Arabic sentiment analysis of YouTube comments
Abdel-Karim Al-Tamimi, Ali M. Shatnawi, Esraa Bani-Issa · 2017
With the current level of ubiquity of social media websites, obtaining the users preferences automatically became a crucial task to assess their tendencies and behaviors online. Arabic language as one of the most spoken languages in the world and the fastest growing language on the Internet motivates us to provide reliable automated tools that can perform sentiment analysis to reveal users opinions. In this paper, we present our work of Arabic comments classification based on our collected and manually annotated YouTube Arabic comments. We share our classification results utilizing the most commonly used supervised classifiers: SVM-RBF, KNN, and Bernoulli NB classifiers. Experiments were performed using both raw and language-normalized datasets. We show that SVM-RBF outperformed other classification methods with an f-measure of 88.8% using a normalized dataset with two polarities.