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This paper introduces an approach to sentiment analysis using Support Vector Machines. It combines diverse sources of potentially pertinent information, including several fa-vorability measures and knowledge of the topic of the text. Experiments show that hybrid SVMs which combine unigram-style feature-based SVMs with those based on real-valued favorability measures obtain superior performance. Further experiments using a feature set enriched with topic information on a smaller dataset of music reviews are

Author

jehosha

Language

EN