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Product reviews on the web sites help not only consumers to purchase products but also developers to analyze consumers' needs. Because huge amount of the reviews are presented on the various sites, however, it is a hard task for them to read and to find only the reviews which match their viewpoint that they focus on. Though, to overcome this issue, many researchers in the field of the natural language processing tried to find review sites, classification of reviews according to their viewpoints does not have been succeeded because the corpus for classification must be needed and building it takes a lot of cost. In this paper, we propose a method to build the corpus for each type of products automatically and also propose a method for automatic classification of method of the review. In our method of classification, we focused on the property of review by extending the Tf-Idf. As the classification results contained many errors of classifications in the similar viewpoints, we built the improved method. In this method, we divided the classification process into two-stage. As the result, we could classify reviews by over 80 point.

An Automatic Classification of Product Review into Given Viewpoints