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Who is talking about the French Open?
2017-06-10
rtweet
Twitter
NLP
cld2
cld3
language-detection
hrbrthemes
rolandgarros
true

I don't think rOpenSci's Jeroen Ooms can ever top the coolness of his magick package but I have to admit other things he's developped are not bad at all. He's recently been working on interfaces to Google compact language detectors 2 and 3 (the latter being more experimental). I saw this cool use case and started thinking about other possible applications of the packages.

I was very sad when I realized it was too late to try and download tweets about the Eurovision song context but then I also remembered there's this famous tennis tournament going on right now, about which people probably tweet in various languages. I don't follow the French Open myself, but it seemed interesting to find out which languages were the most prevalent, and whether the results from the cld2 and cld3 packages are similar and whether they're similar to the language detection results from Twitter itself.

Getting the tweets

I'm using my usual rtweet recipe. I no longer need to open my eyes when downloading tweets.

rg_tweets <- rtweet::search_tweets(q = "#RolandGarros2017",
                                   include_rts = FALSE,
                                   n = 18000)
save(rg_tweets, file = "data/2017-06-07-rolandgarros.RData")

I got 18000 tweets.

Using the language detectors

I decided to first clean the tweets a bit, removing hashtags, mentions, and at least part of the links.

rg_tweets <- dplyr::mutate(rg_tweets,
                           text = stringr::str_replace_all(text, "#.*$", ""),
                           text = stringr::str_replace_all(text, "https.*$", ""),
                           text = stringr::str_replace_all(text, "@.*$", ""),
                           text = stringr::str_replace_all(text, "#.* ", ""),
                           text = stringr::str_replace_all(text, "https.* ", ""),
                           text = stringr::str_replace_all(text, "@.* ", ""))

Today I'm a happy naive user of language detectors, but more technical details can be found in their respective README's. It'd be difficult to have an easier interface than the two cld2 and cld3 packages. They're also fast, although I haven't timed the following chunk so you'll have to believe me or test the packages yourselves.

Note that cld2 and cld3 both have functions for outputting several languages instead of one, with the associated reliability, but I won't use them since I want a direct comparison with the Twitter output of one language per tweet.

rg_tweets <- dplyr::mutate(rg_tweets, 
                           cld2_language = cld2::detect_language(text,
                                                                 lang_code = TRUE), 
                           cld3_language = cld3::detect_language(text))

Before analysing the results, I'll transform the Twitter detected language a bit: it's not NA for undertemined language, it's "und".

rg_tweets <- dplyr::mutate(rg_tweets, 
                           lang = ifelse(lang == "und", NA, lang))

Looking at detected languages

Twitter output a language for 96% of the tweets, cld2 for 59% and cld3 for 84% of them.

Let's see a few cases in which Twitter outputs a language whereas the other don't.

As a side note, I learnt how to insert a DT::datatable from Daniela Vázquez after admiring one she had put in this cool blog post. This was an adventure in htmlwidgets hell. I started looking at Daniela's Github blog repo, then talked with her on the R-Ladies slack, and I told her I'd do more the next day. I woke up to a PR solving all problems! Thanks a lot Daniela, and also thanks to your husband! Update: Daniela's husband Gervasio came up with a fix not involving custom Jekyll plugins after I realized my site couldn't be built on Github pages... I am so thankful for their help!

