Abstract
In this paper, we describe a procedure for extracting annotated Arabic negative and positive tweets. We use these extracted annotated tweets to build our sentiment system using Naive Bayes with TF-IDF enhancement. The large size of training data for a highly inflected language is necessary to compensate for the sparseness nature of such languages. We present our techniques and explain our experimental system. We automatically collect 200 thousand annotated tweets. The evaluation shows that our sentiment analysis system has high precision and accuracy measures compared to existing ones.
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