2009) managed to increase the gender recognition quality to 89.2%, using sentence length, 35 non-dictionary words, and 52 slang words.The authors do not report the set of slang words, but the non-dictionary words appear to be more related to style than to content, showing that purely linguistic behaviour can contribute information for gender recognition as well.With only token unigrams, the recognition accuracy was 80.5%, while using all features together increased this only slightly to 80.6%. (2014) examined about 9 million tweets by 14,000 Twitter users tweeting in American English.They used lexical features, and present a very good breakdown of various word types.2004), with and without preprocessing the input vectors with Principal Component Analysis (PCA; (Pearson 1901); (Hotelling 1933)).We also varied the recognition features provided to the techniques, using both character and token n-grams.
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.
172 For Tweets in Dutch, we first look at the official user interface for the Twi NL data set, Among other things, it shows gender and age statistics for the users producing the tweets found for user specified searches.
These statistics are derived from the users profile information by way of some heuristics.
The general quality of the assignment is unknown, but in the (for this purpose) rather unrepresentative sample of users we considered for our own gender assignment corpus (see below), we find that about 44% of the users are assigned a gender, which is correct in about 87% of the cases.
Another system that predicts the gender for Dutch Twitter users is Tweet Genie ( 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.