Classification of Informative Reviews Based on Personal Values
Yasufumi Takama, Zhongjie Mao, and Shunichi Hattori
Graduate School of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan
This paper proposes a method for classifying informative reviews based on personal values. Reviews of an item are useful for a user who is considering purchasing it. However, it is difficult for readers to find informative reviews from vast amount of reviews because of existence of too many uninformative reviews. This paper supposes that the value of a review is affected by reader-dependent and independent factors. Typical uninformative reviews in terms of reader-independent factor are copy-and-paste reviews, which do not provide any readers with useful information for their decision-making. On the other hand, it is supposed different readers regard different reviews as informative, which is affected by their personal values. This paper focuses on such a readerdependent factor, and proposes a methods for classifying informative reviews based on reader’s personal value. Experiments are conducted using actual review data provided by Rakuten Inc., of which the results show about 0.7 of average accuracy is achieved. Furthermore, it is also shown proposed method can model judging criteria common to those who have similar personal values.
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