ABSTRACT
The increasing pervasiveness of the Internet has dramatically changed the way that consumers shop for goods. Consumer-generated product reviews have become a valuable source of information for customers, who read the reviews and decide whether to buy the product based on the information provided. In this paper, we use techniques that decompose the reviews into segments that evaluate the individual characteristics of a product (e.g., image quality and battery life for a digital camera). Then, as a major contribution of this paper, we adapt methods from the econometrics literature, specifically the hedonic regression concept, to estimate: (a) the weight that customers place on each individual product feature, (b) the implicit evaluation score that customers assign to each feature, and (c) how these evaluations affect the revenue for a given product. Towards this goal, we develop a novel hybrid technique combining text mining and econometrics that models consumer product reviews as elements in a tensor product of feature and evaluation spaces. We then impute the quantitative impact of consumer reviews on product demand as a linear functional from this tensor product space. We demonstrate how to use a low-dimension approximation of this functional to significantly reduce the number of model parameters, while still providing good experimental results. We evaluate our technique using a data set from Amazon.com consisting of sales data and the related consumer reviews posted over a 15-month period for 242 products. Our experimental evaluation shows that we can extract actionable business intelligence from the data and better understand the customer preferences and actions. We also show that the textual portion of the reviews can improve product sales prediction compared to a baseline technique that simply relies on numeric data.
- Berndt, E. R. The Practice of Econometrics: Classic and Contemporary. Addison-Wesley, 1996.Google Scholar
- Bickart, B., and Schindler, R. M. Internet forums as in?uential sources of consumer information. Journal of Interactive Marketing 15, 3 (2001), 31--40.Google ScholarCross Ref
- Carenini, G., Ng, R. T., and Zwart, E. Extracting knowledge from evaluative text. In K-CAP '05: Proceedings of the 3rd International Conference on Knowledge Capture (2005), pp. 11--18. Google ScholarDigital Library
- Chen, Y., and Xie, J. Online consumer review: A strategic analysis of an emerging type of word-of-mouth. University of Arizona, Working Paper, 2004.Google Scholar
- Chevalier, J. A., and Goolsbee, A. Measuring prices and price competition online: Amazon.com and BarnesandNoble.com. Quantitative Marketing and Economics 1, 2 (2003), 203--222.Google ScholarCross Ref
- 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
- Das, S. R., and Chen, M. Yahoo! for Amazon: Sentiment extraction from small talk on the web. Working Paper, Santa Clara University. Available at http://scumis.scu.edu/~srdas/chat.pdf, 2006.Google Scholar
- Dave, K., Lawrence, S., and Pennock, D. M. Mining the peanut gallery: Opinion extraction and semantic classi?cation of product reviews. In Proceedings of the 12th International World Wide Web Conference (WWW12)(2003), pp. 519--528. Google ScholarDigital Library
- Ghani, R., Probst, K., Liu, Y., Krema, M., and Fano, A. Text mining for product attribute extraction. SIGKDD Explorations 1, 8 (June 2006), 41--48. Google ScholarDigital Library
- 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
- Ghose, A., and Sundararajan, A. Evaluating pricing strategy using ecommerce data: Evidence and estimation challenges. Statistical Science 21, 2 (2006), 131--142.Google ScholarCross Ref
- Greene, W. H. Econometric Analysis, 5th ed. Prentice Hall, 2002.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), pp. 78--87. Google ScholarDigital Library
- Hastie, T., Tibshirani, R., and Friedman, J. H. The Elements of Statistical Learning. Springer Verlag, Aug. 2001.Google ScholarCross Ref
- 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, M., and Liu, B. Mining opinion features in customer reviews. In Proceeding of the 2004 AAAI Spring Symposium Series: Semantic Web Services (2004), pp. 755--760. Google ScholarDigital Library
- Lee, T. Use-centric mining of customer reviews. In Workshop on Information Technology and Systems (2004).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 (WWW2005)(2005), pp. 342--351. Google ScholarDigital Library
- Nakagawa, H., and Mori, T. A simple but powerful automatic term extraction method. In COMPUTERM 2002: Second International Workshop on Computational Terminology (2002), pp. 1--7. 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 classi?cation using machine learning techniques. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2002) (2002). Google ScholarDigital Library
- Popescu, A.-M., and Etzioni, O. Extracting product features and opinions from reviews. In Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP2005) (2005), pp. 339--346. Google ScholarDigital Library
- Rosen, S. Hedonic prices and implicit markets: Product differentiation in pure competition. The Journal of Political Economy 82, 1 (Jan.-Feb. 1974), 34--55.Google Scholar
- Samuelson, P. A., and Nordhaus, W. D. Economics, 18th ed. McGraw-Hill/Irwin, 2004.Google Scholar
- Scaffidi, C. Application of a probability-based algorithm to extraction of product features from online reviews. Tech. Rep. CMU-ISRI-06-111, Institute for Software Research, School of Computer Science, Carnegie Mellon University, June 2006.Google Scholar
- 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 classi?cation of reviews. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL 2002) (2002), pp. 417--424. Google ScholarDigital Library
- Turney, P. D., and Littman, M. L. Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems 21, 4 (Dec. 2003), 315--346. 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
- Wooldridge, J. M. Econometric Analysis of Cross Section and Panel Data. The MIT Press, 2001.Google Scholar
Index Terms
- Show me the money!: deriving the pricing power of product features by mining consumer reviews
Recommendations
Designing novel review ranking systems: predicting the usefulness and impact of reviews
ICEC '07: Proceedings of the ninth international conference on Electronic commerceWith 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 ...
Deriving the Pricing Power of Product Features by Mining Consumer Reviews
Increasingly, user-generated product reviews serve as a valuable source of information for customers making product choices online. The existing literature typically incorporates the impact of product reviews on sales based on numeric variables ...
An Empirical Examination of the Decision to Invest in Fulfillment Capabilities: A Study of Internet Retailers
Internet technology has allowed for a higher degree of decoupling between the information-intensive sales process and the physical process of inventory management than its brick-and-mortar counterpart. As a result, some Internet retailers choose to ...
Comments