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Existing sentiment analysis methods often assume that the comments of any single reviewer are independent of each other and so they do not take advantage of significant information that may be extracted from reviewers\u2019 comment histories. Using psychological findings and the theory of negativity bias, we propose a method for exploiting reviewers\u2019 comment histories to improve sentiment analysis. Furthermore, to use more fine-grained information about the content of a review, our method predicts the overall ratings by aggregating sentence-level scores. In the proposed system, the Dempster\u2013Shafer theory of evidence is utilized for score aggregation. The results from four large and diverse social Web datasets establish the superiority of our approach in comparison with the state-of-the-art machine learning techniques. 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