8.6 Twitter data
We the twitter.json data accessed from here. This is a JSON file (.json) downloaded from the Twitter stream API.
8.6.2 Create a corpus
## Corpus consisting of 7,504 documents and 42 docvars.
## twitter.json.1 :
## "@EFC_Jayy UKIP"
##
## twitter.json.2 :
## "RT @Corbynator2:@jeremycorbyn Reaction from people at the Wa..."
##
## twitter.json.3 :
## "RT @ryvr: Stephen Hawking, the world’s smartest man, backs J..."
##
## twitter.json.4 :
## "RT @TheGreenParty: How you cast your vote will shape the fut..."
##
## twitter.json.5 :
## "RT @UKLabour: #VoteLabour today for a fairer Britain. Find o..."
##
## twitter.json.6 :
## "RT @WestmonsterUK: POLLS: FINAL DAY AVERAGE CON 43.8% LAB 35..."
##
## [ reached max_ndoc ... 7,498 more documents ]8.6.2.1 Summary
## Corpus consisting of 7504 documents, showing 10 documents:
##
## Text Types Tokens Sentences retweet_count favorite_count favorited
## twitter.json.1 2 2 1 0 0 FALSE
## twitter.json.2 24 30 3 90 108 FALSE
## twitter.json.3 15 16 1 78 104 FALSE
## twitter.json.4 20 23 3 244 218 FALSE
## twitter.json.5 23 23 2 1896 2217 FALSE
## twitter.json.6 18 22 1 55 52 FALSE
## twitter.json.7 28 34 2 65 73 FALSE
## twitter.json.8 23 23 1 30 9 FALSE
## twitter.json.9 23 23 2 1896 2217 FALSE
## twitter.json.10 26 27 2 0 0 FALSE
## truncated id_str in_reply_to_screen_name
## FALSE 872596537142116352 EFC_Jayy
## FALSE 872596536869363712 <NA>
## FALSE 872596537444093952 <NA>
## FALSE 872596538492637185 <NA>
## FALSE 872596538480103425 <NA>
## FALSE 872596538815651841 <NA>
## FALSE 872596537376989185 <NA>
## FALSE 872596538366849024 <NA>
## FALSE 872596538811457540 <NA>
## TRUE 872596537632804864 <NA>
## source
## <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>
## <a href="http://twitter.com" rel="nofollow">Twitter Web Client</a>
## <a href="http://twitter.com" rel="nofollow">Twitter Web Client</a>
## <a href="http://twitter.com/download/android" rel="nofollow">Twitter for Android</a>
## <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>
## <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>
## <a href="http://twitter.com/download/android" rel="nofollow">Twitter for Android</a>
## <a href="http://twitter.com" rel="nofollow">Twitter Web Client</a>
## <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>
## <a href="http://twitter.com/download/android" rel="nofollow">Twitter for Android</a>
## retweeted created_at in_reply_to_status_id_str
## FALSE Wed Jun 07 23:30:01 +0000 2017 872596176834572288
## FALSE Wed Jun 07 23:30:01 +0000 2017 <NA>
## FALSE Wed Jun 07 23:30:01 +0000 2017 <NA>
## FALSE Wed Jun 07 23:30:01 +0000 2017 <NA>
## FALSE Wed Jun 07 23:30:01 +0000 2017 <NA>
## FALSE Wed Jun 07 23:30:01 +0000 2017 <NA>
## FALSE Wed Jun 07 23:30:01 +0000 2017 <NA>
## FALSE Wed Jun 07 23:30:01 +0000 2017 <NA>
## FALSE Wed Jun 07 23:30:01 +0000 2017 <NA>
## FALSE Wed Jun 07 23:30:01 +0000 2017 <NA>
## in_reply_to_user_id_str lang listed_count verified
## 4556760676 en 1 FALSE
## <NA> en 28 FALSE
## <NA> en 2 FALSE
## <NA> en 3 FALSE
## <NA> en 6 FALSE
## <NA> en 2 FALSE
## <NA> en 12 FALSE
## <NA> en 90 FALSE
## <NA> en 1 FALSE
## <NA> en 25 FALSE
## location user_id_str
## Japan 863929468984995840
## Gondwana 153295243
## LDN rt/mention/follow/link ≠ e 273731990
## East, England 477177095
## London 241664322
## AKast UDI RN PRI Evopoli FKast 811768906301968384
## Liverpool 2157745426
## Social Affairs Editor: Scisco 586255942
## <NA> 4551111388
## Glasgow, Scotland 704227420
## description
## <NA>
## #Black. #Green. #Red. #Aboriginal. #Environmental. #Socialist. #Atheist.
## Infovore, atheist, post-Ⓐ, p/t nihilist, lifelong radiophile, aspiring cultural terrorist 🏴 ☮️ 🇵🇸 \nImages: @laborglitch/Airside\nM: openlyclassist@x0r.be
## think outside your own perspective and find transcendence bypassing state opulence
## 🇩🇿🇲🇦
## REPUBLICA LIBERTARIA NO ➕BUROCRACIA ESTATISTA MONOPOLISTA IMPRODUCTIVA 🇦🇷🇨🇱🇺🇾🇫🇷🇺🇸🇪🇸PROTOPÍA #STOPCastrituyente #PiñeraPresidente
## LFC. Lets get Labour back in power! Its not left vs right, its right vs wrong. Tories are morally and spiritually bankrupt. #JC4PM
## Public interest issues, policy, human rights & social sciences. Researcher and writer for: Politics and Insights (home site), @Welfare_Weekly, @SciscoMedia.
