Enhancing Deep Learning Gender Identification with Gated Recurrent Units Architecture in Social Text

Abstract

Author profiling consists in inferring the authors’ gender, age, native language, dialects or personality by examining his/her written text. This paper represent an extension of the recursive neural network that employs a variant of the Gated Recurrent Units (GRUs) architecture. Our study focuses on gender identification based on Arabic Twitter and Facebook texts by investigating the examined texts features. The introduced exploiting a model that applies a mixture of unsupervised and supervised techniques to learn word vectors capturing the words syntactic and semantic. We applied our approach on two corpora of two social media varieties: twitter texts, in which each author is assigned at least 100 tweets, and Facebook corpus containing short texts with an average of 15.77 words per author. The obtained experimental results are comparable to the best findings provided by the best per-forming systems presented in PAN Lab at CLEF 2017.