Arabic Sentiment Analysis based on Topic Modeling

Users of social media generate a huge volume of reviews and comments. These reviews and comments express user's opinions about different topics. As a result, there is a great need to understand and classify these reviews. Sentiment Analysis Systems is a good way to overcome this problem. Reviews are considered as short texts and they are different from traditional documents without enough contextual information. To address this issue, we propose an efficient representation for short text based on concepts instead of terms, which transforms the data representation into a shorter, more compact, and more predictive one. However, for the Arabic language, the majority of semantic resources are incomplete projects; this may presents a serious problem about the coverage ratio of the Arabic language compared with other Languages. To overcome this problem and starting with the assumption that terms belonging to same topic share many semantic links in the same dataset, their corresponding concepts will share the same semantics links in the same dataset. We suggest integrating Topic Modeling as a tool to bring together terms with the same semantic links. The proposed method has been tested and evaluated using the Large Scale Arabic Book Reviews Dataset and the obtained results illustrate the interest and efficiency of our contribution.