Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach
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...
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| Published in | Decision Support Systems Vol. 50; no. 2; pp. 511 - 521 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Amsterdam
Elsevier B.V
2011
Elsevier Elsevier Sequoia S.A |
| Subjects | |
| Online Access | Get full text |
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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. |
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| AbstractList | 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. 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. [PUBLICATION ABSTRACT] |
| Author | Duan, Wenjing Gan, Qiwei Cao, Qing |
| Author_xml | – sequence: 1 givenname: Qing surname: Cao fullname: Cao, Qing email: qing.cao@ttu.edu organization: Area of Information Systems and Quantitative Sciences, Rawls College of Business, Texas Tech University, Lubbock, TX 79409-2101, United States – sequence: 2 givenname: Wenjing surname: Duan fullname: Duan, Wenjing email: wduan@gwu.edu organization: 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 – sequence: 3 givenname: Qiwei surname: Gan fullname: Gan, Qiwei email: qiwei.gan@ttu.edu organization: Area of Information Systems and Quantitative Sciences, Rawls College of Business, Texas Tech University, Lubbock, TX 79409-2101, United States |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=23818595$$DView record in Pascal Francis |
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| Cites_doi | 10.1016/j.jretai.2008.04.005 10.2307/25148852 10.1287/mksc.1050.0137 10.1109/TAC.1974.1100705 10.1287/isre.1040.0023 10.2307/2529336 10.1080/01638539809545028 10.1287/isre.1070.0154 10.1287/mnsc.49.10.1407.17308 10.1016/j.dss.2008.02.009 10.1207/S15327957PSPR0504_2 10.1509/jmkr.43.3.345 10.1016/j.dss.2008.04.001 10.1145/361219.361220 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 10.1093/biomet/54.1-2.167 10.1287/mnsc.46.4.563.12061 10.1016/j.dss.2007.07.008 10.1287/isre.1060.0106 10.1086/208674 10.1287/isre.1080.0193 10.1086/209256 |
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| Keywords | 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 |
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| Snippet | The “helpfulness” feature of online user reviews helps consumers cope with information overloads and facilitates decision-making. However, many online user... The "helpfulness" feature of online user reviews helps consumers cope with information overloads and facilitates decision-making. However, many online user... |
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| SubjectTerms | 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 |
| Title | Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach |
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