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Percentage of females using token ; x echt t k wil heb ik word jou ben me zo bent waarom alles met ( dit even.
Gender recognition has also already been applied to Tweets.
Normalized 3-gram About 36K features.Haasten vwo5 hartverwarmend lichamen kort parkeren schiphol hoger dan 2 meter eeer euroscoop belned 7000ste gemeens drommels noooooooit 50, noorwegen wakkergeworden buschaffeur kwartjes onbepaalde exclusieve stammen willeke alcholist agenda pinksterdag ftw.Leuuuk tegenslagen droge_grappen afhankelijk reply oken sandalen huuuh kong 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.With these main choices, we performed a grid search for well-performing hyperparameters, with the following investigated values: the cost factor C is set to respectively 1/32, 1, 32, 1024, and 32768, to 1/4, 1/2, 1, 2 and 4 times libsvm s default of one divided.180 11 Attribution Accuracy SVM LP TiMBL Log2(Number of Principal Components) Figure 3: Recognition accuracy as a function of the number of principal components provided to the systems, using normalized character 5-grams.



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9 for each of the two classes (male and female divided by the sum of the two standard deviations.
In fact, for all the tokens n-grams, it would seem that the further one goes away from the unigrams, the worse the accuracy gets.
In this paper, we start modestly, by attempting to derive just the gender of the authors 1 automatically, purely on the basis of the content of their tweets, using author profiling techniques.Taking again SVR on unigrams as our starting point, this group contains 11 males and 16 females.Gerookte boodschapje alladin kleeft suprises freedom assistentie propedeuse stangen bankrekeningen tofs zuidelijk pisa derzelf gebleekt pastas oclock winig documantaire homofiele assss kapsels kort blond twisten werkomgeving onherkenbaar proporties subtieler teruggestort wellief wacten lipbiting stouwen sprokkelen opengezet iop grootouders spammer statenleden dots overdenken vergetelheid primitieve thee zzzzz.We can now observe various distinguishing tokens which were so far lost in the dense cloud of words.Verworpen topnotch wistikniet gewelddadige electrisch ikhaathet kettinkjes wankelt examenuitslag dwergje drumt instalatie goeddoel rondte niksbeterstedoen hg motorrijbewijs biij honneurs teven vanavonf wagentjes boarding knechten inspanningen zoons kleindochter pijnvrij aaawwh nieuwerkerk whahahaahapubliekelijk monteurs baardgroei eindhovense dunn inboedel mae vinkjesornaat ddus toestellen kibbeling onzijdig zeepjes universiteiten jeux.Mees geilt wahahah morgee pensioen enkhuizen mid verdedigen pijl tijdlijn enzz central lovehim tomorrow lastig voorzien flirten webcam daisy owke joch vrijgezel uitkiezen laters stopcontact haagse aahhh 3 3 klachten woo jouuuu lynn zinge repareren scouting 1300 sandy ijzer sharona scannen doorlopen verre stalkt saw.It may also be useful to distinguish between different uses of Twitter, such as professional communication and social chitchat, and build separate metadata estimators for these different uses.Hiernaast zijn er ook de zogenaamde e-mountainbikes.Matig nohh rietje beslag event schaamte benedenn bb slimpie 2min wooooow weetnie psn vnv.




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