2025
doi: 10.12720/jait.16.1.21-36
Enhanced Negation Detection in Arabic Reviews Using Supervised Classification Approach
Ahmed S. Abuhammad1,2,*
,
Mahmoud A. Ahmed3
- 1Department of Computer Science and Information Technology, University College of Science and Technology, Khan Younis, Palestine
- 2Department of Information Technology, University of the Holy Quran and Taseel of Science, Wad Madani, Sudan
- 3Department of Computer Science, University of Khartoum, Khartoum, Sudan
- Manuscript receivedApril 18, 2024
- revisedJuly 4, 2024
- acceptedSeptember 10, 2024
- publishedJanuary 9, 2025
Abstract
This paper introduces a novel approach for automated negation detection in Arabic reviews, leveraging advanced supervised classification techniques. We explore various methods, including naïve bayes (kernel), decision tree, and k-nearest neighbors, to analyze lexical and structural features from an Arabic text corpus. Our experimental results reveal that the decision tree model achieves the highest accuracy at 97.13%, significantly outperforming other classifiers. This advancement highlights the effectiveness of our approach in enhancing sentiment analysis for Arabic text, demonstrating a major improvement in negation detection capabilities.
Keywords
- Arabic sentiment analysis
- machine learning
- natural language processing
- negation detection
- supervised classification
How to Cite
Ahmed S. Abuhammad and Mahmoud A. Ahmed, "Enhanced Negation Detection in Arabic Reviews Using Supervised Classification Approach," Journal of Advances in Information Technology, vol. 16, no. 1, pp. 21-36, 2025. https://doi.org/10.12720/jait.16.1.21-36
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
