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

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 o...