983 1 vsv6ku2hkw 0.000003570
## 32984 2 vsv6ku2hkw 0.000040200
## 32985 1 vsyraf 0.000039300
## 32986 2 vsyraf 0.000003660
## 32987 1 vt 0.000039300
## 32988 2 vt 0.000003660
## 32989 1 vtc8td8emm 0.000003570
## 32990 2 vtc8td8emm 0.000040200
## 32991 1 vte2wa5izl 0.000003570
## 32992 2 vte2wa5izl 0.000040200
## 32993 1 vty703rwut 0.000039300
## 32994 2 vty703rwut 0.000003660
## 32995 1 vuela 0.000039300
## 32996 2 vuela 0.000003660
## 32997 1 vuelen 0.000039300
## 32998 2 vuelen 0.000003660
## 32999 1 vueles 0.000003570
## 33000 2 vueles 0.000076800
## 33001 1 vueling 0.000003570
## 33002 2 vueling 0.003696743
## 33003 1 vuelingairlines 0.000039300
## 33004 2 vuelingairlines 0.000003660
## 33005 1 vuelingclients 0.000289034
## 33006 2 vuelingclients 0.000003660
## 33007 1 vuelingfacts 0.000003570
## 33008 2 vuelingfacts 0.000076800
## 33009 1 vuelingofertas 0.000039300
## 33010 2 vuelingofertas 0.000003660
## 33011 1 vuelingsucks 0.000039300
## 33012 2 vuelingsucks 0.000003660
## 33013 1 vuelo 0.000003570
## 33014 2 vuelo 0.000808091
## 33015 1 vuelos 0.001145430
## 33016 2 vuelos 0.000040200
## 33017 1 vuelvo 0.000039300
## 33018 2 vuelvo 0.000003660
## 33019 1 vuestro 0.000003570
## 33020 2 vuestro 0.000040200
## 33021 1 vujee 0.000039300
## 33022 2 vujee 0.000003660
## 33023 1 vwx1q4lmef 0.000003570
## 33024 2 vwx1q4lmef 0.000040200
## 33025 1 vx231 0.000003570
## 33026 2 vx231 0.000040200
## 33027 1 vxarx6pxlr 0.000003570
## 33028 2 vxarx6pxlr 0.000040200
## 33029 1 vxcardenvy 0.000110618
## 33030 2 vxcardenvy 0.000003660
## 33031 1 vxnewlook 0.000003570
## 33032 2 vxnewlook 0.000076800
## 33033 1 vxpride 0.000039300
## 33034 2 vxpride 0.000003660
## 33035 1 vxsvjer1gg 0.000039300
## 33036 2 vxsvjer1gg 0.000003660
## 33037 1 vy3003 0.000003570
## 33038 2 vy3003 0.000040200
## 33039 1 vy6601pmo 0.000003570
## 33040 2 vy6601pmo 0.000040200
## 33041 1 vygrokyajp 0.000003570
## 33042 2 vygrokyajp 0.000040200
## 33043 1 vyus6rhv4a 0.000039300
## 33044 2 vyus6rhv4a 0.000003660
## 33045 1 vz7dyixyll 0.000003570
## 33046 2 vz7dyixyll 0.000040200
## 33047 1 vzhpvwyx8d 0.000003570
## 33048 2 vzhpvwyx8d 0.000040200
## 33049 1 vzla 0.000003570
## 33050 2 vzla 0.000076800
## 33051 1 vzubtczyvc 0.000039300
## 33052 2 vzubtczyvc 0.000003660
## 33053 1 w0kc4h70cn 0.000003570
## 33054 2 w0kc4h70cn 0.000040200
## 33055 1 w1wzpbiexh 0.000039300
## 33056 2 w1wzpbiexh 0.000003660
## 33057 1 w2iuieah6y 0.000003570
## 33058 2 w2iuieah6y 0.000040200
## 33059 1 w3uijtkui1 0.000039300
## 33060 2 w3uijtkui1 0.000003660
## 33061 1 w3zxvgvkg8 0.000003570
## 33062 2 w3zxvgvkg8 0.000040200
## 33063 1 w43jbmxtyv 0.000039300
## 33064 2 w43jbmxtyv 0.000003660
## 33065 1 w64cjsvlda 