## <NA>
## artist. sick and tired of not being allowed an opinion in a so called free country! if I offend thee...you can fuck right off you CUNT! I thank you *takes bow*
## geo_enabled user_created_at statuses_count followers_count
## FALSE Mon May 15 01:30:11 +0000 2017 2930 367
## FALSE Tue Jun 08 05:05:23 +0000 2010 10223 845
## FALSE Tue Mar 29 01:59:32 +0000 2011 20934 761
## FALSE Sat Jan 28 22:41:07 +0000 2012 13603 321
## TRUE Sat Jan 22 20:53:11 +0000 2011 13179 386
## FALSE Thu Dec 22 03:02:43 +0000 2016 52384 792
## FALSE Mon Oct 28 21:16:49 +0000 2013 8572 361
## FALSE Mon May 21 04:46:43 +0000 2012 33759 4475
## TRUE Sun Dec 20 23:27:00 +0000 2015 171 21
## TRUE Tue Oct 08 00:09:09 +0000 2013 21568 837
## favourites_count protected user_url
## 1260 FALSE <NA>
## 9813 FALSE <NA>
## 14733 FALSE <NA>
## 5421 FALSE http://www.instagram.com/stuhornett/
## 5219 FALSE <NA>
## 6 FALSE <NA>
## 11592 FALSE <NA>
## 18368 FALSE https://kittysjones.wordpress.com/
## 120 FALSE <NA>
## 12177 FALSE <NA>
## name time_zone user_lang utc_offset friends_count screen_name
## ジョージ <NA> en NA 304 CoysJoji
## Yara-ma-yha-who London en 3600 439 Unkle_Ken
## Openly classist London en 3600 2761 OpenlyClassist
## Stu Casablanca en 0 767 StuHornett
## Ousama Dublin en 3600 257 ousamaZ18
## Capitán Tatán <NA> es NA 353 omentando
## Kev <NA> en NA 357 YahMelia
## RevolutionBreeze London en 3600 4999 suejonessays
## Paul <NA> en-GB NA 77 probinson63
## Lady Jan(e) Gray Edinburgh en 3600 1263 jansengray_jo
## country_code country place_type full_name place_name place_id place_lat
## <NA> <NA> NA <NA> <NA> <NA> NaN
## <NA> <NA> NA <NA> <NA> <NA> NaN
## <NA> <NA> NA <NA> <NA> <NA> NaN
## <NA> <NA> NA <NA> <NA> <NA> NaN
## <NA> <NA> NA <NA> <NA> <NA> NaN
## <NA> <NA> NA <NA> <NA> <NA> NaN
## <NA> <NA> NA <NA> <NA> <NA> NaN
## <NA> <NA> NA <NA> <NA> <NA> NaN
## <NA> <NA> NA <NA> <NA> <NA> NaN
## <NA> <NA> NA <NA> <NA> <NA> NaN
## place_lon lat lon
## NaN NA NA
## NaN NA NA
## NaN NA NA
## NaN NA NA
## NaN NA NA
## NaN NA NA
## NaN NA NA
## NaN NA NA
## NaN NA NA
## NaN NA NA
## expanded_url
## <NA>
## <NA>
## http://www.independent.co.uk/news/science/stephen-hawking-jeremy-corbyn-labour-theresa-may-conservatives-endorsement-general-election-a7774016.html
