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Fusion: Practice and Applications
Volume 9 , Issue 1, PP: 08-28 , 2022 | Cite this article as | XML | Html |PDF

Title

Opinion mining for Arabic dialect in social media data fusion platforms: A systematic review

Authors Names :   Hani D. Hejazi   1 *     Ahmed A. Khamees   2  

1  Affiliation :  Faculty of Engineering &IT, The British University in Dubai, UAE

    Email :  hani.hejazi@gmail.com


2  Affiliation :  Faculty of Engineering &IT, The British University in Dubai, UAE

    Email :  khamisos@gmail.com



Doi   :   https://doi.org/10.54216/FPA.090101

Received: April 10, 2022 Accepted: September 22, 2022

Abstract :

The huge text generated on social media in Arabic, especially the Arabic dialect becomes more attractive for Natural Language Processing (NLP) to extract useful and structured information that benefits many domains. The more challenging point is that this content is mostly written in an Arabic dialect with a big data fusion challenge, and the problem with these dialects it has no written rules like Modern Standard Arabic (MSA) or traditional Arabic, and it is changing slowly but unexpectedly. One of the ways to benefit from this huge data fusion is opinion mining, so we introduce this systematic review for opinion mining from Arabic text dialect for the years from 2016 until 2019. We have found that Saudi, Egyptian, Algerian, and Jordanian are the most studied dialects even if it is still under development and need a bit more effort, nevertheless, dialects like Mauritanian, Yemeni, Libyan, and somalin have not been studied in this period. Many data fusion models that show a good result is the last four years have been discussed.

Keywords :

Data Fusion; Arabic dialect; Natural language processing; Opinion mining; Systematic Review.

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Cite this Article as :
Hani D. Hejazi , Ahmed A. Khamees, Opinion mining for Arabic dialect in social media data fusion platforms: A systematic review, Fusion: Practice and Applications, Vol. 9 , No. 1 , (2022) : 08-28 (Doi   :  https://doi.org/10.54216/FPA.090101)