0.000039300
## 33066 2 w64cjsvlda 0.000003660
## 33067 1 w7t86ldxw3 0.000039300
## 33068 2 w7t86ldxw3 0.000003660
## 33069 1 waaaaay 0.000003570
## 33070 2 waaaaay 0.000040200
## 33071 1 waahzombie 0.000039300
## 33072 2 waahzombie 0.000003660
## 33073 1 waar 0.000146301
## 33074 2 waar 0.000003660
## 33075 1 waardeloos 0.000003570
## 33076 2 waardeloos 0.000040200
## 33077 1 waarderen 0.000039300
## 33078 2 waarderen 0.000003660
## 33079 1 waarom 0.000003570
## 33080 2 waarom 0.000149917
## 33081 1 waarzeggers 0.000003570
## 33082 2 waarzeggers 0.000040200
## 33083 1 wac 0.000039300
## 33084 2 wac 0.000003660
## 33085 1 wachten 0.000003570
## 33086 2 wachten 0.000040200
## 33087 1 wae 0.000039300
## 33088 2 wae 0.000003660
## 33089 1 waffles 0.000039300
## 33090 2 waffles 0.000003660
## 33091 1 wage 0.000039300
## 33092 2 wage 0.000003660
## 33093 1 wages 0.000003570
## 33094 2 wages 0.000040200
## 33095 1 wagging 0.000003570
## 33096 2 wagging 0.000040200
## 33097 1 wait 0.002715491
## 33098 2 wait 0.000003660
## 33099 1 waited 0.000289034
## 33100 2 waited 0.000149917
## 33101 1 waitin 0.000039300
## 33102 2 waitin 0.000003660
## 33103 1 waiting 0.002858224
## 33104 2 waiting 0.000149917
## 33105 1 waitingforever 0.000039300
## 33106 2 waitingforever 0.000003660
## 33107 1 waitley 0.000003570
## 33108 2 waitley 0.000040200
## 33109 1 waive 0.000003570
## 33110 2 waive 0.000040200
## 33111 1 waived 0.000074900
## 33112 2 waived 0.000003660
## 33113 1 wake 0.000039300
## 33114 2 wake 0.000003660
## 33115 1 waktu 0.000039300
## 33116 2 waktu 0.000003660
## 33117 1 wala 0.000039300
## 33118 2 wala 0.000003660
## 33119 1 walcott 0.000003570
## 33120 2 walcott 0.000040200
## 33121 1 wales 0.000003570
## 33122 2 wales 0.000040200
## 33123 1 walk 0.000146301
## 33124 2 walk 0.000003660
## 33125 1 walked 0.000003570
## 33126 2 walked 0.000076800
## 33127 1 walkmans 0.000039300
## 33128 2 walkmans 0.000003660
## 33129 1 wall 0.000039300
## 33130 2 wall 0.000003660
## 33131 1 wallet 0.000039300
## 33132 2 wallet 0.000003660
## 33133 1 wallmart 0.000003570
## 33134 2 wallmart 0.000040200
## 33135 1 wally97 0.000003570
## 33136 2 wally97 0.000040200
## 33137 1 walmsleyr 0.000003570
## 33138 2 walmsleyr 0.000040200
## 33139 1 walter 0.000039300
## 33140 2 walter 0.000003660
## 33141 1 wan 0.000074900
## 33142 2 wan 0.000003660
## 33143 1 wana 0.000039300
## 33144 2 wana 0.000003660
## 33145 1 wanderngaramean 0.000181984
## 33146 2 wanderngaramean 0.000003660
## 33147 1 wank 0.000039300
## 33148 2 wank 0.000003660
## 33149 1 wanna 0.000467450
## 33150 2 wanna 0.000003660
## 33151 1 wanneer 0.000003570
## 33152 2 wanneer 0.000040200
## 33153 1 wante 0.000039300
## 33154 