HILATSA: A hybrid Incremental learning approach for Arabic tweets sentiment analysis

Abstract

A huge amount of data is generated since the evolution in technology and the tremendous growth of social networks. In spite of the availability of data, there is a lack of tools and resources for analysis. Though Arabic is a popular language, there are too few dialectal Arabic analysis tools. This is because of the many challenges in Arabic due to its morphological complexity and dynamic nature. Sentiment Analysis (SA) is used by different organizations for many reasons as developing product quality, adjusting market strategy and improving customer services. This paper introduces a semi- automatic learning system for sentiment analysis that is capable of updating the lexicon in order to be up to date with language changes. HILATSA is a hybrid approach which combines both lexicon based and machine learning approaches in order to identify the tweets sentiments polarities. The proposed approach has been tested using different datasets. It achieved an accuracy of 73.67% for 3-class classification problem and 83.73% for 2-class classification problem. The semi-automatic learning component proved to be effective as it improved the accuracy by 17.55%. Keywords: Sentiment analysis, Opinion mining, Hybrid approach, Arabic Tweets Sentiment Analysis, Sentiment classification, Self-learning