Gender Recognition on Dutch Tweets
In this paper, we investigate gender recognition on Dutch Twitter material, using a corpus consisting of the full Tweet production (as far as present in the TwiNL data set) of 600 users (known to be human individuals) over 2011 and 2012. We experimented with several authorship profiling techniques and various recognition features, using Tweet text only, in order to determine how well they could distinguish between male and female authors of Tweets. We achieved the best results, 95.5% correct assignment in a 5-fold cross-validation on our corpus, with Support Vector Regression on all token unigrams. Two other machine learning systems, Linguistic Profiling and TiMBL, come close to this result, at least when the input is first preprocessed with PCA.