Exploring determinants of voting for the "helpfulness" of online user reviews: A text mining approach

Authors:
Qing Cao;Wenjing Duan;Qiwei Gan
Affiliations:
Area of Information Systems and Quantitative Sciences, Rawls College of Business, Texas Tech University, Lubbock, TX 79409-2101, United States;Department of Information System & Technology Management, School of Business, Funger Hall, Suite 515, The George Washington University, 2201 G Street, NW, Washington, DC 20052, United States;Area of Information Systems and Quantitative Sciences, Rawls College of Business, Texas Tech University, Lubbock, TX 79409-2101, United States
Venue:
Decision Support Systems
Year:
2011

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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 characteristics 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.