Construction of an accurate automatic lexicon for Arabic sentiment analysis

Sentiment analysis has aroused the interest of many studies in recent years. Regarding to its high importance in taking and extracting decisional information, the light of research is still shed on it. The first step of a sentiment analysis system is the construction of the basic knowledge, namely the linguistic resources. The classical methods of lexicon building are manual, semi-automatic, or automatic. Both the manual and semi-automatic methods need a manual check that is time and effort consuming whereas the automatic approach neglects word semantic. Herein, we intend to automate the lexicon extraction method as well as giving accurate polarity. In order to perform this task and achieve satisfying results, we extract a Bag-of-Words and we then apply many filters on it to keep only clean and sentimental terms. This paper also explores the effectiveness of a supervised approach based on the Bag-of-Words model in defining sentiment polarity of the processed reviews in order to shed the light on its usefulness.