2 wante 0.000003660
## 33155 1 wanttogohome 0.000039300
## 33156 2 wanttogohome 0.000003660
## 33157 1 war 0.000074900
## 33158 2 war 0.000003660
## 33159 1 waren 0.000003570
## 33160 2 waren 0.000040200
## 33161 1 warkentin 0.000003570
## 33162 2 warkentin 0.000040200
## 33163 1 warm 0.000110618
## 33164 2 warm 0.000003660
## 33165 1 warming 0.000039300
## 33166 2 warming 0.000003660
## 33167 1 warmth 0.000003570
## 33168 2 warmth 0.000040200
## 33169 1 warn 0.000074900
## 33170 2 warn 0.000003660
## 33171 1 warning 0.000003570
## 33172 2 warning 0.000076800
## 33173 1 warns 0.000003570
## 33174 2 warns 0.000040200
## 33175 1 warp 0.000039300
## 33176 2 warp 0.000003660
## 33177 1 warrior6delta 0.000039300
## 33178 2 warrior6delta 0.000003660
## 33179 1 wash 0.000039300
## 33180 2 wash 0.000003660
## 33181 1 washington 0.000003570
## 33182 2 washington 0.000186483
## 33183 1 washtimes 0.000039300
## 33184 2 washtimes 0.000003660
## 33185 1 wasn’t 0.000039300
## 33186 2 wasn’t 0.000003660
## 33187 1 wasnt 0.000039300
## 33188 2 wasnt 0.000003660
## 33189 1 waste 0.000003570
## 33190 2 waste 0.000223048
## 33191 1 wasted 0.000110618
## 33192 2 wasted 0.000003660
## 33193 1 wasteoftime 0.000003570
## 33194 2 wasteoftime 0.000040200
## 33195 1 wasting 0.000003570
## 33196 2 wasting 0.000040200
## 33197 1 wastingmytime 0.000003570
## 33198 2 wastingmytime 0.000040200
## 33199 1 wat 0.000003570
## 33200 2 wat 0.000661830
## 33201 1 watch 0.000003570
## 33202 2 watch 0.001246874
## 33203 1 watched 0.000003570
## 33204 2 watched 0.000113352
## 33205 1 watchg 0.000003570
## 33206 2 watchg 0.000040200
## 33207 1 watching 0.000324717
## 33208 2 watching 0.000040200
## 33209 1 water 0.000396083
## 33210 2 water 0.000003660
## 33211 1 waterlooairshow 0.000039300
## 33212 2 waterlooairshow 0.000003660
## 33213 1 watersinmn 0.000039300
## 33214 2 watersinmn 0.000003660
## 33215 1 waterwayrealty 0.000003570
## 33216 2 waterwayrealty 0.000040200
## 33217 1 waterwhirled 0.000039300
## 33218 2 waterwhirled 0.000003660
## 33219 1 wavdxme6zm 0.000003570
## 33220 2 wavdxme6zm 0.000040200
## 33221 1 wave 0.000003570
## 33222 2 wave 0.000040200
## 33223 1 waving 0.000039300
## 33224 2 waving 0.000003660
## 33225 1 wayne 0.000003570
## 33226 2 wayne 0.000040200
## 33227 1 wayofthewong 0.000039300
## 33228 2 wayofthewong 0.000003660
## 33229 1 wayyyy 0.000039300
## 33230 2 wayyyy 0.000003660
## 33231 1 wba5txx6vt 0.000039300
## 33232 2 wba5txx6vt 0.000003660
## 33233 1 wbcsays 0.000039300
## 33234 2 wbcsays 0.000003660
## 33235 1 wbrettwilson 0.000003570
## 33236 2 wbrettwilson 0.000040200
## 33237 1 wbtd5dvniq 0.000003570
## 33238 2 wbtd5dvniq 0.000040200
## 33239 1 wbtma1l4qg 