library("magrittr")
rg_tweets %>%
  dplyr::filter(is.na(cld2_language), is.na(cld3_language)) %>%
  dplyr::select(text, lang, cld2_language,
         cld3_language)%>%
  head(n = 50) %>%
  DT::datatable()
<script type="application/json" data-for="htmlwidget-63daaefa0d92bf46c034">{"x":{"filter":"none","data":[["1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33","34","35","36","37","38","39","40","41","42","43","44","45","46","47","48","49","50"],["Congratulations to ","Verdasco y Zimonjic se quedan a las puertas de la final de dobles\nhttps://t.co/Mm3nnAhfpo\n\n","Next up :\nSimona Halep v Karolina Pliskova\n","攼㹤愼㸰戼㹣攼㹤戼㸷戼㸱攼㹤愼㸰戼㹣攼㹤戼㸷戼㹢攼㹤愼㸰戼㹣攼㹤戼㹥戼㹥 Jelena Ostapenko avanzó a su 1er final al vencer 7-6 3-6 6-3 a Timea Bacsinszky ","Enorme ","OSTAPENKOOOO BEBEEE 攼㹤愼㸰戼㹤攼㹤戼㸴愼㸵攼㹤愼㸰戼㹤攼㹤戼㸲㤼㸸攼㹤愼㸰戼㹤攼㹤戼㸲㤼㸸 cote a 2,5 qui passe on continue a encaisser la famille !! 攼㹤愼㸰戼㹤攼㹤戼㸲戼㸰攼㹤愼㸰戼㹤攼㹤戼㸲戼㸰 ","Listen ","TIMEA 攼㹤愼㸰戼㹤攼㹤戼㸸愼㹤攼㹤愼㸰戼㹤攼㹤戼㸸愼㹤攼㹤愼㸰戼㹤攼㹤戼㸸愼㹤攼㹤愼㸰戼㹤攼㹤戼㸸愼㹤攼㹤愼㸰戼㹤攼㹤戼㸸愼㹤攼㹤愼㸰戼㹤攼㹤戼㸸愼㹤攼㹤愼㸰戼㹤攼㹤戼㸸愼㹤攼㹤愼㸰戼㹤攼㹤戼㸸愼㹤攼㹤愼㸰戼㹤攼㹤戼㸸愼㹤 ","OsTaPenKo!!!! Fantastika!! 6dien svetki!! Finals!!! ","Merci ","YEEEES BRAVO! LA FINALE !!! :D ","Jelena Ostapenko - French Open Finalist. ","Bravo ","Ostapenko WINS !!!!!! ","Goooo 攼㹤愼㸰戼㹣攼㹤戼㹦㠼㸶攼㹤愼㸰戼㹤攼㹤戼㸴㤼㹤\n\n\n","Jelena strong\n","ostapenko-bacsinszky muazzam maç oluyor... ","Congratulations ","A la final!!! Bien ","Eyes on ","[攼㹤愼㸰戼㹣攼㹤戼㹥戼㹥\n1 set partout entre Bacsinszky et Ostapenko ! (6/7 6/3)","Belle demie ! ","Enorme Santyyy ","Grande ","A un game de la final! ","Going 3 ","Lache pas Ostapenko !!!! ","played!\n\n\n","Congratulations ","Set González / Young por 7-5. Verdasco y Zimonjic al tercero ","Unranked Latvian ","BBCSport: \n\n攼㹤愼㸰戼㹤攼㹤戼㸱㠼㸹 ","Congratulations to ","Dominic thiem攼㹤愼㸰戼㹤攼㹤戼㸲愼㹡攼㹤愼㸰戼㹤攼㹤戼㸲愼㹡\n","Anyway ","Merci ","Rhoooo pas de ","Alizé Lim 攼㹤愼㸰戼㹤攼㹤戼㸸㠼㹤 ","攼㹤愼㸰戼㹣攼㹤戼㹥戼㹥 Bet on ","Congratulations ","Congratulations 攼㹤愼㸰戼㹣攼㹤戼㹦㠼㸶攼㹤愼㸰戼㹣攼㹤戼㹥戼㹥\n攼㹤愼㸰戼㹣攼㹤戼㸷愼㹥攼㹤愼㸰戼㹣攼㹤戼㸷戼㸳\n\n","TENNIS ","Respect ","Congratulations ","Congratulations ","Robert Farah \n ","Por favorrrrrr no digan la S en Roland Garros, suena horribleeeeeee!!!! ","Congratulations ","Congratulations ","Congratulations \n\n\n"],["en","es","cs","ro","es","fr","en","en","sl","fr","it","eu","pt","pl",null,"en","tr","en","es","en","fi","fr","en","es","es","en","ht","en","en","es","en","en","en","en","en","fr","fr","ht","en","en","en","is","en","en","en","es","es","en","en","en"],[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null],[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null]],"container":"\n \n \n
<\/th>\n text<\/th>\n lang<\/th>\n cld2_language<\/th>\n cld3_language<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"order":[],"autoWidth":false,"orderClasses":false,"columnDefs":[{"orderable":false,"targets":0}]}},"evals":[],"jsHooks":[]}</script>

It seems that the languages with undetermined languages via cld2 and cld3 are quite short. The Twitter language detector might be more focused at short sentences which well given the length of tweets wouldn't be surprising. Moreover, maybe it often inputs a language even when uncertain. If we take the example of the word "Merci", it's French but also used in Catalan at least in Barcelona, so to me that seems uncertain. Some other tweets to which Twitter but not the other language detectors associated a language are a mix of languages.

