@inbook{06d4ff6b0a864defb5da93dfbf4ea324,
title = "Sentiment Analysis of Arabic Sequential Data Using Traditional and Deep Learning: A Review",
abstract = "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.",
keywords = "Big data, Deep learning, Machine learning, Sentiment analysis",
author = "Omran, {Thuraya M.} and Sharef, {Baraa T.} and Crina Grosan",
note = "Publisher Copyright: {\textcopyright} 2020, The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2021",
doi = "10.1007/978-3-030-62796-6_26",
language = "English",
series = "Studies in Computational Intelligence",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "439--459",
booktitle = "Studies in Computational Intelligence",
address = "Germany",
}