We reimplemented this model as our reference for gender classification, further details of which are given below: For each conversation side, a training example was created using unigram and bigram features with tf-idf weighting, as done in standard text classification approaches.
stopwords were retained in the feature set as various sociolinguistic studies have shown that use of some of the stopwords, for instance, pronouns and determiners, are correlated with age and gender.