0.000039300
## 33240 2 wbtma1l4qg 0.000003660
## 33241 1 wbvt_98fm 0.000039300
## 33242 2 wbvt_98fm 0.000003660
## 33243 1 wc 0.000003570
## 33244 2 wc 0.000040200
## 33245 1 wc14 0.000003570
## 33246 2 wc14 0.000040200
## 33247 1 wca2mgvxof 0.000039300
## 33248 2 wca2mgvxof 0.000003660
## 33249 1 wcn0mhyppk 0.000039300
## 33250 2 wcn0mhyppk 0.000003660
## 33251 1 wd 0.000003570
## 33252 2 wd 0.000040200
## 33253 1 we’ll 0.000003570
## 33254 2 we’ll 0.000040200
## 33255 1 we’re 0.000181984
## 33256 2 we’re 0.000003660
## 33257 1 we’ve 0.000003570
## 33258 2 we’ve 0.000040200
## 33259 1 we9awfqndm 0.000039300
## 33260 2 we9awfqndm 0.000003660
## 33261 1 weak 0.000003570
## 33262 2 weak 0.000040200
## 33263 1 weakest 0.000039300
## 33264 2 weakest 0.000003660
## 33265 1 wear 0.000074900
## 33266 2 wear 0.000003660
## 33267 1 wearebuild 0.000039300
## 33268 2 wearebuild 0.000003660
## 33269 1 wearing 0.000074900
## 33270 2 wearing 0.000149917
## 33271 1 weaseled 0.000003570
## 33272 2 weaseled 0.000040200
## 33273 1 weather 0.001466579
## 33274 2 weather 0.000003660
## 33275 1 weatherbenice 0.000003570
## 33276 2 weatherbenice 0.000040200
## 33277 1 weatherchannel 0.000039300
## 33278 2 weatherchannel 0.000003660
## 33279 1 web 0.000003570
## 33280 2 web 0.000552135
## 33281 1 webcare 0.000003570
## 33282 2 webcare 0.000040200
## 33283 1 webpage 0.000039300
## 33284 2 webpage 0.000003660
## 33285 1 website 0.002108877
## 33286 2 website 0.000003660
## 33287 1 webtracer 0.000003570
## 33288 2 webtracer 0.000040200
## 33289 1 weckjyjxrn 0.000039300
## 33290 2 weckjyjxrn 0.000003660
## 33291 1 wecome 0.000039300
## 33292 2 wecome 0.000003660
## 33293 1 wed 0.000003570
## 33294 2 wed 0.000149917
## 33295 1 wedding 0.000396083
## 33296 2 wedding 0.000040200
## 33297 1 wederom 0.000003570
## 33298 2 wederom 0.000040200
## 33299 1 wednesday 0.000003570
## 33300 2 wednesday 0.000186483
## 33301 1 wedstrijd 0.000039300
## 33302 2 wedstrijd 0.000003660
## 33303 1 wedstrijden 0.000003570
## 33304 2 wedstrijden 0.000040200
## 33305 1 weechookeong 0.000003570
## 33306 2 weechookeong 0.000040200
## 33307 1 weed 0.000039300
## 33308 2 weed 0.000003660
## 33309 1 weeee 0.000003570
## 33310 2 weeee 0.000040200
## 33311 1 week 0.000146301
## 33312 2 week 0.001795352
## 33313 1 week’s 0.000003570
## 33314 2 week’s 0.000076800
## 33315 1 weekdays 0.000039300
## 33316 2 weekdays 0.000003660
## 33317 1 weekend 0.000110618
## 33318 2 weekend 0.000661830
## 33319 1 weekend's 0.000039300
## 33320 2 weekend's 0.000003660
## 33321 1 weekendhasbegun 0.000003570
## 33322 2 weekendhasbegun 0.000040200
## 33323 1 weekends 0.000003570
## 33324 2 weekends 0.000040200
## 33325 1 weeklong 0.000039300
## 33326 2 weeklong 0.000003660
## 33327 1 weekly 0.000039300
## 33328 2 weekly 0.000003660
## 33329 1 weeks 0.000931331
## 33330 2 weeks 0.000003660
## 33331 1 weer 0.000003570
## 33332 2 weer 0.000186483
## 33333 1 weet 0.000039300
## [ reached 'max' / getOption("max.print") -- omitted 2595 rows ]# Start with the topics output from the LDA run
lda_topics %>%