## <NA>
## http://bit.ly/2s0qDvb
## http://www.westmonster.com/final-polls-verdict/
## <NA>
## <NA>
## http://bit.ly/2s0qDvb
## https://twitter.com/i/web/status/872596537632804864
## url
## <NA>
##
## https://t.co/2kl3ayLd44
## <NA>
## https://t.co/953WYc2p0c
## https://t.co/6zBHeHWuru
##
##
## https://t.co/953WYc2p0c
## https://t.co/qYdu0hUeey8.6.2.2 Accessing parts of corpus
## Corpus consisting of 7,504 documents and 42 docvars.
## twitter.json.1 :
## "@EFC_Jayy UKIP"
##
## twitter.json.2 :
## "RT @Corbynator2:@jeremycorbyn Reaction from people at the Wa..."
##
## twitter.json.3 :
## "RT @ryvr: Stephen Hawking, the world’s smartest man, backs J..."
##
## twitter.json.4 :
## "RT @TheGreenParty: How you cast your vote will shape the fut..."
##
## twitter.json.5 :
## "RT @UKLabour: #VoteLabour today for a fairer Britain. Find o..."
##
## twitter.json.6 :
## "RT @WestmonsterUK: POLLS: FINAL DAY AVERAGE CON 43.8% LAB 35..."
##
## [ reached max_ndoc ... 7,498 more documents ]8.6.2.3 Document-level information
## retweet_count favorite_count favorited truncated id_str
## 1 0 0 FALSE FALSE 872596537142116352
## 2 90 108 FALSE FALSE 872596536869363712
## 3 78 104 FALSE FALSE 872596537444093952
## 4 244 218 FALSE FALSE 872596538492637185
## 5 1896 2217 FALSE FALSE 872596538480103425
## 6 55 52 FALSE FALSE 872596538815651841
## in_reply_to_screen_name
## 1 EFC_Jayy
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## source
## 1 <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>
## 2 <a href="http://twitter.com" rel="nofollow">Twitter Web Client</a>
## 3 <a href="http://twitter.com" rel="nofollow">Twitter Web Client</a>
## 4 <a href="http://twitter.com/download/android" rel="nofollow">Twitter for Android</a>
## 5 <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>
## 6 <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>
## retweeted created_at in_reply_to_status_id_str
## 1 FALSE Wed Jun 07 23:30:01 +0000 2017 872596176834572288
## 2 FALSE Wed Jun 07 23:30:01 +0000 2017 <NA>
## 3 FALSE Wed Jun 07 23:30:01 +0000 2017 <NA>
## 4 FALSE Wed Jun 07 23:30:01 +0000 2017 <NA>
## 5 FALSE Wed Jun 07 23:30:01 +0000 2017 <NA>
## 6 FALSE Wed Jun 07 23:30:01 +0000 2017 <NA>
## in_reply_to_user_id_str lang listed_count verified
## 1 4556760676 en 1 FALSE
## 2 <NA> en 28 FALSE
## 3 <NA> en 2 FALSE
## 4 <NA> en 3 FALSE
## 5 <NA> en 6 FALSE
## 6 <NA> en 2 FALSE
## location user_id_str
## 1 Japan 863929468984995840
## 2 Gondwana 153295243
## 3 LDN rt/mention/follow/link ≠ e 273731990
## 4 East, England 477177095
## 5 London 241664322
## 6 AKast UDI RN PRI Evopoli FKast 811768906301968384
## description
## 1 <NA>
## 2 #Black. #Green. #Red. #Aboriginal. #Environmental. #Socialist. #Atheist.
## 3 Infovore, atheist, post-Ⓐ, p/t nihilist, lifelong radiophile, aspiring cultural terrorist 🏴 ☮️ 🇵🇸 \nImages: @laborglitch/Airside\nM: openlyclassist@x0r.be
## 4 think outside your own perspective and find transcendence bypassing state opulence
## 5 🇩🇿🇲🇦
## 6 REPUBLICA LIBERTARIA NO ➕BUROCRACIA ESTATISTA MONOPOLISTA IMPRODUCTIVA 🇦🇷🇨🇱🇺🇾🇫🇷🇺🇸🇪🇸PROTOPÍA #STOPCastrituyente #PiñeraPresidente
## geo_enabled user_created_at statuses_count followers_count
## 1 FALSE Mon May 15 01:30:11 +0000 2017 2930 367
## 2 FALSE Tue Jun 08 05:05:23 +0000 2010 10223 845
## 3 FALSE Tue Mar 29 01:59:32 +0000 2011 20934 761
## 4 FALSE Sat Jan 28 22:41:07 +0000 2012 13603 321
## 5 TRUE Sat Jan 22 20:53:11 +0000 2011 13179 386
## 6 FALSE Thu Dec 22 03:02:43 +0000 2016 52384 792
## favourites_count protected user_url
## 1 1260 FALSE <NA>
## 2 9813 FALSE <NA>
## 3 14733 FALSE <NA>
## 4 5421 FALSE http://www.instagram.com/stuhornett/
## 5 5219 FALSE <NA>
## 6 6 FALSE <NA>
## name time_zone user_lang utc_offset friends_count screen_name
## 1 ジョージ <NA> en NA 304 CoysJoji
## 2 Yara-ma-yha-who London en 3600 439 Unkle_Ken
## 3 Openly classist London en 3600 2761 OpenlyClassist
## 4 Stu Casablanca en 0 767 StuHornett
## 5 Ousama Dublin en 3600 257 ousamaZ18
## 6 Capitán Tatán <NA> es NA 353 omentando
## country_code country place_type full_name place_name place_id place_lat
## 1 <NA> <NA> NA <NA> <NA> <NA> NaN
## 2 <NA> <NA> NA <NA> <NA> <NA> NaN
## 3 <NA> <NA> NA <NA> <NA> <NA> NaN
## 4 <NA> <NA> NA <NA> <NA> <NA> NaN
## 5 <NA> <NA> NA <NA> <NA> <NA> NaN
## 6 <NA> <NA> NA <NA> <NA> <NA> NaN
## place_lon lat lon
## 1 NaN NA NA
## 2 NaN NA NA
## 3 NaN NA NA
## 4 NaN NA NA
## 5 NaN NA NA
## 6 NaN NA NA
## expanded_url
## 1 <NA>
## 2 <NA>
## 3 http://www.independent.co.uk/news/science/stephen-hawking-jeremy-corbyn-labour-theresa-may-conservatives-endorsement-general-election-a7774016.html
## 4 <NA>
## 5 http://bit.ly/2s0qDvb
## 6 http://www.westmonster.com/final-polls-verdict/
## url
## 1 <NA>
## 2
## 3 https://t.co/2kl3ayLd44
## 4 <NA>
## 5 https://t.co/953WYc2p0c
## 6 https://t.co/6zBHeHWuru8.6.3 Tokens
tokens() segments texts in a corpus into tokens (words or sentences) by word boundaries.
