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Article
Investigating Determinants of Voting for the “Helpfulness” of Online Consumer Reviews: A Text Mining Approach
AMCIS 2010 Proceedings
  • Wenjing Duan, The George Washington University
  • Qing Cao, Texas Tech University
  • Qiwei Gan, Texas Tech University
Publication Date
8-1-2010
Abstract

The “helpfulness” feature of online user reviews helps consumers cope with information overloads and facilitates decision making. However, many online user reviews lack sufficient helpfulness votes for other users to evaluate their true helpfulness level. This study empirically examines the impact of the various features, that is, basic, stylistic, and semantic, of online user reviews on the number of helpfulness votes those reviews receive. Text mining techniques are employed to extract semantic characteristics from review texts. Our findings show that the semantic characteristics are more influential than other characteristics in affecting how many helpfulness votes reviews receive. Our findings also suggest that reviews with extreme opinions receive more helpfulness votes than those with mixed or neutral opinions. This paper sheds light on the understanding of online users’ helpfulness voting behavior and the design of a better helpfulness voting mechanism for online user review systems.

Citation Information
Wenjing Duan, Qing Cao and Qiwei Gan. "Investigating Determinants of Voting for the “Helpfulness” of Online Consumer Reviews: A Text Mining Approach" (2010)
Available at: http://works.bepress.com/qiwei_gan/2/