# Arrange the topics by word probabilities in descending order
arrange(desc(beta))## topic term beta
## 1 1 flight 0.034366481
## 2 1 time 0.010173278
## 3 2 service 0.008815872
## 4 1 plane 0.006890424
## 5 1 trip 0.006141077
## 6 2 customer 0.006036916
## 7 2 airline 0.005963786
## 8 1 delayed 0.005712879
## 9 1 hours 0.005320364
## 10 1 day 0.004999215
## 11 1 3 0.004963532
## 12 1 airport 0.004463967
## 13 1 bag 0.004428284
## 14 1 entered 0.004428284
## 15 1 gate 0.004071452
## 16 1 getaway 0.004071452
## 17 1 check 0.003821670
## 18 1 luggage 0.003821670
## 19 1 delay 0.003785986
## 20 1 hour 0.003750303
## 21 2 vueling 0.003696743
## 22 1 hey 0.003678937
## 23 2 airlines 0.003660178
## 24 1 chance 0.003536204
## 25 2 love 0.003513917
## 26 1 cancelled 0.003500521
## 27 2 nice 0.003367656
## 28 2 travel 0.003221395
## 29 1 lost 0.003215055
## 30 1 people 0.003215055
## 31 1 summer 0.003036639
## 32 2 flypal 0.003002004
## 33 2 con 0.002965439
## 34 2 ticket 0.002965439
## 35 2 home 0.002928873
## 36 2 se 0.002928873
## 37 1 britishairways 0.002893907
## 38 2 indonesiagaruda 0.002892308
## 39 1 waiting 0.002858224
## 40 2 change 0.002855743
## 41 1 summergetaway 0.002822540
## 42 1 worldmarket 0.002822540
## 43 1 free 0.002786857
## 44 1 sweeps 0.002786857
## 45 2 class 0.002782613
## 46 1 days 0.002751174
## 47 1 wait 0.002715491
## 48 2 amigos 0.002709482
## 49 2 adios 0.002672917
## 50 1 told 0.002644124
## 51 1 bags 0.002572758
## 52 2 hope 0.002563221
## 53 2 nedmex 0.002563221
## 54 2 turkishairlines 0.002563221
## 55 1 airasia 0.002537075
## 56 1 seat 0.002537075
## 57 2 email 0.002526656
## 58 1 flyfrontier 0.002501392
## 59 1 hold 0.002465708
## 60 1 seats 0.002465708
## 61 2 por 0.002453526
## 62 2 tamairlines 0.002453526
## 63 1 pay 0.002430025
## 64 1 tomorrow 0.002430025
## 65 2 air 0.002416960
## 66 2 mas 0.002416960
## 67 1 20 0.002394342
## 68 2 worst 0.002343830
## 69 1 guys 0.002287293
## 70 2 phone 0.002270700
## 71 1 bad 0.002251609
## 72 1 boarding 0.002251609
## 73 1 crew 0.002215926
## 74 1 minutes 0.002215926
## 75 1 5 0.002180243
## 76 1 sitting 0.002180243
## 77 2 world 0.002161004
## 78 2 gt 0.002124439
## 79 1 call 0.002108877
## 80 1 website 0.002108877
## 81 2 online 0.002087874
## 82 1 6 0.002037510
## 83 2 baggage 0.002014743
## 84 1 30 0.002001827
## 