We can remove punctuations or not
8.6.3.1 With punctuations
## Tokens consisting of 7,504 documents and 42 docvars.
## twitter.json.1 :
## [1] "@EFC_Jayy" "UKIP"
##
## twitter.json.2 :
## [1] "RT" "@Corbynator2" ":" "@jeremycorbyn"
## [5] "Reaction" "from" "people" "at"
## [9] "the" "Watford" "Rally" ":"
## [ ... and 18 more ]
##
## twitter.json.3 :
## [1] "RT" "@ryvr" ":" "Stephen" "Hawking" ","
## [7] "the" "world’s" "smartest" "man" "," "backs"
## [ ... and 4 more ]
##
## twitter.json.4 :
## [1] "RT" "@TheGreenParty" ":" "How"
## [5] "you" "cast" "your" "vote"
## [9] "will" "shape" "the" "future"
## [ ... and 11 more ]
##
## twitter.json.5 :
## [1] "RT" "@UKLabour" ":" "#VoteLabour" "today"
## [6] "for" "a" "fairer" "Britain" "."
## [11] "Find" "out"
## [ ... and 11 more ]
##
## twitter.json.6 :
## [1] "RT" "@WestmonsterUK" ":" "POLLS"
## [5] ":" "FINAL" "DAY" "AVERAGE"
## [9] "CON" "43.8" "%" "LAB"
## [ ... and 10 more ]
##
## [ reached max_ndoc ... 7,498 more documents ]8.6.3.2 Without punctuations
dat_twitter_corpus_tok_no_punct <- tokens(dat_twitter_corpus, remove_punct = TRUE)
dat_twitter_corpus_tok_no_punct## Tokens consisting of 7,504 documents and 42 docvars.
## twitter.json.1 :
## [1] "@EFC_Jayy" "UKIP"
##
## twitter.json.2 :
## [1] "RT" "@Corbynator2" "@jeremycorbyn" "Reaction"
## [5] "from" "people" "at" "the"
## [9] "Watford" "Rally" "We" "believe"
## [ ... and 10 more ]
##
## twitter.json.3 :
## [1] "RT" "@ryvr"
## [3] "Stephen" "Hawking"
## [5] "the" "world’s"
## [7] "smartest" "man"
## [9] "backs" "Jeremy"
## [11] "Corbyn" "https://t.co/2kl3ayLd44"
## [ ... and 1 more ]
##
## twitter.json.4 :
## [1] "RT" "@TheGreenParty" "How" "you"
## [5] "cast" "your" "vote" "will"
## [9] "shape" "the" "future" "Every"
## [ ... and 6 more ]
##
## twitter.json.5 :
## [1] "RT" "@UKLabour" "#VoteLabour" "today" "for"
## [6] "a" "fairer" "Britain" "Find" "out"
## [11] "where" "to"
## [ ... and 7 more ]
##
## twitter.json.6 :
## [1] "RT" "@WestmonsterUK" "POLLS" "FINAL"
## [5] "DAY" "AVERAGE" "CON" "43.8"
## [9] "LAB" "35.4" "LD" "8"
## [ ... and 4 more ]
##
## [ reached max_ndoc ... 7,498 more documents ]8.6.4 Stop words
It is best to remove stop words (function/grammatical words) when we use statistical analyses of a corpus.
dat_twitter_corpus_tok_no_punct_no_Stop <- tokens_select(dat_twitter_corpus_tok_no_punct, pattern = stopwords("en", source = "stopwords-iso"), selection = "remove")
dat_twitter_corpus_tok_no_punct_no_Stop## Tokens consisting of 7,504 documents and 42 docvars.
## twitter.json.1 :
## [1] "@EFC_Jayy" "UKIP"
##
## twitter.json.2 :
## [1] "RT" "@Corbynator2" "@jeremycorbyn" "Reaction"
## [5] "people" "Watford" "Rally" "Jeremy"
## [9] "Corbyn" "Jeremy" "#GE2017" "#GE17"
## [ ... and 2 more ]
##
## twitter.json.3 :
## [1] "RT" "@ryvr"
## [3] "Stephen" "Hawking"
## [5] "world’s" "smartest"
## [7] "Jeremy" "Corbyn"
## [9] "https://t.co/2kl3ayLd44" "#TuesdayThoughts"
##
## twitter.json.4 :
## [1] "RT" "@TheGreenParty"
## [3] "cast" "vote"
## [5] "shape" "future"
## [7] "single" "vote"
## [9] "counts" "Tomorrow"
## [11] "#VoteGreen2017" "https://t.co/QgEdxfnDCK"
##
## twitter.json.5 :
## [1] "RT" "@UKLabour"
## [3] "#VoteLabour" "fairer"
## [5] "Britain" "vote"
## [7] "share" "↓"
## [9] "https://t.co/953WYc2p0c" "https://t.co/kDnXiJ"
##
## twitter.json.6 :
## [1] "RT" "@WestmonsterUK" "POLLS" "FINAL"
## [5] "DAY" "AVERAGE" "43.8" "LAB"
## [9] "35.4" "LD" "8" "UKIP"
## [ ... and 3 more ]
##
## [ reached max_ndoc ... 7,498 more documents ]8.6.5 Statistical analyses
We can start by providing statistics (whether descriptives or inferential) based on our corpora.
8.6.5.1 Simple frequency analysis
Here we look at obtaining a simple frequency analysis of usage.
8.6.5.1.1 DFM
We start by generating a DFM (document-feature matrix)
dat_twitter_corpus_tok_no_punct_no_Stop_dfm <- dfm(dat_twitter_corpus_tok_no_punct_no_Stop)
dat_twitter_corpus_tok_no_punct_no_Stop_dfm## Document-feature matrix of: 7,504 documents, 12,235 features (99.92% sparse) and 42 docvars.
