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An interesting observation is that there is a clear class of misclassified users who have a majority of opposite gender users in their social network. When adding more information sources, such as profile fields, they reach an accuracy of 92.0%.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.They used lexical features, and present a very good breakdown of various word types.When using all user tweets, they reached an accuracy of 88.0%.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.
2004), with and without preprocessing the input vectors with Principal Component Analysis (PCA; (Pearson 1901); (Hotelling 1933)).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.Gender recognition has also already been applied to Tweets. (2010) examined various traits of authors from India tweeting in English, combining character N-grams and sociolinguistic features like manner of laughing, honorifics, and smiley use.A group which is very active in studying gender recognition (among other traits) on the basis of text is that around Moshe Koppel. 2002) they report gender recognition on formal written texts taken from the British National Corpus (and also give a good overview of previous work), reaching about 80% correct attributions using function words and parts of speech.
Later, in 2004, the group collected a Blog Authorship Corpus (BAC; (Schler et al.With lexical N-grams, they reached an accuracy of 67.7%, which the combination with the sociolinguistic features increased to 72.33%. (2011) attempted to recognize gender in tweets from a whole set of languages, using word and character N-grams as features for machine learning with Support Vector Machines (SVM), Naive Bayes and Balanced Winnow2.