Automatic lexicon construction for arabic sentiment analysis

Sentiment Analysis (SA) is the process of determining the sentiment of a text written in a natural language to be positive, negative or neutral. It is one of the most interesting subfields of natural language processing (NLP) and Web mining due to its diverse applications and the challenges associated with applying it on the massive amounts of textual data available online (especially, on social networks). Most of the current works on SA focus on the English language and follow one of two main approaches, (corpus-based and lexicon-based) or a hybrid of them. This work focuses on a less studied aspect of SA, which is lexicon-based SA for the Arabic language. In addition to experimenting and comparing three different lexicon construction techniques, an Arabic SA tool is designed and implemented to effectively take advantage of the constructed lexicons. The proposed SA tool possesses many novel features such as the way negation and intensification are handled. The experimental results show encouraging outcomes with 74.6% accuracy in addition to revealing new insights and guidelines that could direct the future research efforts.