Abstract:

Every day, a huge volume of comments and reviews about the different aspects of our life are generated. Such data in plain text can be used to infer im- portant information about the author. Thus, authorship profiling can be defined as the task of providing information about the background of the author of an anonymous text based on the language of the text. As instances of the author's characteristics, we can mention age, gender, native language and personality. This important task is a very active research area because of its utility in crime, marketing and business. In this paper, we address the problem of gender identification by applying the Long Short-Term Memory neural network architecture which is a novel type of recurrent network architecture that implements an appropriate gradient-based learning algorithm to overcome the vanishing-gradient problem. Experimental results show that our composition outperformed the traditional machine learning methods on gender identification.