Ahmed, AL-Saffar and Bilal, Sabri and Hai, Tao and Suryanti, Awang and Mazlina, Abdul Majid and Wafaa, ALSaiagh (2016) Sentiment Analysis in Arabic Social Media Using Association Rule Mining. Journal of Engineering and Applied Sciences, 11 (14). pp. 3239-3247. ISSN 1816-949x (Print); 1818-7803 (Online). (Published)
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Abstract
The fast-paced growth in worldwide webs has resulted in the development of sentiment analysis it involves the analysis of comments or web reviews. The sentiment classification of the Arabic social media is an exciting and fascinating area of study. Hence this study brings forth a new method engaging association rules with three Feature Selection (FS) methods in the Sentiment Analysis (SA) of web reviews in the Arabic language. The feature selection methods used are (χ2), Gini Index (GI) and Information Gain (GI). This study reveals that the use of feature selection methods has enhanced the classifier results. This means that the proposed model shows a better result than the baseline result. Finally, the experimental results show that the Chi-square Feature Selection (FS) produces the best classification technique with a high accuracy of f-measure (86.811).
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Association rule; Arabic sentiment analysis; NLP; machine learning; feature selection method |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Faculty/Division: | Faculty of Computer System And Software Engineering |
| Depositing User: | Miss Amelia Binti Hasan |
| Date Deposited: | 04 Jul 2023 01:51 |
| Last Modified: | 03 Jan 2024 06:29 |
| URI: | http://umpir.ump.edu.my/id/eprint/37903 |
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