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International Conference on Neural Information Processing

ICONIP 2017: Neural Information Processing pp 667–676Cite as

Enhanced Deep Learning Models for Sentiment Analysis in Arab Social Media

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,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.

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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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Authors and Affiliations

  1. REsearch Groups in Intelligent Machines (REGIM), National School of Engineers (ENIS), University of Sfax, BP 1173, 3038, Sfax, Tunisia

    Mariem Abbes, Zied Kechaou & Adel M. Alimi

Authors
  1. Mariem Abbes
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  2. Zied Kechaou
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  3. Adel M. Alimi
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Corresponding author

Correspondence to Mariem Abbes .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Abbes, M., Kechaou, Z., Alimi, A.M. (2017). Enhanced Deep Learning Models for Sentiment Analysis in Arab Social Media. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_68

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  • DOI: https://doi.org/10.1007/978-3-319-70139-4_68

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70138-7

  • Online ISBN: 978-3-319-70139-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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