Abstract
Enabled by Web 2.0 technologies, social media provide an unparalleled platform for consumers to share their product experiences and opinions through word-of-mouth (WOM) or consumer reviews. It has become increasingly important to understand how WOM content and metrics influence consumer purchases and product sales. By integrating marketing theories with text mining techniques, we propose a set of novel measures that focus on sentiment divergence in consumer product reviews. To test the validity of these metrics, we conduct an empirical study based on data from Amazon.com and BN.com (Barnes & Noble). The results demonstrate significant effects of our proposed measures on product sales. This effect is not fully captured by nontextual review measures such as numerical ratings. Furthermore, in capturing the sales effect of review content, our divergence metrics are shown to be superior to and more appropriate than some commonly used textual measures the literature. The findings provide important insights into the business impact of social media and user-generated content, an emerging problem in business intelligence research. From a managerial perspective, our results suggest that firms should pay special attention to textual content information when managing social media and, more importantly, focus on the right measures.
- Abbasi, A., Chen, H. C., and Salem, A. 2008. Sentiment analysis in multiple languages: Feature selection for opinion classification in web forums. ACM Trans. Info. Syst. 26, 3, 1--34. Google ScholarDigital Library
- Airoldi, E., Bai, X., and Padman, R. 2006. Markov blankets and meta-heuristics search: Sentiment extraction from unstructured texts. Lecture Notes in Artificial Intelligence, vol. 3932, 167--187. Google ScholarDigital Library
- Andreas, K. M. and Michael, H. 2010. Users of the world, unite! The challenges and opportunities of social media. Bus. Horizons 53, 1, 59--68.Google ScholarCross Ref
- Antweiler, W. and Frank, M. Z. 2004. Is all that talk just noise? The information content of internet stock message boards. J. Finance 59, 3, 1259--1294.Google ScholarCross Ref
- Asquith, P. and Mullins, D. 1986. Equity issues and offering dilution. J. Finan. Econ. 15, 1--2, 61--89.Google ScholarCross Ref
- Bagnoli, M., Beneish, M. D., and Watts, S. G. 1999. Whisper forecasts of quarterly earnings per share. J. Account. Econ. 28, 1, 27--50.Google ScholarCross Ref
- Bikhchandani, S., Hirshleifer, D., and Welch, I. 1992. A theory of fads, fashion, custom, and cultural-change as informational cascades. J. Polit. Econ. 100, 5, 992--1026.Google ScholarCross Ref
- Boiy, E. and Moens, M. F. 2009. A machine learning approach to sentiment analysis in multilingual web texts. Infor. Retriev. 12, 5, 526--558. Google ScholarDigital Library
- Chaney, P. K., Devinney, T. M., and Winer, R. S. 1991. The impact of new product introductions on the market value of firms. J. Business 64, 4, 573--610.Google ScholarCross Ref
- Chen, H. 2010. Business and market intelligence 2.0. IEEE Intell. Syst. 25, 6, 2--5. Google ScholarDigital Library
- Chen, Y. and Xie, J. 2008. Online consumer review: Word-of-mouth as a news element of marketing communication mix. Manag. Sci. 54, 3, 477--491. Google ScholarDigital Library
- Chen, Y., Wang, Q., and Xie, J. 2011. Online social interactions: A natural experiment on word-of-mouth versus observational learning. J. Market. Res. To appear.Google ScholarCross Ref
- Chevalier, J. and Goolsbee, A. 2003. Measuring prices and price competition online: Amazon.com and BarnesandNoble.com. Quant. Market. Econ. 1, 203--222.Google ScholarCross Ref
- Chevalier, J. A. and Mayzlin, A. 2006. The effect of word-of-mouth on sales: Online book reviews. J. Market. Res. 43, 3, 345--354.Google ScholarCross Ref
- Chung, W. 2009. Automatic summarization of customer reviews: An integrated approach. In Proceedings of the Americas Conference on Information Systems.Google Scholar
- Das, S., Martinez-Jerez, A., and Tufano, P. 2005. eInformation: A clinical study of investor discussion and sentiment. Financ. Manag. 34, 3, 103--137.Google ScholarCross Ref
- Das, S. R. and Chen, M. Y. 2007. Yahoo! for Amazon: Sentiment extraction from small talk on the web. Manag. Science 53, 9, 1375--1388. Google ScholarDigital Library
- Dave, K., Lawrence, S., and Pennock, D. M. 2003. Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of the International Conference on the World Wide Web. Google ScholarDigital Library
- Dellarocas, C. 2003. The digitization of word-of-mouth: Promise and challenges of online feedback mechanisms. Manag. Science 49, 10, 1407--1424. Google ScholarDigital Library