## features
## docs @efc_jayy ukip rt @corbynator2 @jeremycorbyn reaction people
## twitter.json.1 1 1 0 0 0 0 0
## twitter.json.2 0 0 1 1 1 1 1
## twitter.json.3 0 0 1 0 0 0 0
## twitter.json.4 0 0 1 0 0 0 0
## twitter.json.5 0 0 1 0 0 0 0
## twitter.json.6 0 1 1 0 0 0 0
## features
## docs watford rally jeremy
## twitter.json.1 0 0 0
## twitter.json.2 1 1 2
## twitter.json.3 0 0 1
## twitter.json.4 0 0 0
## twitter.json.5 0 0 0
## twitter.json.6 0 0 0
## [ reached max_ndoc ... 7,498 more documents, reached max_nfeat ... 12,225 more features ]8.6.5.1.2 Frequencies
dat_twitter_corpus_tok_no_punct_no_Stop_dfm_freq <- textstat_frequency(dat_twitter_corpus_tok_no_punct_no_Stop_dfm)
dat_twitter_corpus_tok_no_punct_no_Stop_dfm_freq## feature frequency rank docfreq group
## 1 rt 5827 1 5591 all
## 2 #ge2017 2483 2 2481 all
## 3 vote 1886 3 1708 all
## 4 #votelabour 1495 4 1493 all
## 5 @uklabour 1479 5 1470 all
## 6 conservatives 959 6 957 all
## 7 ↓ 792 7 792 all
## 8 labour 774 8 757 all
## 9 share 649 9 647 all
## 10 britain 639 10 639 all
## 11 tomorrow 571 11 563 all
## 12 fairer 571 11 571 all
## 13 voting 570 13 521 all
## 14 https://t.co/953wyc2p0c 560 14 560 all
## 15 https://t.co/kdnxij 560 14 560 all
## 16 amp 542 16 474 all
## 17 corbyn 516 17 515 all
## 18 @jeremycorbyn 498 18 498 all
## 19 #generalelection17 451 19 451 all
## 20 election 424 20 419 all
## 21 people 389 21 373 all
## 22 tories 378 22 372 all
## 23 #ge17 375 23 375 all
## 24 + 364 24 207 all
## 25 @conservatives 312 25 311 all
## 26 theresa 295 26 292 all
## 27 tory 279 27 270 all
## 28 win 275 28 274 all
## 29 ukip 274 29 270 all
## 30 https 271 30 271 all
## 31 1 265 31 179 all
## 32 jeremy 256 32 251 all
## 33 watch 253 33 246 all
## 34 final 242 34 240 all
## 35 2 224 35 141 all
## 36 poll 206 36 178 all
## 37 3 200 37 194 all
## 38 talk 197 38 159 all
## 39 friends 197 38 193 all
## 40 support 195 40 194 all
## 41 #forthemany 193 41 193 all
## 42 nhs 181 42 172 all
## 43 party 180 43 179 all
## 44 campaign 179 44 178 all
## 45 lab 177 45 171 all
## 46 amusing 177 45 177 all
## 47 standards 176 47 176 all
## 48 polls 174 48 170 all
## 49 pretty 172 49 172 all
## 50 @corbynator2 171 50 171 all
## 51 @chrislhayes 171 50 171 all
## 52 behavior 167 52 167 all
## 53 polling 166 53 162 all
## 54 @thesnp 165 54 164 all
## 55 self-professed 164 55 164 all
## 56 appropriateness 164 55 164 all
## 57 spiral 164 55 164 all
## 58 downward 164 55 164 all
## 59 @trobinsonnewera 162 59 161 all
## 60 drinking 152 60 76 all
## 61 5 151 61 145 all
## 62 #generalelection 142 62 142 all
## 63 hope 141 63 139 all
## 64 day 139 64 138 all
## 65 @britainelects 138 65 138 all
## 66 @theresa_may 133 66 132 all
## 67 rights 132 67 125 all
## 68 tax 130 68 126 all
## 69 @hackneyabbott 129 69 128 all
## 70 luck 122 70 122 all
## 71 #jc4pm 122 70 122 all
## 72 @libdems 119 72 118 all
## 73 #votesnp 119 72 119 all
## 74 human 117 74 112 all
## 75 conservative 116 75 111 all
## 76 #labour 115 76 115 all
## 77 8 115 76 114 all
## 78 time 114 78 109 all
## 79 westminster 113 79 112 all
## 80 country 113 79 112 all
## 81 @socialistvoice 110 81 110 all
## 82 brexit 109 82 105 all
## 83 political 106 83 106 all
## 84 choice 104 84 104 all
## 85 terrorist 103 85 101 all
## 86 night 102 86 102 all
## 87 voters 102 86 101 all
## 88 manifesto 99 88 92 all
## 89 @survation 98 89 98 all
## 90 live 98 89 98 all
## 91 rally 97 91 87 all
## 92 13 97 91 97 all
## 93 check 97 91 95 all
## 94 change 97 91 97 all
## 95 british 96 95 95 all
## 96 #voteconservative 92 96 92 all
## 97 question 92 96 92 all
## 98 😂 91 98 36 all
## 99 london 91 98 89 all
## 100 snp 90 100 89 all
## 101 public 88 101 64 all
## 102 future 86 102 84 all
## 103 hours 86 102 85 all
## 104 remember 86 102 85 all
## 105 ldem 85 105 85 all
## 106 intention 84 106 84 all
## 107 @jeremycorbyn4pm 84 106 84 all
## 108 neighbour 83 108 83 all
## 109 40 81 109 78 all
## 110 grn 81 109 81 all
## 111 game 80 111 80 all
## 112 students 79 112 79 all
## 113 june 77 113 77 all
## 114 scotland 77 113 76 all
## 115 #brexit 77 113 77 all
## 116 @skwawkbox 77 113 77 all
## 117 @natesilver538 76 117 76 all
## 118 @annawcp 76 117 76 all
## 119 numb 76 117 76 all
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8.6.5.2 Lexical diversity
