Weakly Supervised Learning for Judging the Credibility of Movie Reviews
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In this paper, we deal with the problem of judging the credibility of movie reviews. The problem is challenging because even experts cannot clearly and efficiently judge the credibility of a movie review and the number of movie reviews is very large. To attack this problem, we propose a weakly supervised learning method for fast annotation. In terms of predefined criteria for weakly supervised learning, we present a simple and clear criterion based on historical movie ratings associated with movie reviewers. The proposed method has the following two advantages. First, it is significantly efficient because we can annotate the entire data sets according to the predefined rule. Indeed, we show that the proposed method can annotate 8,000 movie reviews only in 0.712 seconds. Second, a criterion adapted for weakly supervised learning is simple but effective. We use as a comparison learning method that uses the helpfulness votes of other reviewers as the criterion to judge the credibility of movie reviews, which has been widely used to judge the credibility of online reviews. We indicate that the proposed learning method is comparable to or even better than the helpfulness vote method by showing an improvement over the accuracy of the latter method of 1.57
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