A Multi-Criteria Approach for Arabic Dialect Sentiment Analysis for Online Reviews: Exploiting Optimal Machine Learning Algorithm Selection

Mohamed Elhag Mohamed Abo, Norisma Idris, Rohana Mahmud, Atika Qazi, Ibrahim Abaker Targio Hashem, Jaafar Zubairu Maitama, Usman Naseem, Shah Khalid Khan and Shuiqing Yang
Additional contact information
Mohamed Elhag Mohamed Abo: Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Norisma Idris: Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Rohana Mahmud: Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Atika Qazi: Centre for Lifelong Learning, Universiti Brunei Darussalam, Gadong BE1410, Brunei
Ibrahim Abaker Targio Hashem: Department of Computer Science, College of Computing and Informatics, University of Sharjah, Sharjah 27272, United Arab Emirates
Jaafar Zubairu Maitama: Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Usman Naseem: School of Computer Science, University of Sydney, Sydney, NSW 2006, Australia
Shah Khalid Khan: School of Engineering, RMIT University, Carlton, VIC 3053, Australia
Shuiqing Yang: School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou 310018, China

Sustainability, 2021, vol. 13, issue 18, 1-20

Abstract: A sentiment analysis of Arabic texts is an important task in many commercial applications such as Twitter. This study introduces a multi-criteria method to empirically assess and rank classifiers for Arabic sentiment analysis. Prominent machine learning algorithms were deployed to build classification models for Arabic sentiment analysis classifiers. Moreover, an assessment of the top five machine learning classifiers’ performances measures was discussed to rank the performance of the classifier. We integrated the top five ranking methods with evaluation metrics of machine learning classifiers such as accuracy, recall, precision, F-measure, CPU Time, classification error, and area under the curve (AUC). The method was tested using Saudi Arabic product reviews to compare five popular classifiers. Our results suggest that deep learning and support vector machine (SVM) classifiers perform best with accuracy 85.25%, 82.30%; precision 85.30, 83.87%; recall 88.41%, 83.89; F-measure 86.81, 83.87%; classification error 14.75, 17.70; and AUC 0.93, 0.90, respectively. They outperform decision trees, K-nearest neighbours (K-NN), and Naïve Bayes classifiers.

Keywords: multiple-criteria; Arabic dialect; sentiment analysis; machine learning; performance evaluation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/18/10018/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/18/10018/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:18:p:10018-:d:630640

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().