AraSenTi: Large-Scale Twitter-Specific Arabic Sentiment Lexicons

Sentiment Analysis (SA) is an active research area nowadays due to the tremendous interest in aggregating and evaluating opinions being disseminated by users on the Web. SA of English has been thoroughly researched; however research on SA of Arabic has just flourished. Twitter is considered a powerful tool for disseminating information and a rich resource for opinionated text containing views on many different topics. In this paper we attempt to bridge a gap in Arabic SA of Twitter which is the lack of sentiment lexicons that are tailored for the informal language of Twitter. We generate two lexicons extracted from a large dataset of tweets using two approaches and evaluate their use in a simple lexicon based method. The evaluation is performed on internal and external datasets. The performance of these automatically generated lexicons was very promising, albeit the simple method used for classification. The best F-score obtained was 89.58% on the internal dataset and 63.1-64.7% on the exter-