85 1 book 0.002001827
## 86 2 booked 0.001978178
## 87 1 agent 0.001930461
## 88 1 staff 0.001930461
## 89 2 10 0.001905047
## 90 1 happy 0.001894777
## 91 2 dm 0.001868482
## 92 2 lol 0.001868482
## 93 2 sas 0.001868482
## 94 1 board 0.001859094
## 95 2 customers 0.001831917
## 96 2 frontiercare 0.001831917
## 97 2 morning 0.001831917
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## [ reached 'max' / getOption("max.print") -- omitted 2595 rows ]Summarizing topics
# Produce a grouped summary of the LDA output by topic
lda_topics %>%
group_by(topic) %>%
summarize(
# Calculate the sum of the word probabilities
sum = sum(beta),
# Count the number of terms
n = n()
)## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 3
## topic sum n
## <int> <dbl> <int>
## 1 1 1.00 17964
## 2 2 1.00 17964Visualizing topics
word_probs <- lda_topics %>%
# Keep the top 10 highest word probabilities by topic
group_by(topic) %>%
top_n(10, beta) %>%
ungroup() %>%
# Create term2, a factor ordered by word probability
mutate(term2 = fct_reorder(term, beta))
# Plot term2 and the word probabilities
ggplot(word_probs, aes(x = term2, y = beta)) +
geom_col() +
# Facet the bar plot by topic
facet_wrap(~ topic, scales = "free") +
coord_flip()
Document term matrices
[Video]
Creating a DTM
# Start with the tidied Twitter data
tidy_twitter %>%
# Count each word used in each tweet
count(word, tweet_id) %>%
# Use the word counts by tweet to create a DTM
cast_dtm(tweet_id, word, n)## <<DocumentTermMatrix (documents: 7044, terms: 17997)>>
## Non-/sparse entries: 61957/126708911
## Sparsity : 100%
## Maximal term length: 44
## Weighting : term frequency (tf)Evaluating a DTM as a matrix
# Assign the DTM to dtm_twitter
dtm_twitter <- tidy_twitter_subset %>%
count(word, tweet_id) %>%
# Cast the word counts by tweet into a DTM
cast_dtm(tweet_id, word, n)
# Coerce dtm_twitter into a matrix called matrix_twitter
matrix_twitter <- as.matrix(dtm_twitter)
# Print rows 1 through 5 and columns 90 through 95
matrix_twitter[1:5, 90:95]## Terms
## Docs upload version vuelos wait weird yesterday
## 4.7688e+17 0 0 0 1 0 0
## 4.76889e+17 0 0 1 0 0 0
## 4.76881e+17 0 0 0 0 1 1
## 4.76884e+17 0 0 0 0 0 0
## 4.76885e+17 0 0 0 0 0 0Running topic models
[Video]
Fitting an LDA
# Load the topicmodels package
library(topicmodels)
# Cast the word counts by tweet into a DTM
dtm_twitter <- tidy_twitter %>%
count(word, tweet_id) %>%
cast_dtm(tweet_id, word, n)
# Run an LDA with 2 topics and a Gibbs sampler
lda_out <- LDA(
dtm_twitter,
k = 2,
method = "Gibbs",
control = list(seed = 42)
)Tidying LDA output
# Glimpse the topic model output
glimpse(lda_out)## Formal class 'LDA_Gibbs' [package "topicmodels"] with 16 slots