Let's have a look at lines with disagreements when no language information is missing.

rg_tweets %>%
  dplyr::filter(!is.na(lang),
                !is.na(cld2_language),
                !is.na(cld3_language)) %>%
  dplyr::group_by(text) %>%
  dplyr::filter(length(unique(c(lang, cld2_language,
                              cld3_language))) != 1) %>%
  head(n = 50) %>%
  dplyr::select(text, lang, cld2_language,
         cld3_language)%>%
  DT::datatable()
<script type="application/json" data-for="htmlwidget-8c3851ccde84819289b8">{"x":{"filter":"none","data":[["1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33","34","35","36","37","38","39","40","41","42","43","44","45","46","47","48","49","50"],["#RolandGarros2017\nEl mexicano Santiago Gonzales y el estadounidense Donald Young están en la final de dobles ","Rafael Nadal face à son seul bourreau ","COME ON SIMONA HALEP !攼㹤愼㸰戼㹣攼㹤戼㸷戼㸹攼㹤愼㸰戼㹣攼㹤戼㸷愼㸹攼㹤愼㸰戼㹣攼㹤戼㸷戼㸹攼㹤愼㸰戼㹣攼㹤戼㸷愼㸹攼㹤愼㸰戼㹣攼㹤戼㸷戼㸹攼㹤愼㸰戼㹣攼㹤戼㸷愼㸹 ","I'd like to see Simona Halep start winning some slams. ","Much respect. Congratulations! ","The latest ))) El Fútbol se Siente! ","LA aceleración de la pelota de Pliskova. Nada, eso. ","Break point Halep! ","Hallelujah!! ","Timea the Swiss vs. Jelena the Latvian\n\n","Timea the Swiss vs. Jelena the Latvian\n\n","Timea the Swiss vs. Jelena the Latvian\n\n","Timea the Swiss vs. Jelena the Latvian\n\n","Ostapenko is a joy to watch. Happy birthday. ","Timea the Swiss vs. Jelena the Latvian\n\n","Timea the Swiss vs. Jelena the Latvian\n\n","Aus der Traum: ","Ostapenko is a beaasstt ","Jelena Ostapenko s'offre \n","Alona Finaalaa! ","Le sang ostapenko ","Jelena Ostapienko w finale ","Happy Birthday Ostapenko 攼㹤愼㸰戼㹤攼㹤戼㸱㠼㹦攼㹤愼㸰戼㹣攼㹤戼㹦戼㹢攼㹤愼㸰戼㹤攼㹤戼㸱㠼㹦攼㹤愼㸰戼㹣攼㹤戼㹦戼㹢攼㹤愼㸰戼㹣攼㹤戼㹥㠼㸹 ","allez jelena !!! ","Felicitar ampliamente a ","On sait maintenant que ","Pauvre Timea, écouter Ostapenko comme ça... ","Osta marafakin' penko ","Felicidades ","El mexa 攼㹤愼㸰戼㹣攼㹤戼㸷戼㸲攼㹤愼㸰戼㹣攼㹤戼㸷戼㹤 ya está en la final de dobles. ","Felicidades a Santiago Gonzalez ","Bresnik nach Champion!\"\n\n","Día histórico para el tenis mexicano, ","Ostapenko se na kurtu chova jako 13 lete rozmazlene decko. Zensky tenis je k smichu. ","Très belle 1/2 finale : ostapenko vs bacsinzky. 攼㹤愼㸰戼㹣攼㹤戼㹥戼㹥","Early break Jelena 3rd set ","Que lindo que juega Ostapenko ","Ostapenko j'aime bien ! 攼㹤愼㸰戼㹣攼㹤戼㹥戼㹥","Nan ta pas le droit la !! ","quel match entre ostapenko et bacsinsky!! ","Jelena Ostapenko 攼㹤愼㸰戼㹤攼㹤戼㸲愼㹡攼㹤愼㸰戼㹣攼㹤戼㹦戼㹢攼㹤愼㸰戼㹤攼㹤戼㸱㠼㹣攼㹤愼㸰戼㹣攼㹤戼㹦戼㹢攼㹤愼㸰戼㹤攼㹤戼㸸㠼㹤 ","Selain cute, dek Ostapenko ini boleh ya. Agresif! ","Tocó acostumbrarse a ver perder a ","1st set to Ostapenko after tiebreak 7-6 (7-4)! ","elle joue trop bien ","Is \"JOKER\" grand slam career over? ","Come on Jelena !!\n","Ostapenko..good luck..\n","Happy birthday, girls! Ostapenko v bacsinzky ","Vero Cepede llegó a Paraguay tras su histórico Francia\n\n"],["es","fr","en","en","en","es","es","en","in","en","en","en","en","en","en","en","de","nl","fr","fi","eu","pl","en","eu","es","fr","fr","eu","es","es","es","de","es","cs","ht","en","es","fr","fr","no","eu","in","es","no","fr","en","en","tl","en","es"],["gl","fr","en","en","en","en","es","en","et","en","en","en","en","en","en","en","hmn","en","sr","fi","st","sr","en","sr","ca","en","fr","fi","pt","es","gl","en","gl","cs","fr","en","es","fr","fr","fr","sr","ms","pt","en","fr","en","en","en","en","gl"],["es","co","ceb","haw","lb","es","eo","de","nl","sq","sq","sq","sq","pl","sq","sq","de","lb","hr","so","st","pl","sr","sr","gl","fr","ro","eu","gl","ca","gl","ru-Latn","es","hr","fy","fy","pt","fy","ht","lb","hr","ms","ca","nl","af","no","hr","fi","ru-Latn","es"]],"container":"\n \n \n
<\/th>\n text<\/th>\n lang<\/th>\n cld2_language<\/th>\n cld3_language<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"order":[],"autoWidth":false,"orderClasses":false,"columnDefs":[{"orderable":false,"targets":0}]}},"evals":[],"jsHooks":[]}</script>

