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.
Full text not available from this repository.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 Naive Bayes classifiers.
| Item Type: | Article |
|---|---|
| Funders: | Universiti Malaya Research University (GPF091A-2020), Universiti Brunei Darussalam (UBD/RSCH/URC/RG(b)/2020/023) |
| Uncontrolled Keywords: | Multiple-criteria; Arabic dialect; Sentiment analysis; Machine learning; Performance evaluation |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Artificial Intelligence |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 01 Sep 2022 03:23 |
| Last Modified: | 01 Sep 2022 03:23 |
| URI: | http://eprints.um.edu.my/id/eprint/34132 |
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