## ..@ seedwords : NULL
## ..@ z : int [1:63633] 1 1 2 1 1 1 1 2 1 1 ...
## ..@ alpha : num 25
## ..@ call : language LDA(x = dtm_twitter, k = 2, method = "Gibbs", control = list(seed = 42))
## ..@ Dim : int [1:2] 7044 17997
## ..@ control :Formal class 'LDA_Gibbscontrol' [package "topicmodels"] with 14 slots
## ..@ k : int 2
## ..@ terms : chr [1:17997] "_adowaa_" "_arzar" "_austrian" "_bbbb_" ...
## ..@ documents : chr [1:7044] "486973619952971776" "478816318784036864" "477008545637224448" "477077022695768064" ...
## ..@ beta : num [1:2, 1:17997] -10.3 -12.8 -10.3 -12.8 -12.7 ...
## ..@ gamma : num [1:7044, 1:2] 0.509 0.578 0.483 0.491 0.475 ...
## ..@ wordassignments:List of 5
## .. ..$ i : int [1:61957] 1 1 1 2 2 2 2 2 2 2 ...
## .. ..$ j : int [1:61957] 1 4659 16984 2 1510 9108 9487 9693 10326 11634 ...
## .. ..$ v : num [1:61957] 1 1 2 1 1 1 1 2 1 1 ...
## .. ..$ nrow: int 7044
## .. ..$ ncol: int 17997
## .. ..- attr(*, "class")= chr "simple_triplet_matrix"
## ..@ loglikelihood : num -519525
## ..@ iter : int 2000
## ..@ logLiks : num(0)
## ..@ n : int 63633# Tidy the matrix of word probabilities
lda_topics <- lda_out %>%
tidy(matrix = "beta")
# Arrange the topics by word probabilities in descending order
lda_topics %>%
arrange(desc(beta))## # A tibble: 35,994 x 3
## topic term beta
## <int> <chr> <dbl>
## 1 1 t.co 0.0429
## 2 1 http 0.0415
## 3 2 flight 0.0286
## 4 1 klm 0.0277
## 5 2 united 0.0261
## 6 2 americanair 0.0239
## 7 1 de 0.0145
## 8 1 southwestair 0.0144
## 9 1 delta 0.0141
## 10 2 usairways 0.0137
## # … with 35,984 more rowsComparing LDA output
# Run an LDA with 3 topics and a Gibbs sampler
lda_out2 <- LDA(
dtm_twitter,
k = 3,
method = "Gibbs",
control = list(seed = 42)
)
# Tidy the matrix of word probabilities
lda_topics2 <- lda_out2 %>%
tidy(matrix = "beta")
# Arrange the topics by word probabilities in descending order
lda_topics2 %>%
arrange(desc(beta))## # A tibble: 53,991 x 3
## topic term beta
## <int> <chr> <dbl>
## 1 1 t.co 0.0612
## 2 1 http 0.0596
## 3 2 klm 0.0409
## 4 3 flight 0.0399
## 5 3 united 0.0374
## 6 3 americanair 0.0327
## 7 1 delta 0.0254
## 8 2 de 0.0211
## 9 1 southwestair 0.0207
## 10 3 usairways 0.0197
## # … with 53,981 more rowsInterpreting topics
[Video]
Naming three topics
# Select the top 15 terms by topic and reorder term
word_probs2 <- lda_topics2 %>%
group_by(topic) %>%
top_n(15, beta) %>%
ungroup() %>%
mutate(term2 = fct_reorder(term, beta))
# Plot word probs, color and facet based on topic
ggplot(
word_probs2,
aes(term2, beta, fill = as.factor(topic))
) +
geom_col(show.legend = FALSE) +
facet_wrap(~ topic, scales = "free") +
coord_flip()
Naming four topics
# Select the top 15 terms by topic and reorder term
word_probs3 <- lda_topics3 %>%
group_by(topic) %>%
top_n(15, beta) %>%
ungroup() %>%
mutate(term2 = fct_reorder(term, beta))
# Plot word_probs3, color and facet based on topic
ggplot(
word_probs3,
aes(term2, beta, fill = as.factor(topic))
) +
geom_col(show.legend = FALSE) +
facet_wrap(~ topic, scales = "free") +
coord_flip()
Wrap-up
[Video]
About Michael Mallari
Michael is a hybrid thinker and doer—a byproduct of being a CliftonStrengths “Learner” over time. With 20+ years of engineering, design, and product experience, he helps organizations identify market needs, mobilize internal and external resources, and deliver delightful digital customer experiences that align with business goals. He has been entrusted with problem-solving for brands—ranging from Fortune 500 companies to early-stage startups to not-for-profit organizations.
Michael earned his BS in Computer Science from New York Institute of Technology and his MBA from the University of Maryland, College Park. He is also a candidate to receive his MS in Applied Analytics from Columbia University.
LinkedIn | Twitter | www.michaelmallari.com/data | www.columbia.edu/~mm5470