Similarly I think these tweets are quite short. Moreover, languages seem to often be different but not that different, e.g. "es" (Spanish) and "ca" (Catalan) or "es" and "gl" (Galician). I sometimes make similar mistakes, when I say I've heard a "Nordic language"" because I couldn't identify it further than "not Swedish" (which I should be able to recognize).

I can also look at dissimilarities, computed on tweets with determined language.

dplyr::select(rg_tweets, lang, cld2_language, cld3_language) %>%
  dplyr::mutate_all(dplyr::funs(as.factor(.))) %>%
  t() %>%
  as.data.frame() %>%
cluster::daisy()
## Dissimilarities :
##                     lang cld2_language
## cld2_language 0.09870999              
## cld3_language 0.41158430    0.16547982
## 
## Metric :  mixed ;  Types = N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, 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N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, 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N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, N 
## Number of objects : 3

Unsurprisingly there's a worse agreement between cld3 results and the other two ones than between cld2 and Twitter. I say I'm not surprised because cld3 is a still experimental language detector.

Representing languages

I'm going to assume that if Twitter and cld2 agree on the language assigned to a tweet, then it's quite reliable.

agreed <- dplyr::filter(rg_tweets,
                        lang == cld2_language)

I'm therefore only considering 9432 tweets out of the original 18000 tweets.

agreed <- dplyr::group_by(agreed, lang)
agreed <- dplyr::summarize(agreed, tweets_count = n())
agreed <- dplyr::ungroup(agreed)
agreed <- dplyr::arrange(agreed, desc(tweets_count))
agreed <- dplyr::mutate(agreed, lang = factor(lang, ordered = TRUE, levels = unique(lang))) 

Let's plot the results.

library("ggplot2")
library("hrbrthemes")
ggplot(agreed) +
  geom_col(aes(lang, tweets_count)) +
  theme_ipsum(base_size = 20,
              axis_title_size = 20) +
  xlab("Detected language") +
  ylab("No. of tweets in the sample")

The most represented languages, English, French and Spanish are surely a result of who's on Twitter, what are the most spoken languages on Earth, who's interested in tennis (or the tennis players) and who's awake when the tournament happens. One way to control for the timezone of the tournament would be to stream tweets during each of the tournaments of the tennis Grand Slam. Another extension of this small blog post would be to look for players names in tweets and to then see if one can find an association between the most mentioned players in a language and the nationality of these players. This could even be coupled to a sentiment analysis (you could support one player and criticize the other players). Then again, that's something that'd be even more interesting in my opinion if applied to Eurovision contestants instead! Next year maybe...