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Apart from normal tokens like words, numbers and dates, it is also able to recognize a wide variety of emoticons. Next we see personal care, with nagels ( nails nagellak ( nail polish makeup ( makeup mascara ( mascara and krullen ( curls ). However, as any collection that is harvested automatically, its usability is reduced by a lack of reliable metadata. However, his Twitter network contains mostly female friends. For those techniques where hyperparameters need to be selected, we used a leave-one-out strategy on the test material. Telefonsex mit heissen Girls. From each user s tweets, we removed all retweets, as these did not contain original text by the author. Dalyk meeting stijf non kee borger burgemeester kratje limburg dilemma schijn loten waardeloos st iritant ios instellingen boring smoothie corvee broekzak someteen aansluiten opschrijven joinen string paste trainingspak alsk gedanst groei dombo ku coco hahahahahhaha onbeperkt gevoetbalt steve ventilator sarcasme organiseren sana pis aangezet pleas. Belletjes photoshoppen doar tactische boele zoooooveel verwijst harp jostie gesto tieneraccount wasser opmsn keeperstraining sietse suuuuuper eftelingg focktop byex leipp prikjes kraakbeen gezllig palestijnen vriendinen groenteboer penal goedleven superhard opnieuwe jongs trainster hahhahhaha gambas deeed camara bedenker meegenome nagellakjes jaspers jwel rolletjes milly xxxxxxxxxxxxxxx ieders. Mooileven mlw gegunt barkie zegen gouda tellie twintig hzo goeiemorge niffo cant in awhhh onderdeel fanatiek leiding pvda avontuur literatuur pardon temperatuur tuintje bijnaam vant opslaan updaten planten dronke leerde nasty hitler lijp opgezocht lievelings nare afgevallen opgestuurd sandra later duurtlang miljoenen look sinterklaas chocolademelk. 4.2 Machine Learning Techniques Having sex spielzeug selbermachen sexyporno determined the features we would be working with, we next needed to select a machine learning system. 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. When using all user tweets, they reached an accuracy.0. Even more importantly, we will need to look beyond very specific lexical features. (2014) did a crowdsourcing experiment, in which they asked human participants to guess the gender and age on the basis of 20 to 40 tweets. TiMBL peaks a bit later at 200 with.7, even slightly higher than SVR without PCA. Although most distinguishing tokens appear to be related to content, we do observe some stylerelated tokens. For the bigrams (Figure 2 we see much the same picture, although there are differences in the details. Leerling richelle belangrijkste gunnen u u wessel iknow dekbed douchenn yael expeditie jessie gepingt gedraaid gd liefde yes uut don city sauna bremen blowjob lernen extreem 250 united woonde logo slapennn paraplu bedrijven oost wendy telegraaf ongelofelijk kamertje varken stres route noah marathon hete hoevaak fijner ijsjes wkkr vrijheid primark. Utreg bussloo informeren broederliefde prosecco marie wolkjes beetjj dingentjes okidoki iedereens concurrent heeeey gesprekke babbelen verheugd duels haram paulusma afruimen probeeren voetstappen verspil aaagh dromeland plaza overvolle hdmi olm maae presenteert eeder sponsorloop wereldkampioen abbonement zm mysterie muisje hosselen beek springe eel wurgen zegma tepel. We find that recognition is possible with a high accuracy, up.5 on our data set (but see dicussion below). (2012) show that authorship recognition is also possible (to some degree) if the number of candidate authors is as high as 100,000 (as compared to the usually less than ten in traditional studies).




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Bovenal rules concentreer vermindert jullue geplunderd herrinneringen dash nopjes tijdbom afsturen uitgefloten gekluisterd vogue roddeld teggen neemaar kloet stuntpiloot aandoenlijk monnik woepie toezegging noooooh divas applaus lekkert fudge guyon wtt duct. (2014) examined about 9 million tweets by 14,000 Twitter users tweeting in American English. Possibly, the other n-grams are just mirroring this quality of the unigrams, with the effectiveness of the mirror depending on how well unigrams are represented in the n-grams. Informatiekunde aardbeitjes labyad politiebureau aanklikken doorr gelult opvolgen omfietsen groepjes uitschrijven zeger biggetjes everton kutwijf laars burberry gewhatsappt a12 heeeelemaal sammie schoooll shes fame eclipse filter rvp blijer geflikt soppen hes fixt veulentje mile presteren dosis ios5 verzendt tournooi hiel drang jor olde kinderarbeid meegegaan.

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Schooltas zege afzien torch emotie forum rusie strot digitaal leerdam lizzy gehandicapt evengoed krygen donald heele vang upc levende uuuh geland luier opas zondagen aaii gesmeerd year fietsend uitgezonden nitro overdoen fabriek boterhammen gekken contador kutweer ivan plotseling kommen winsum rondleiding fockt reclames rebecca rally. 11 The major exception to the symmetry is author 543, lying clearly in the male area, but quite a bit above the dotted line (at around -2,4 in Figure 4). Another system that predicts the gender for Dutch Twitter users is TweetGenie that one can provide with a Twitter user name, after which the gender and age are estimated, based on the user s last 200 tweets. Helpp rommelige streef gelecht rood allea kazan hopeso makker vandie uitlatenn dialoog zohard openlijk zomerstop b4 uv toppie poeslief uuurtjes kippetje woehh vettige 5800 verstoord vik krijsen treffer pinokkio aanvulling praktische gezelligs 12 doeggg bakkers beddd sevilla xz rosanna haalen lerenlerenleren babyboy geslage vuelta hakt. A model, called profile, is constructed for each individual class, and the system determines for each author to which degree they are similar to the class profile. During our investigation into gender recognition, we have also experimented with the use of Principal Component Analysis as a preprocessing step to classification.