Twitter sentiment analysis for arabic tweets

Abstract

Recently, increasing attention has been attracted to social networking sentiment analysis. Twitter is an online real-time social network and microblogging service that allows certified participants to distribute short posts called tweets. Twitter plays a major role in showing how consumers discover, research, and share information about brands and products. Sentiment analysis can be considered as a basic classification problem between three classes (Positive, Negative, and Neutral). Much work had been done on sentiment analysis in English while less work had been done on other languages like Arabic. Social media and blogs used by individuals are typically in Dialect Arabic. This work is focused on exploring efficient ways to increase the accuracy of sentiment analysis in Egyptian Arabic. The proposed system is based on semantic orientation (Cosine similarity and ISRI Arabic stemmer) and machine learning techniques. Experimental results showed that it achieves an overall accuracy of 92.98% using Linear Support Vector Machine.