Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Computer Engineering

Sentiment analysis (SA) is the process of analyzing writers{u2019} opinions, emotions and attitudes from documents and determining whether they are positive, negative or neutral. It is used for many reasons as developing product quality, adjusting market strategy and improving customer services. Since the evolution in technology and the tremendous growth of social networks, a huge amount of data is generated. In spite of the availability of data, there is a lack of tools and resources. 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 its dynamic nature. Approaches used to classify the opinions are categorized into lexicon based, machine based and hybrid based approach. This thesis 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. It is a hybrid approach which combines both lexicon based and machine learning approaches in order to identify Arabic tweets sentiments polarities. It proved to be able to cope with the dynamic nature and improved the accuracy by 17.55%

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