Gender classification of microblog text based on authorial style

Gender profiling of unstructured text data has several applications in areas such as marketing, advertising, legal investigation, and recommender systems. The automatic detection of gender in microblogs, like twitter, is a difficult task. It requires a system that can use knowledge to interpret the linguistic styles being used by the genders. In this paper, we try to provide this knowledge for such a system by considering different sets of features, which are relatively independent of the text, such as function words and part of speech n-grams. We test a range of different feature sets using two different classifiers; namely Naïve Bayes and maximum entropy algorithms. Our results show that the gender detection task benefits from the inclusion of features that capture the authorial style of the microblog authors. We achieve an accuracy of approximately 71 %, which outperforms the classification accuracy of commercially available gender detection software like Gender Genie and Gender Guesser.