Hybrid Deep Learning for Sentiment Polarity Determination of Arabic Microblogs

Sadam Al-Azani, El Sayed M. El-Alfy*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

53 Scopus citations

Abstract

In this study, we investigate various deep learning models based on convolutional neural networks (CNNs) and Long Short Term Memory (LSTM) recurrent neural networks for sentiment analysis of Arabic microblogs. Unlike English, the Arabic language has several specifics which complicate the process of feature extraction by traditional methods. We adopted a neural language model created at Google, known as word2vec, for vectorizing text. We then designed and evaluated several deep learning architectures using CNN and LSTM. The experiments were run on two publicly available Arabic tweets datasets. Promising results have been attained when combining LSTMs and compared favorably with most related work.

Original languageEnglish
Title of host publicationNeural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
EditorsDongbin Zhao, El-Sayed M. El-Alfy, Derong Liu, Shengli Xie, Yuanqing Li
PublisherSpringer Verlag
Pages491-500
Number of pages10
ISBN (Print)9783319700953
DOIs
StatePublished - 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10635 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Bibliographical note

Publisher Copyright:
© 2017, Springer International Publishing AG.

Keywords

  • Arabic sentiment analysis
  • Convolutional neural network
  • Deep learning
  • Long short-term memory
  • Word embedding

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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