We can compute the lexical diversity in a document. This is a measure allowing us to provide a statistical account of diversity in the choice of lexical items in a text. See the different measures implemented here
8.6.5.2.1 TTR (Type-Token Ratio)
8.6.5.2.1.1 Computing TTR
dat_twitter_corpus_tok_no_punct_no_Stop_dfm_tstat_lexdiv_ttr <- textstat_lexdiv(dat_twitter_corpus_tok_no_punct_no_Stop_dfm, measure = "TTR")
head(dat_twitter_corpus_tok_no_punct_no_Stop_dfm_tstat_lexdiv_ttr, 5)## document TTR
## 1 twitter.json.1 1.0000000
## 2 twitter.json.2 0.9230769
## 3 twitter.json.3 1.0000000
## 4 twitter.json.4 0.9090909
## 5 twitter.json.5 1.00000008.6.5.2.1.2 Plotting TTR
plot(dat_twitter_corpus_tok_no_punct_no_Stop_dfm_tstat_lexdiv_ttr$TTR, type = "l", xaxt = "n", xlab = NULL, ylab = "TTR")
grid()
axis(1, at = seq_len(nrow(dat_twitter_corpus_tok_no_punct_no_Stop_dfm_tstat_lexdiv_ttr)), labels = dat_twitter_corpus_tok_no_punct_no_Stop_dfm_tstat_lexdiv_ttr$document)
8.6.5.2.2 CTTR (Corrected Type-Token Ratio)
8.6.5.2.2.1 Computing CTTR
dat_twitter_corpus_tok_no_punct_no_Stop_dfm_tstat_lexdiv_cttr <- textstat_lexdiv(dat_twitter_corpus_tok_no_punct_no_Stop_dfm, measure = "CTTR")
head(dat_twitter_corpus_tok_no_punct_no_Stop_dfm_tstat_lexdiv_cttr, 5)## document CTTR
## 1 twitter.json.1 1.000000
## 2 twitter.json.2 2.353394
## 3 twitter.json.3 2.121320
## 4 twitter.json.4 2.132007
## 5 twitter.json.5 1.8708298.6.5.2.2.2 Plotting TTR
plot(dat_twitter_corpus_tok_no_punct_no_Stop_dfm_tstat_lexdiv_cttr$CTTR, type = "l", xaxt = "n", xlab = NULL, ylab = "CTTR")
grid()
axis(1, at = seq_len(nrow(dat_twitter_corpus_tok_no_punct_no_Stop_dfm_tstat_lexdiv_cttr)), labels = dat_twitter_corpus_tok_no_punct_no_Stop_dfm_tstat_lexdiv_cttr$document)
8.6.5.2.3 K (Yule’s K)
8.6.5.2.3.1 Computing K
dat_twitter_corpus_tok_no_punct_no_Stop_dfm_tstat_lexdiv_K <- textstat_lexdiv(dat_twitter_corpus_tok_no_punct_no_Stop_dfm, measure = "K")
head(dat_twitter_corpus_tok_no_punct_no_Stop_dfm_tstat_lexdiv_K, 5)## document K
## 1 twitter.json.1 0.0000
## 2 twitter.json.2 118.3432
## 3 twitter.json.3 0.0000
## 4 twitter.json.4 165.2893
## 5 twitter.json.5 0.00008.6.5.2.3.2 Plotting K
plot(dat_twitter_corpus_tok_no_punct_no_Stop_dfm_tstat_lexdiv_K$K, type = "l", xaxt = "n", xlab = NULL, ylab = expression(italic(K)))
grid()
axis(1, at = seq_len(nrow(dat_twitter_corpus_tok_no_punct_no_Stop_dfm_tstat_lexdiv_K)), labels = dat_twitter_corpus_tok_no_punct_no_Stop_dfm_tstat_lexdiv_K$document)
8.6.5.3 Keyness - relative frequency analysis
The relative frequency analysis allows to provide a statistical analysis of frequent words as a function of a target reference level. For this dataset, we do not have a specific target. Hence the comparison is done based on the full dataset.
8.6.5.3.1 Computing keyness
dat_twitter_corpus_tok_no_punct_no_Stop_dfm_tstat_key <- textstat_keyness(dat_twitter_corpus_tok_no_punct_no_Stop_dfm)
head(dat_twitter_corpus_tok_no_punct_no_Stop_dfm_tstat_key, 10)## feature chi2 p n_target n_reference
## 1 @efc_jayy 2.475875e+03 0.000000e+00 1 3
## 2 ukip 3.528582e+01 2.846981e-09 1 273
## 3 #039 -2.523627e-05 9.959918e-01 0 1
## 4 #4many -2.523627e-05 9.959918e-01 0 1
## 5 #50 -2.523627e-05 9.959918e-01 0 1
## 6 #5moreyears -2.523627e-05 9.959918e-01 0 1
## 7 #abbot -2.523627e-05 9.959918e-01 0 1
## 8 #accountability -2.523627e-05 9.959918e-01 0 1
## 9 #ace -2.523627e-05 9.959918e-01 0 1
## 10 #activism -2.523627e-05 9.959918e-01 0 1
8.6.5.4 Collocations - scoring multi-word expressions
A collocation analysis is a way to identify contiguous collocations of words, i.e., multi-word expressions. Depending on the language, these can be identified based on capitalisation (e.g., proper names) as in English texts. However, this is not the same across languages.
