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Ranking Credibility of Online Product Reviews Based on Feature-Opinion Pair |
Wang Zhongqun( ), Wu Dongsheng, Jiang Sheng, Huang Subin |
School of Management and Engineering, Anhui Polytechnic University, Wuhu 241000, China |
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Abstract [Objective] This paper tries to choose credible comments from a large number of online product reviews, aiming to help consumers make purchasing decisions. [Methods] First, we proposed a concept of mainstream feature-opinion pair with the help of big data. Then, we established the credibility ranking model based on the recognition level of feature-opinion pair from different users’ comments. [Results] We found that the mainstream feature-opinions of online product reviews were relatively stable among the users of Taobao, TMall and Jingdong. Compared with existing models, the reviews sorted by our method covered more product features, and their helpfulness was increased by 7.5%. [Limitations] We did not consider the specific semantic situation of the comments while ranking their credibility. [Conclusions] The more mainstream feature-opinion pairs each comment contains, the more credible it is.
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Received: 15 May 2017
Published: 08 November 2017
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