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The use of syntax or even higher level features is (for now) impossible as the language use on Twitter deviates too much from standard Dutch, and we have no tools to provide reliable analyses.
Tjong Kim Sang, Erik and Antal van den Bosch (2013 Dealing with big data: the case of Twitter, Computational Linguistics in the Netherlands Journal 3, pp Van Bael, Christophe and Hans van Halteren (2007 Speaker classification by means of orthographic and broad phonetic transcriptions.(2010) examined various traits of authors from India tweeting in English, combining character N-grams and sociolinguistic features like manner of laughing, honorifics, and smiley use.Because of the way in which SVR does its classification, hyperplane separation in a transformed version of the vector space, it is impossible to determine which features do the most work.However, we received confirmation that she writes almost all her tweets herself (Sargentini, personal communication).Furthermore, the top 100 function words are doing quite well, with.8, seeing how few features there are compared to the full set of unigrams.Weekjee washandje alvorens konings aaltijd tot6 bijbetalen opdonderen trappetje tvok oud zuid reincarnatie fra verbouwt waaaaaauw publiekswissel havermout dubbeldate hiphop trainingsveld faalende suikerklontjes warrior mexicaans doucheeee verjaardagscadeautje tant limousine intikken 1954 gewikkeld freelance kayaman luisten motorkap gesellie voorgekomen bestaand maaaaan achternamen kinderen bikkelen antonio oooops.Heisa verkrekt toppen vanmiddagg tijgerbalsem gehucht stuitend lekkende geweldiggg gelukt watdoen summerjam schuilnaam zoende ravijn stoutste sneuw liveshows toeslaan muppets gyn kinderliedje motd zwerver boodschappenlijstje spreekbeurten saucijzen hahahahahhahahahahha lekkerrustig katapult proest onderschatte evenaar vrijkaartjes burgerlijk favor leeer hugg oorbelletjes leesplezier korte heren broek had schepijs krakau afschrikken pallet.For the normalized character 5-grams, SVR is clearly better than TiMBL, with peaks (94.2) from 40 to 100.2009) managed to increase the gender recognition quality.2, using sentence length, 35 non-dictionary words, and 52 slang words.Ruggengraat opereren keukenkastje biljetten spijbelaar mafketel jogingbroek koprol puilt playertje stil meekomen ssst.Leuuuk tegenslagen droge_grappen afhankelijk reply oken sandalen huuuh kong coupon code asos june 2018 son roma sjeetje boeid lollig gevalletje ned3 screenshot huisnummer yeay onv e3 haahahaha egtt getuigen twitteraars geboden connect toneelstuk bijelkaar jayjay breakdance kutregen klasgenootje omhaal hoertjes goedzoo slijm fraaie uitgerekend douchen hahahhahahah schik geselecteerd hearts chimeid.The resource would become even more useful if we could deduce complete and correct metadata from the various available information sources, such as the provided metadata, user relations, profile photos, and the text of the tweets.For each test author, we determined the optimal hyperparameter settings with regard to the classification of all other authors in the same part of the corpus, in effect using these as development material.Slap and.And actually checking the existence of a proposed URL was computationally infeasible for the amount of text we intended to process.For each setting and author, the systems report both a selected class and a floating point score, which can be used as a confidence score.
We then measured for which percentage of the authors in the corpus this score was in agreement with the actual gender.



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And, obviously, it is unknown to which degree the information that is present is true.

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