We look for capital letters in our text. The result provides Wald’s Lamda and z statistics. Usually, any z value higher or equal to 2 is statistically significant. To compute p values, we use the probability of a normal distribution based on a mean of 0 and an SD of 1. This is appended to the table.
dat_twitter_corpus_tok_no_punct_no_Stop_tstat_col_caps <- tokens_select(dat_twitter_corpus_tok_no_punct_no_Stop, pattern = "^[A-Z]", valuetype = "regex", case_insensitive = FALSE, padding = TRUE) %>% textstat_collocations(min_count = 10) %>% mutate(p_value = 1 - pnorm(z, 0, 1))
dat_twitter_corpus_tok_no_punct_no_Stop_tstat_col_caps## collocation count count_nested length lambda z
## 1 jeremy corbyn 176 0 2 6.182393 42.218248
## 2 london check 59 0 2 8.333997 29.588186
## 3 british polling 50 0 2 8.631779 25.878777
## 4 VOTE VOTE 27 0 2 5.901117 23.386710
## 5 jeremy corbyn's 31 0 2 5.566790 22.319301
## 6 qriously british 50 0 2 9.313865 22.192370
## 7 cor-byn ohh 21 0 2 8.066542 21.755299
## 8 hey london 60 0 2 10.127519 21.112422
## 9 polling council 50 0 2 10.593825 20.687270
## 10 diane abbott 17 0 2 9.439982 18.597275
## 11 islington ohh 13 0 2 6.795293 18.491436
## 12 POLL vote 23 0 2 5.210318 18.457502
## 13 theresa may's 27 0 2 6.885338 17.308094
## 14 election day 14 0 2 8.598223 16.969754
## 15 labour tories 43 0 2 2.807819 16.790348
## 16 vote labour 42 0 2 2.735443 16.238365
## 17 corbyn's THERESA 27 0 2 9.445852 16.091863
## 18 snow jeremy 15 0 2 5.917992 15.345651
## 19 labour john 19 0 2 4.863777 14.440510
## 20 fuck theresa 10 0 2 4.940903 12.941056
## 21 shadow secretary 18 0 2 11.821105 12.640321
## 22 human rights 18 0 2 12.188858 12.552120
## 23 share RT 11 0 2 6.436435 12.319489
## 24 conservatives liberals 10 0 2 4.264936 11.396895
## 25 watch corbyn 10 0 2 4.643320 11.388633
## 26 winston churchill 10 0 2 12.210388 11.327697
## 27 britain share 11 0 2 5.673323 10.176563
## 28 norstat qriously 50 0 2 14.090556 9.496325
## 29 surveymonkey norstat 50 0 2 15.389894 9.389525
## 30 UC berkeley 46 0 2 15.307429 9.336253
## 31 sues UC 46 0 2 15.307429 9.336253
## 32 ohh je-re-my 42 0 2 15.217537 9.277934
## 33 tory socialist 14 0 2 7.898721 9.184902
## 34 THERESA TERRIFIED 27 0 2 13.935102 9.144721
## 35 jon snow 27 0 2 14.271588 9.143238
## 36 facebook live 53 0 2 13.178696 9.120978
## 37 defending hackney 19 0 2 13.927928 8.895874
## 38 liberal democrats 19 0 2 12.972361 8.735761
## 39 union chapel 16 0 2 14.271756 8.641955
## 40 paris agreement 24 0 2 12.469565 8.574443
## 41 john lewis 19 0 2 12.401747 8.481123
## 42 absolute monarchy 12 0 2 13.994180 8.443905
## 43 stormzy shut 10 0 2 13.309015 8.425364
## 44 campus breitbart 23 0 2 11.873456 8.222646
## 45 MILO fan 46 0 2 16.406055 8.181051
## 46 fan sues 46 0 2 16.406055 8.181051
## 47 je-re-my cor-byn 42 0 2 16.316163 8.134178
## 48 national insurance 10 0 2 12.085155 8.124028
## 49 POLL UPCOMING 23 0 2 11.645982 8.081906
## 50 lib dem 12 0 2 11.725315 7.998497
## 51 WIDE POLL 23 0 2 11.460756 7.964360
## 52 SAMPLE SIZE 30 0 2 15.984553 7.959707
## 53 carbon tax 13 0 2 11.614168 7.955598
## 54 david cameron's 10 0 2 11.550989 7.851791
## 55 ARTICLE WIDE 23 0 2 15.723924 7.820460
## 56 UPCOMING ARTICLE 23 0 2 15.723924 7.820460
## 57 boris johnson 10 0 2 11.421750 7.778978
## 58 chapel islington 16 0 2 11.195548 7.741550
## 59 prime minister 19 0 2 15.537394 7.719358
## 60 income tax 10 0 2 11.204629 7.652121
## 61 september theresa 67 0 2 10.822512 7.614649
## 62 baroness cavendish 10 0 2 14.918480 7.371982
## 63 abuse conservatives 46 0 2 9.694710 6.814486
## 64 conservatives campus 46 0 2 9.687351 6.809343
## 65 demand