Staff View: A multi-criteria approach for Arabic dialect sentiment analysis for online reviews: Exploiting optimal machine learning algorithm selection

A multi-criteria approach for Arabic dialect sentiment analysis for online reviews: Exploiting optimal machine learning algorithm selection

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 classificati...

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Main Authors:	Abo, Mohamed Elhag Mohamed, Idris, Norisma, Mahmud, Rohana, Qazi, Atika, Hashem, Ibrahim Abaker Targio, Maitama, Jaafar Zubairu, Naseem, Usman, Khan, Shah Khalid, Yang, Shuiqing
Format:	Article
Published:	MDPI 2021
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.um.edu.my/34132/
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id	my.um.eprints.34132
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spelling	my.um.eprints.341322022-09-01T03:23:41Z http://eprints.um.edu.my/34132/ A multi-criteria approach for Arabic dialect sentiment analysis for online reviews: Exploiting optimal machine learning algorithm selection Abo, Mohamed Elhag Mohamed Idris, Norisma Mahmud, Rohana Qazi, Atika Hashem, Ibrahim Abaker Targio Maitama, Jaafar Zubairu Naseem, Usman Khan, Shah Khalid Yang, Shuiqing QA75 Electronic computers. Computer science 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 Naive Bayes classifiers. MDPI 2021-09 Article PeerReviewed Abo, Mohamed Elhag Mohamed and Idris, Norisma and Mahmud, Rohana and Qazi, Atika and Hashem, Ibrahim Abaker Targio and Maitama, Jaafar Zubairu and Naseem, Usman and Khan, Shah Khalid and Yang, Shuiqing (2021) A multi-criteria approach for Arabic dialect sentiment analysis for online reviews: Exploiting optimal machine learning algorithm selection. Sustainability, 13 (18). ISSN 2071-1050, DOI https://doi.org/10.3390/su131810018 <https://doi.org/10.3390/su131810018>. 10.3390/su131810018
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Abo, Mohamed Elhag Mohamed Idris, Norisma Mahmud, Rohana Qazi, Atika Hashem, Ibrahim Abaker Targio Maitama, Jaafar Zubairu Naseem, Usman Khan, Shah Khalid Yang, Shuiqing A multi-criteria approach for Arabic dialect sentiment analysis for online reviews: Exploiting optimal machine learning algorithm selection
description	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 Naive Bayes classifiers.
format	Article
author	Abo, Mohamed Elhag Mohamed Idris, Norisma Mahmud, Rohana Qazi, Atika Hashem, Ibrahim Abaker Targio Maitama, Jaafar Zubairu Naseem, Usman Khan, Shah Khalid Yang, Shuiqing
author_facet	Abo, Mohamed Elhag Mohamed Idris, Norisma Mahmud, Rohana Qazi, Atika Hashem, Ibrahim Abaker Targio Maitama, Jaafar Zubairu Naseem, Usman Khan, Shah Khalid Yang, Shuiqing
author_sort	Abo, Mohamed Elhag Mohamed
title	A multi-criteria approach for Arabic dialect sentiment analysis for online reviews: Exploiting optimal machine learning algorithm selection
title_short	A multi-criteria approach for Arabic dialect sentiment analysis for online reviews: Exploiting optimal machine learning algorithm selection
title_full	A multi-criteria approach for Arabic dialect sentiment analysis for online reviews: Exploiting optimal machine learning algorithm selection
title_fullStr	A multi-criteria approach for Arabic dialect sentiment analysis for online reviews: Exploiting optimal machine learning algorithm selection
title_full_unstemmed	A multi-criteria approach for Arabic dialect sentiment analysis for online reviews: Exploiting optimal machine learning algorithm selection
title_sort	multi-criteria approach for arabic dialect sentiment analysis for online reviews: exploiting optimal machine learning algorithm selection
publisher	MDPI
publishDate	2021
url	http://eprints.um.edu.my/34132/
_version_	1744649158453624832
score	13.211869

A multi-criteria approach for Arabic dialect sentiment analysis for online reviews: Exploiting optimal machine learning algorithm selection

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