Sentiment Analysis of Arabic Sequential Data Using Traditional and Deep Learning: A Review

Thuraya M. Omran, Baraa T. Sharef*, Crina Grosan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Citation (Scopus)


With the emergence of social media and review sites peoples express their opinions toward entities, generating a huge amount of data or what is called big data that comes in non structured form of sequential data such as tweets or reviews. The availability of big data leads to the excitement in Artificial Intelligence and many applications such as Sentiment Analysis (SA). Although many studies conducted in SA, however majority of them focused on English, while that consider the Arabic one are very limited due to many challenges like variation of dialects, morphological attributes, and the lack of Arabic sources and corpora, despite the spread of the Arabic language and its frequent use in social media. The objective of this review is to highlight different studies of Arabic sequential data that utilized traditional and deep learning techniques.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages21
Publication statusPublished - 2021

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503


  • Big data
  • Deep learning
  • Machine learning
  • Sentiment analysis


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