Paper Info
Title
Exploring determinants of voting for the "helpfulness" of online user reviews: A text mining approach
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.
Year
DOI
Venue
2011
10.1016/j.dss.2010.11.009
Decision Support Systems
Keywords
Field
DocType
semantic characteristic,online user,true helpfulness level,text mining approach,helpfulness voting mechanism,sufficient helpfulness vote,helpfulness vote,exploring determinant,online user review system,online user review,helpfulness voting behavior,helpfulness votes review,latent semantic analysis,text mining,logistic regression,information overload,ordinal logistic regression,voting behavior
Data mining,World Wide Web,Knowledge representation and reasoning,User assistance,Helpfulness,Voting,Computer science,Decision support system,Voting behavior,Latent semantic analysis,Semantics
Journal
Volume
Issue
ISSN
50
2
Decision Support Systems
Citations 
PageRank 
References 
115
2.63
16
Authors
3
Search Limit
100115
Name
Order
Citations
PageRank
Qing Cao128610.64
Wenjing Duan292845.64
Qiwei Gan31294.82