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Enhanced Deep Learning Models for Sentiment Analysis in Arab Social Media

  • Mariem Abbes
  • Zied Kechaou
  • Adel M. Alimi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10638)

Abstract

Over the last few years, the amount of Arab sentiment rich data as appearing on the web has been marked with a rapid surge, owing mainly to the remarkable increase noticed in the number of social media users. In this respect, various companies are now turning to online forums, blogs, and tweets with the aim of getting reviews of their products, as drown from customers. Hence, sentiment analysis turns out to lie at the heart of social media associated research, targeted towards detecting people opinion as embedded within the wide range of texts while attempting to capture their pertaining polarities, whether positive or negative.

While research associated with English sentiment analysis has already achieved significant progress and success, a remarkable efforts have been made to extend the focus of interest to cover the Arabic language domain. Indeed, most of the Arabic sentiment analysis systems tend to still rely on costly hand-crafted features, where features representation seems to rest on manual pre-processing procedures for the intended accuracy to be achieved. This is mainly due to the Arabic language morphological complexity, linguistic specificities and lack of the resources. For this purpose, deep learning (DL) techniques for Sentiment Analysis turn out to be very versatile and popular. It is in this context that the present paper can be set, with the major focus of the interest being laid on proposing a novel automated information processing systems based DL. The experiment result show that RNN outperforms DNN in term of precision.

Keywords

Sentiment analysis Deep learning Arabic language DNN RNN LSTM 

Notes

Acknowledgement

The research leading to these results has received funding from the Ministry of Higher Education and Scientific Research of Tunisia under the grant agreement number LR11ES48.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.REsearch Groups in Intelligent Machines (REGIM), National School of Engineers (ENIS)University of SfaxSfaxTunisia

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