Sentiment Analysis of Arabic Tweets Using Semantic Resources

Abstract

Sentiment analysis has grown to be one of the most active research areas in natural language processing and text mining. Many researchers have investigated sentiment analysis and opinion mining from different classification approaches. However, limited research is conducted on Arabic sentiment analysis as compared to the English language. In this paper, we have proposed and implemented a technique for Twitter Arabic sentiment analysis consisting of a semantic approach and Arabic linguistic features. Hence, we introduced a mechanism for preprocessing Arabic tweets, and for the methodology of sentiment classification we used a semantic approach. Also, we proposed a technique of classification which uses both Arabic and English sentiment lexicons to classify the Arabic tweets into three sentiment categories (positive or negative or neutral). Our experiments show that many issues were encountered when we used the Arabic SentiWordNet facility to classify Arabic tweets directly; these issues are basically related to Arabic text processing. The Arabic lexicons and Arabic tools must be improved or built from scratch in order to improve Arabic sentiment analysis using the semantic approach. The improvement in results, which are due to our contribution in the form of enhanced Arabic lexicons and amended Arabic tools, demonstrate this need.