Thesis (Ph.D.) - Cairo University - Institute Statistical Studies - Department of Computer and Information Science

The rise of social media such as blogs and social networks has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations; therefore, many are now looking to the field of sentiment analysis. In this work, we present a feature-based document level approach for Arabic sentiment analysis system. The system is using Arabic idioms/saying phrases lexicon as a key importance for improving the detection of the sentiment polarity in Arabic sentences as well as a number of novels and rich set of linguistically motivated features (contextual Intensifiers, contextual Shifter and negation handling), syntactic features for conflicting phrases which enhance the sentiment classification accuracy. Furthermore, I introduce an automatic expandable wide coverage polarity lexicon of Arabic sentiment words. The lexicon is built with gold-standard sentiment words as a seed which is manually collected and annotated and it expands and detects the sentiment orientation automatically of new sentiment words using synset aggregation technique and free online Arabic lexicons and thesauruses. The applied data is focuses on modern standard Arabic and Egyptian Dialectal Arabic tweets and microblogs (hotel reservation, product reviews, etc.). The experimental results using the resources and techniques with SVM classifier indicate high performance levels, with accuracies of over 95%

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