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

• Published in: Decision Support Systems Vol. 50; no. 2; pp. 511 - 521
• Main Authors: Cao, Qing, Duan, Wenjing, Gan, Qiwei
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 2011; Elsevier; Elsevier Sequoia S.A
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Data mining; Decision making; Decision support systems; Decision theory. Utility theory; Design engineering; Determinants; Exact sciences and technology; Helpfulness; Impact analysis; Information systems. Data bases; Latent Semantic Analysis; Memory organisation. Data processing; Mining; On-line systems; Online; Online user review; Operational research and scientific management; Operational research. Management science; Ordinal logistic regression; Semantics; Software; Speech and sound recognition and synthesis. Linguistics; Studies; Text mining; Texts; User behavior; Voting; Text mining; Latent Semantic Analysis; Online user review; Helpfulness; Ordinal logistic regression; Evaluation; Data analysis; Decision support system; Semantic analysis; Decision making; Consumer products; Knowledge representation; Text; Data mining; Logistic regression; Overload; Voting; Semantics; User assistance; Electronic trade; User behavior
• ISSN: 0167-9236; 1873-5797
• DOI: 10.1016/j.dss.2010.11.009

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.