Anindya Ghose
Panagiotis G. Ipeirotis
ABSTRACT
With the rapid growth of the Internet, users' ability to publish content has created active electronic communities that provide a wealth of product information. Consumers naturally gravitate to reading reviews in order to decide whether to buy a product. However, the high volume of reviews that are typically published for a single product makes it harder for individuals to locate the best reviews and understand the true underlying quality of a product based on the reviews. Similarly, the manufacturer of a product needs to identify the reviews that influence the customer base, and examine the content of these reviews. In this paper, we propose two ranking mechanisms for ranking product reviews: a consumer-oriented ranking mechanism ranks the reviews according to their expected helpfulness, and a manufacturer-oriented ranking mechanism ranks the reviews according to their expected effect on sales. Our ranking mechanism combines econometric analysis with text mining techniques in general, and with subjectivity analysis in particular. We show that subjectivity analysis can give useful clues about the helpfulness of a review and about its impact on sales. Our results can have several implications for the market design of online opinion forums.
- Archak, N., Ghose, A., and Ipeirotis, PG. Show me the money! Deriving the pricing power of product features by mining consumer reviews. In Proceedings of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2007) (2007). Google Scholar
Digital Library
- Berndt, E. R. The Practice of Econometrics: Classic and Contemporary. Addison-Wesley, 1996.Google Scholar
- Chevalier, J. A., and Mayzlin, D. The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research 43, 3 (Aug. 2006), 345--354.Google ScholarCross Ref
- Cui, H., Mittal, V., and Datar, M. Comparative experiments on sentiment classification for online product reviews. In Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI-2006) (2006). Google ScholarDigital Library
- Das, S. R., and Chen, M. Yahoo! for Amazon: Sentiment extraction from small talk on the web. Working Paper, Santa Clara University.Google Scholar
- Dave, K., Lawrence, S., and Pennock, D. M. Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of the 12th International World Wide Web Conference (WWW12) (2003), pp. 519--528. Google ScholarDigital Library
- Dellarocas, C., Awady, N. F., and Zhangz, X. M. Exploring the value of online product ratings in revenue forecasting: The case of motion pictures. Working Paper, Robert H. Smith School Research Paper, 2007.Google Scholar
- Forman, C., Ghose, A., and Wiesenfeld, B. A multi-level examination of the impact of social identities on economic transactions in electronic markets. Working Paper CeDER-06-09, Stern School of Business, New York University. Available at http://hdl.handle.net/2451/14809, 2006.Google Scholar
- Ghose, A., Ipeirotis, P. G., and Sundararajan, A. Opinion mining using econometrics: A case study on reputation systems. In Proceedings of the 44th Annual Meeting of the Association for Computational Linguistics (ACL 2007) (2007).Google Scholar
- Gruhl, D., Guha, R., Kumar, R., Novak, J., and Tomkins, A. The predictive power of online chatter. In Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2005) (2005), pp78--87. Google ScholarDigital Library
- Hatzivassiloglou, V., and McKeown, K. R. Predicting the semantic orientation of adjectives. In Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics (ACL'97) (1997), pp. 174--181. Google ScholarDigital Library
- Hu, M., and Liu, B. Mining and summarizing customer reviews. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004) (2004), pp. 168--177. Google ScholarDigital Library
- Hu, N., Pavlou, P. A., and Zhang, J. Can online reviews reveal a product's true quality? Empirical findings and analytical modeling of online word-of-mouth communication. In Proceedings of the 7th ACM conference on Electronic commerce (EC'06) (2006), pp. 324--330. Google ScholarDigital Library
- Kim, S.-M., and Hovy, E. Determining the sentiment of opinions. In Proceedings of the 20th International Conference on Computational Linguistics (COLING 2004) (2004), pp. 1367--1373. Google ScholarDigital Library
- Lee, T. Y., and Bradlow, E. T. Automatic construction of conjoint attributes and levels from online customer reviews. University of Pennsylvania, The Wharton School Working Paper OPIM WP 06-08-01, 2006.Google Scholar
- Lewitt, S., and Syverson, C. Market distortions when agents are better informed: The value of information in real estate transactions. Working Paper, University of Chicago, 2005.Google Scholar
- Liu, B., Hu, M., and Cheng, J. Opinion observer: Analyzing and comparing opinions on the Web. In Proceedings of the 14th International World Wide Web Conference (WWW 2005) (2005), pp. 342--351. Google ScholarDigital Library
- Nigam, K., and Hurst, M. Towards a robust metric of opinion. In AAAI Spring Symposium on Exploring Attitude and Affect in Text (2004), pp. 598--603.Google Scholar
- Pang, B., and Lee, L. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL 2004) (2004), pp. 271--278. Google ScholarDigital Library
- Pang, B., and Lee, L. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL 2005) (2005). Google ScholarDigital Library
- Pang, B., Lee, L., and Vaithyanathan, S. Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2002) (2002). Google ScholarDigital Library
- Snyder, B., and Barzilay, R. Multiple aspect ranking using the good grief algorithm. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT-NAACL 2007) (2007).Google Scholar
- Turney, P. D. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL 2002) (2002), pp417--424. Google ScholarDigital Library
- Wilson, T., Wiebe, J., and Hwa, R. Recognizing strong and weak opinion clauses. Computational Intelligence 22, 2 (May 2006), 73--99.Google ScholarCross Ref
Index Terms
- Designing novel review ranking systems: predicting the usefulness and impact of reviews
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