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Gender Identification: A Comparative Study of Deep Learning Architectures

  • Bsir BassemEmail author
  • Mounir Zrigui
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)

Abstract

Author profiling, dating back to the earliest attempts at of analyzing quantitative text documents, is an extensivel-studied problem among NLP researchers. Because of its utility in crime, marketing and business. In this paper, three deep learning methods were evaluated for author profiling using tweets in Arabic language. The first method is based on a Convolutional Neural Network (CNN) model, while the second and third technique belongs to the family of Recurrent Neural Networks (RNN). The appropriate choice of some parameters, such as the number of amount of filters, training epochs, batch size, dropout and learning rate of Adam optimizer used in a RNN model is crucial in obtaining reliable results. The experimental findings of our comparative evaluation study demonstrate that GRU model outperforms LSTM and CNN models.

Keywords

Gated recurrent units Epochs Batch size Dropout GRU LSTM neural network CNN Author profiling Gender identification Deep learning 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.LATICE Laboratory Research Department of Computer ScienceUniversity of MonastirMonastirTunisia

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