An extended analytical study of Arabic sentiments
Nawaf A. Abdulla, Mahmoud Al Ayyoub, Mohammed Naji Al-Kabi · International Journal of Big Data Intelligence · 2014
Due to the evolution of Web 2.0 technology, internet users are more capable of posting their comments and reviews to express their opinions and feelings about everything. Hence, the necessity of automatically identifying the polarity (be it positive, negative, or neutral) of these comments arose and new interdisciplinary field called sentiment analysis (SA) emerged. Unluckily, many studies were conducted on the English language whereas those on the Arabic language are quite few. In addition, the publicly available datasets and testing tools for SA of Arabic text are rare. In this paper, a relatively large dataset of Arabic comments is manually collected and annotated. The source is one of the most widely used social networks in the Arab world, Yahoo!-Maktoob. A comprehensive analysis of this dataset is presented and two popular classifiers, support vector machine (SVM) and Naive Bayes (NB) are used for empirical experimentations. The results show that SVM outperforms NB and achieves a 64% accuracy level.