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

被引：478

作者：

Cao, Qing ^{[2
]}

Duan, Wenjing ^{[1
]}

Gan, Qiwei ^{[2
]}

机构：

[1] George Washington Univ, Dept Informat Syst & Technol Management, Sch Business, Washington, DC 20052 USA

[2] Texas Tech Univ, Area Informat Syst & Quantitat Sci, Rawls Coll Business, Lubbock, TX 79409 USA

来源：

DECISION SUPPORT SYSTEMS | 2011年 / 50卷 / 02期

关键词：

Online user review; Helpfulness; Text mining; Ordinal logistic regression; Latent Semantic Analysis; WORD-OF-MOUTH; SALES; DYNAMICS; MODEL;

D O I：

10.1016/j.dss.2010.11.009

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：511 / 521

页数：11

共 31 条

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