conservatives 22 0 2 8.911683 6.229097
## 66 backstabbing conservatives 13 0 2 8.380261 5.815766
## 67 compare corbyn 12 0 2 8.189202 5.675412
## 68 labour comedian 10 0 2 7.727688 5.336713
## 69 labour conservatives 12 0 2 1.113263 3.845712
## p_value
## 1 0.000000e+00
## 2 0.000000e+00
## 3 0.000000e+00
## 4 0.000000e+00
## 5 0.000000e+00
## 6 0.000000e+00
## 7 0.000000e+00
## 8 0.000000e+00
## 9 0.000000e+00
## 10 0.000000e+00
## 11 0.000000e+00
## 12 0.000000e+00
## 13 0.000000e+00
## 14 0.000000e+00
## 15 0.000000e+00
## 16 0.000000e+00
## 17 0.000000e+00
## 18 0.000000e+00
## 19 0.000000e+00
## 20 0.000000e+00
## 21 0.000000e+00
## 22 0.000000e+00
## 23 0.000000e+00
## 24 0.000000e+00
## 25 0.000000e+00
## 26 0.000000e+00
## 27 0.000000e+00
## 28 0.000000e+00
## 29 0.000000e+00
## 30 0.000000e+00
## 31 0.000000e+00
## 32 0.000000e+00
## 33 0.000000e+00
## 34 0.000000e+00
## 35 0.000000e+00
## 36 0.000000e+00
## 37 0.000000e+00
## 38 0.000000e+00
## 39 0.000000e+00
## 40 0.000000e+00
## 41 0.000000e+00
## 42 0.000000e+00
## 43 0.000000e+00
## 44 1.110223e-16
## 45 1.110223e-16
## 46 1.110223e-16
## 47 2.220446e-16
## 48 2.220446e-16
## 49 3.330669e-16
## 50 6.661338e-16
## 51 7.771561e-16
## 52 8.881784e-16
## 53 8.881784e-16
## 54 1.998401e-15
## 55 2.664535e-15
## 56 2.664535e-15
## 57 3.663736e-15
## 58 4.884981e-15
## 59 5.884182e-15
## 60 9.880985e-15
## 61 1.321165e-14
## 62 8.404388e-14
## 63 4.730105e-12
## 64 4.902301e-12
## 65 2.345649e-10
## 66 3.017834e-09
## 67 6.917752e-09
## 68 4.732325e-08
## 69 6.010137e-058.6.5.5 Word clouds
We can use word clouds of the top 100 words
set.seed(132)
dat_twitter_corpus_tok_no_punct_no_Stop_dfm %>%
textplot_wordcloud(max_words = 100, color = brewer.pal(8, "Dark2"))
8.6.5.6 Network of feature co-occurrences
A Network of feature co-occurrences allows to obtain association plot of word usage. We use an fcm (feature co-occurrence matrix) based on our DFM.
set.seed(144)
dat_twitter_corpus_tok_no_punct_no_Stop_dfm %>%
dfm_trim(min_termfreq = 500) %>%
textplot_network(min_freq = 0.5)
8.6.5.7 Poisson regression
Finally, we run a GLM with a poisson family to evaluate the significance level of our most frequent words.
8.6.5.7.1 Computing GLM
dat_twitter_corpus_GLM <- dat_twitter_corpus_tok_no_punct_no_Stop_dfm_freq %>%
filter(frequency >= 500) %>%
glm(frequency ~ feature, data = ., family = "poisson")
summary(dat_twitter_corpus_GLM)##
## Call:
## glm(formula = frequency ~ feature, family = "poisson", data = .)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 7.81722 0.02007 389.530 <2e-16 ***
## feature#votelabour -0.50734 0.03274 -15.498 <2e-16 ***
## feature@uklabour -0.51810 0.03285 -15.774 <2e-16 ***
## feature↓ -1.14266 0.04081 -28.000 <2e-16 ***
## featureamp -1.52196 0.04741 -32.102 <2e-16 ***
## featurebritain -1.35732 0.04436 -30.599 <2e-16 ***
## featureconservatives -0.95133 0.03802 -25.022 <2e-16 ***
## featurecorbyn -1.57112 0.04838 -32.474 <2e-16 ***
## featurefairer -1.46983 0.04641 -31.669 <2e-16 ***
## featurehttps://t.co/953wyc2p0c -1.48929 0.04678 -31.835 <2e-16 ***
## featurehttps://t.co/kdnxij -1.48929 0.04678 -31.835 <2e-16 ***
## featurelabour -1.16565 0.04117 -28.315 <2e-16 ***
## featurert 0.85303 0.02397 35.594 <2e-16 ***
## featureshare -1.34179 0.04409 -30.436 <2e-16 ***
## featuretomorrow -1.46983 0.04641 -31.669 <2e-16 ***
## featurevote -0.27501 0.03054 -9.004 <2e-16 ***
## featurevoting -1.47159 0.04644 -31.685 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1.4696e+04 on 16 degrees of freedom
## Residual deviance: -3.9257e-13 on 0 degrees of freedom
## AIC: 181.25
##
## Number of Fisher Scoring iterations: 2
