Gender Clustering of Blog Posts using Distinguishable Features

Abstract

The aim of this research is to find out how to perform effective clustering of unlabeled personal blog posts written in English by gender. Given a gender-labeled blog corpus and a blog corpus that is not gender-labeled, we extracted from the labeled corpus distinguishable unigrams for both males and females. Then, we defined two general features that represent the relative frequencies of the distinguishable males’ unigrams and females’ unigrams, (males’ frequency and females’ frequency). The best distinguishable feature was found to be the males’ frequency feature with a ratio factor at least 1.4 times that of females. This feature leads to accuracy rate of 83.7% for gender clustering of the unlabeled blog corpus. To the best of our knowledge, this study presents two novelties: (1) this is the first study to cluster blog posts by gender, and (2) clustering of an unlabeled corpus using distinguishable features that were extracted from a labeled corpus.