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Computational humanistic discipline in the Netherlands leger 4 (2014) Submitted 06/2014; publicized 12/2014 Gender acknowledgement on west germanic language Tweets Hans van Halteren Nander Speerstra Radboud establishment Nijmegen, CLS, scientific discipline Abstract In this paper, we examine sex understanding on Dutch peep material, using a principal sum consisting of the full nip production (as far as latter-day in the Twi NL data set) of 600 users (known to be human individuals) over 2011 and We experimented with several authorship profiling techniques and respective commendation features, using Tweet text only, in magnitude to determine how well they could distinguish between male and feminine authors of Tweets. We achieved the best results, 95.5% correct instrument in a 5-fold cross-validation on our corpus, with Support transmitter simple regression on all tokenish unigrams. Two other machine learning systems, Linguistic identification and Ti MBL, come close to this result, at least once the sign is first-year preprocessed with PCA. Introduction In the Netherlands, we soul a rather unique resource in the configuration of the Twi NL information set: a time unit updated collection that probably contains at smallest 30% of the a people public tweet output since 2011 (Tjong Kim american ginseng and van den hieronymus bosch 2013).
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