Arabic Sentiment Analysis with Social Network Data: A Comparative Study

Alharbi, A; Sharma, N; Hussain, F

Arabic Sentiment Analysis with Social Network Data: A Comparative Study

Alharbi, A Sharma, N

Hussain, F

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Conference Proceeding
Citation:: Proceedings - 2024 IEEE International Conference on e-Business Engineering, ICEBE 2024, 2024, 00, pp. 87-94
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Alharbi, A
dc.contributor.author	Sharma, N https://orcid.org/0000-0003-0841-1245
dc.contributor.author	Hussain, F https://orcid.org/0000-0003-1513-8072
dc.date	2024-10-11
dc.date.accessioned	2025-02-26T06:31:56Z
dc.date.available	2025-02-26T06:31:56Z
dc.date.issued	2024-01-01
dc.identifier.citation	Proceedings - 2024 IEEE International Conference on e-Business Engineering, ICEBE 2024, 2024, 00, pp. 87-94
dc.identifier.uri	http://hdl.handle.net/10453/185322
dc.description.abstract	Social media platforms have become essential in modern society, with usage rapidly increasing. These platforms serve various roles, from entertainment and personal expression to education and e-commerce. Users frequently share opinions, generating valuable but largely unstructured data. Businesses and governments utilize sentiment analysis on this data to understand public opinions and predict behaviors, aiding in the improvement of products, services, and policies. However, the unstructured na-ture of the data makes its analysis complex. This article provide a comprehensive evaluation of several machine learning (ML) mod-els, including Support Vector Machine (SVM), Bernoulli Naive Bayes (BernoulliNB), Random Forest (RF), Gradient Boosting (GB), K-nearest Neighbors (KNN), and Logistic Regression (LR) on Twitter data for Arabic sentiment classification. Additionally, it provides valuable insights into the preprocessing steps required to prepare the data for analysis. It also discusses the selection of effective feature extraction techniques for Saudi dialect and modern Arabic sentiment analysis to gauge their efficacy in analyzing social media data. Among various feature extraction methods, TF-IDF consistently demonstrated superior accuracy, with SVM emerging as the top-performing model at 92.4%. Finally, a public dataset focusing on customer sentiment towards Saudi banks is employed to further validate our findings and their generalizability across different domains such as government policies and commercial sectors.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	Proceedings - 2024 IEEE International Conference on e-Business Engineering, ICEBE 2024
dc.relation.ispartof	2024 IEEE International Conference on e-Business Engineering (ICEBE)
dc.relation.isbasedon	10.1109/ICEBE62490.2024.00022
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Arabic Sentiment Analysis with Social Network Data: A Comparative Study
dc.type	Conference Proceeding
utslib.citation.volume	00
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Australian Artificial Intelligence Institute (AAII)
utslib.copyright.status	in_progress	*
dc.date.updated	2025-02-26T06:31:55Z
pubs.finish-date	2024-10-13
pubs.publication-status	Published
pubs.start-date	2024-10-11
pubs.volume	00

Abstract:

Social media platforms have become essential in modern society, with usage rapidly increasing. These platforms serve various roles, from entertainment and personal expression to education and e-commerce. Users frequently share opinions, generating valuable but largely unstructured data. Businesses and governments utilize sentiment analysis on this data to understand public opinions and predict behaviors, aiding in the improvement of products, services, and policies. However, the unstructured na-ture of the data makes its analysis complex. This article provide a comprehensive evaluation of several machine learning (ML) mod-els, including Support Vector Machine (SVM), Bernoulli Naive Bayes (BernoulliNB), Random Forest (RF), Gradient Boosting (GB), K-nearest Neighbors (KNN), and Logistic Regression (LR) on Twitter data for Arabic sentiment classification. Additionally, it provides valuable insights into the preprocessing steps required to prepare the data for analysis. It also discusses the selection of effective feature extraction techniques for Saudi dialect and modern Arabic sentiment analysis to gauge their efficacy in analyzing social media data. Among various feature extraction methods, TF-IDF consistently demonstrated superior accuracy, with SVM emerging as the top-performing model at 92.4%. Finally, a public dataset focusing on customer sentiment towards Saudi banks is employed to further validate our findings and their generalizability across different domains such as government policies and commercial sectors.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/185322