›› 2012, Vol. 27 ›› Issue (3): 635-649.doi: 10.1007/s11390-012-1250-z

• Special Issue on Social Network Mining • Previous Articles     Next Articles

Exploiting Consumer Reviews for Product Feature Ranking

Su-Ke Li1 (李素科), Zhi Guan2 (关志), Li-Yong Tang2 (唐礼勇), and Zhong Chen2 (陈钟), Member, CCF, IEEE   

  1. 1. School of Software and Microelectronics, Peking University, Beijing 100871, China;
    2. School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
  • Received:2011-08-31 Revised:2012-01-19 Online:2012-05-05 Published:2012-05-05
  • About author:Su-Ke Li is currently an assis-tant professor in Peking University. He received his B.S. degree from North China Electric Power Univer-sity. He received the M.S. and Ph.D. degrees from Peking University, in 2002 and 2012 respectively. His re-search interests include financial data mining, embedded system, Web min-ing and retrieval, opinion mining, so-cial network, and information security.
  • Supported by:

    This work is supported by the National Natural Science Foundation of China under Grant No. 61170263.

Web 2.0 technology leads Web users to publish a large number of consumer reviews about products and services on various websites. Major product features extracted from consumer reviews may let product providers find what features are mostly cared by consumers, and also may help potential consumers to make purchasing decisions. In this work, we propose a linear regression with rules-based approach to ranking product features according to their importance. Empirical experiments show our approach is effective and promising. We also demonstrate two applications using our proposed approach. The first application decomposes overall ratings of products into product feature ratings. And the second application seeks to generate consumer surveys automatically.

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