Authors : Ahmed AL-Saffar, Bilal Sabri, Hai Tao, Suryanti Binti Awang, Mazlina Binti Abdul Majid and Wafaa ALSaiagh
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).
Ahmed AL-Saffar, Bilal Sabri, Hai Tao, Suryanti Binti Awang, Mazlina Binti Abdul Majid and Wafaa ALSaiagh, 2016. Sentiment Analysis in Arabic Social Media Using Association Rule Mining. Journal of Engineering and Applied Sciences, 11: 3239-3247.