Arjun Talwar
ABSTRACT
Online reviews have become increasingly popular as a way to judge the quality of various products and services. Previous work has demonstrated that contradictory reporting and underlying user biases make judging the true worth of a service difficult. In this paper, we investigate underlying factors that influence user behavior when reporting feedback. We look at two sources of information besides numerical ratings: linguistic evidence from the textual comment accompanying a review, and patterns in the time sequence of reports. We first show that groups of users who amply discuss a certain feature are more likely to agree on a common rating for that feature. Second, we show that a user's rating partly reflects the difference between true quality and prior expectation of quality as inferred from previous reviews. Both give us a less noisy way to produce rating estimates and reveal the reasons behind user bias.Our hypotheses were validated by statistical evidence from hotel reviews on the TripAdvisor website.
- A. Admati and P. Pfleiderer. Noisytalk.com: Broadcasting opinions in a noisy environment. Working Paper 1670R, Stanford University, 2000.Google Scholar
- P. B., L. Lee, and S. Vaithyanathan. Thumbs up? sentiment classification using machine learning techniques. In Proceedings of the EMNLP-02, the Conference on Empirical Methods in Natural Language Processing, 2002. Google ScholarDigital Library
- H. Cui, V. Mittal, and M. Datar. Comparative Experiments on Sentiment Classification for Online Product Reviews. In Proceedings of AAAI, 2006. Google ScholarDigital Library
- K. Dave, S. Lawrence, and D. Pennock. Mining the peanut gallery:opinion extraction and semantic classification of product reviews. In Proceedings of the 12th International Conference on the World Wide Web (WWW03), 2003. Google ScholarDigital Library
- C. Dellarocas, N. Awad, and X. Zhang. Exploring the Value of Online Product Ratings in Revenue Forecasting: The Case of Motion Pictures. Working paper, 2006.Google Scholar
- C. Forman, A. Ghose, and B. Wiesenfeld. A Multi-Level Examination of the Impact of Social Identities on Economic Transactions in Electronic Markets. Available at SSRN: http://ssrn.com/abstract=918978, July 2006.Google Scholar
- A. Ghose, P. Ipeirotis, and A. Sundararajan. Reputation Premiums in Electronic Peer-to-Peer Markets: Analyzing Textual Feedback and Network Structure. In Third Workshop on Economics of Peer-to-Peer Systems, (P2PECON), 2005. Google ScholarDigital Library
- A. Ghose, P. Ipeirotis, and A. Sundararajan. The Dimensions of Reputation in electronic Markets. Working Paper CeDER-06-02, New York University, 2006.Google Scholar
- A. Harmon. Amazon Glitch Unmasks War of Reviewers. The New York Times, February 14, 2004.Google Scholar
- D. Houser and J. Wooders. Reputation in Auctions: Theory and Evidence from eBay. Journal of Economics and Management Strategy, 15:353--369, 2006.Google ScholarCross Ref
- M. Hu and B. Liu. Mining and summarizing customer reviews. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD04), 2004. Google ScholarDigital Library
- N. Hu, P. Pavlou, and J. Zhang. Can Online Reviews Reveal a Product's True Quality? In Proceedings of ACM Conference on Electronic Commerce (EC 06), 2006. Google ScholarDigital Library
- K. Kalyanam and S. McIntyre. Return on reputation in online auction market. Working Paper 02/03-10-WP, Leavey School of Business, Santa Clara University., 2001.Google Scholar
- L. Khopkar and P. Resnick. Self-Selection, Slipping, Salvaging, Slacking, and Stoning: the Impacts of Negative Feedback at eBay. In Proceedings of ACM Conference on Electronic Commerce (EC 05), 2005. Google ScholarDigital Library
- M. Melnik and J. Alm. Does a seller's reputation matter? evidence from ebay auctions. Journal of Industrial Economics, 50(3):337--350, 2002.Google ScholarCross Ref
- R. Olshavsky and J. Miller. Consumer Expectations, Product Performance and Perceived Product Quality. Journal of Marketing Research, 9:19--21, February 1972.Google ScholarCross Ref
- A. Parasuraman, V. Zeithaml, and L. Berry. A Conceptual Model of Service Quality and Its Implications for Future Research. Journal of Marketing, 49:41--50, 1985.Google ScholarCross Ref
- A. Parasuraman, V. Zeithaml, and L. Berry. SERVQUAL: A Multiple-Item Scale for Measuring Consumer Perceptions of Service Quality. Journal of Retailing, 64:12--40, 1988.Google Scholar
- P. Pavlou and A. Dimoka. The Nature and Role of Feedback Text Comments in Online Marketplaces: Implications for Trust Building, Price Premiums, and Seller Differentiation. Information Systems Research, 17(4):392--414, 2006. Google ScholarDigital Library
- A. Popescu and O. Etzioni. Extracting product features and opinions from reviews. In Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, 2005. Google ScholarDigital Library
- R. Teas. Expectations, Performance Evaluation, and Consumers' Perceptions of Quality. Journal of Marketing, 57:18--34, 1993.Google Scholar
- E. White. Chatting a Singer Up the Pop Charts. The Wall Street Journal, October 15, 1999.Google Scholar
Index Terms
- Understanding user behavior in online feedback reporting
Recommendations
User-aware topic modeling of online reviews
The online reviews are one type of social media which are opinions generated by the users to comment on some special items. Since the sentiments are dependent on topics, probabilistic topic models have been widely used for sentiment analysis. However, ...
Hybrid recommendation algorithm based on multi-attribute rating from online reviews
ACM TURC '19: Proceedings of the ACM Turing Celebration Conference - ChinaAs the growing users and products in network shopping platform under the big data environment, the data sparsity and cold start problem are increasingly prominent which lead to the recommended effect of recommendation algorithm can't be satisfied by ...
Manipulation of online reviews: An analysis of ratings, readability, and sentiments
As consumers become increasingly reliant on online reviews to make purchase decisions, the sales of the product becomes dependent on the word of mouth (WOM) that it generates. As a result, there can be attempts by firms to manipulate online reviews of ...
Comments