- Dellarocas, C., Zhang, X. Q., and Awad, N. F. 2007. Exploring the value of online product reviews in forecasting sales: The case of motion pictures. J. Interact. Market. 21, 4, 23--45.Google ScholarCross Ref
- Duan, W. J., Gu, G., and Whinston, A. B. 2008. Do online reviews matter? An empirical investigation of panel data. Decision Support Syst. 45, 4, 1007--1016. Google ScholarDigital Library
- Efron, M. 2004. Cultural orientation: Classifying subjective documents by vocation analysis. In Proceedings of the AAAI Fall Symposium on Style and Meaning in Language, Art, and Music.Google Scholar
- Esuli, A. and Sebastiani, F. 2006. SentiWordNet: A publicly available lexical resource for opinion mining. In Proceedings of the 5th Conference on Language Resources and Evaluation.Google Scholar
- Forman, C., Ghose, A., and Wiesenfeld, B. 2008. Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Info. Syst. Res. 19, 3, 291--313.Google ScholarDigital Library
- Gentzkow, M. and Shapiro, J. M. 2006, Media bias and reputation. J. Political Econ. 114, 2, 280--316.Google ScholarCross Ref
- Gao, Y., Mao, C. X., and Zhong, R. 2006. Divergence of opinion and long-term performance of initial public offerings. J. Financ. Res. 29, 1, 113--129.Google ScholarCross Ref
- Ghose, A. and Ipeirotis, P. G. Forthcoming. Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Trans. Knowl. Data Eng. Google ScholarDigital Library
- Godes, D. and Mayzlin, D. 2004. Using online conversations to study word-of-mouth communication. Market. Science 23, 4, 545--560. Google ScholarDigital Library
- Godes, D., Mayzlin, D., Chen, Y., Das, S., Dellarocas, C., Pfeiffer, B., Libai, B., Sen, S., Shi, M. Z., and Verlegh, P. 2005. The firm's management of social interactions. Market. Letters 16, 3-4, 415--428.Google ScholarCross Ref
- Hair, J., Anderson, R., Tatham, R., and Black, W. 1995. Multivariate Data Analysis, Prentice-Hall. Google ScholarDigital Library
- Herr, P. M., Kardes, F. R., and Kim, J. 1991. Effects of word-of-mouth and product-attribute information on persuasion—An accessibility-diagnosticity perspective. J. Consum. Res. 17, 4, 454--462.Google ScholarCross Ref
- Holthausen, R. and Leftwich, R. 1986. The effect of bond rating changes on common stock prices. J. Financ. Econo. 17, 1, 57--89.Google ScholarCross Ref
- Hotelling, H. 1929. Stability in competition. Econ. J. 39, 153, 41--57.Google ScholarCross Ref
- Hu, Y. and Li, W. J. 2011. Document sentiment classification by exploring description model of topical terms. Comput. Speech Lang. 25, 2, 386--403. Google ScholarDigital Library
- Kahn, B. and Meyer, R. 1991. Consumer multiattribute judgments under attribute uncertainty. J. Consum. Res. 17, 4, 508--522.Google ScholarCross Ref
- Kahneman, D. and Tversky, A. 1979. Prospect theory: An analysis of decision under risk. Econometrica 47, 3, 263--291.Google ScholarCross Ref
- Kennedy, A. and Inkpen, D. 2006. Sentiment classification of movie reviews using contextual valence shifters. Comput. Intell. 22, 2, 110--125.Google ScholarCross Ref
- Kullback, S. and Leibler, R. A. 1951. On information and sufficiency. Annals Math. Stat. 22, 1, 79--86.Google ScholarCross Ref
- Lavrusik, V. 2011. Facebook like button takes over share button functionality. http://mashable.com/2011/02/27/facebook-like-button-takes-over-share-button-functionality/.Google Scholar
- Li, N. and Wu, D. D. 2010. Using text mining and sentiment analysis for online forums hotspot detection and forecast. Decision Support Syst. 48, 2, 354--368. Google ScholarDigital Library
- Li, X. X. and Hitt, L. M. 2008. Self-selection and information role of online product reviews. Info. Syst. Res. 19, 4, 456--474.Google ScholarCross Ref
- Lin, J. H. 1991. Divergence measures based on the shannon entropy. IEEE Trans. Info. Theory 37, 1, 145--151.Google ScholarDigital Library
- Liu, B., Hu, M., and Cheng, J. 2005. Opinion observer: Analyzing and comparing opinions on the web. In Proceedings of the 14th International Conference on the World Wide Web. 342--351. Google ScholarDigital Library
- Liu, Y. 2006. Word-of-mouth for movies: Its dynamics and impact on box office revenue. J. Market. 70, 3, 74--89.Google ScholarCross Ref
- Liu, Y., Chen, Y., Lusch, R., Chen, H., Zimbra, D., and Zeng, S. 2010. User-generated content on social media: Predicting new product market success from online word-of-mouth. IEEE Intell. Syst. 25, 6, 8--12.Google Scholar
- Liu, Y., Huang, X., An, A., and Yu, X. 2007. ARSA: A sentiment-aware model for predicting sales performance using blogs. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 607--614. Google ScholarDigital Library
- Mason, C. H. and Perreault, W. D. 1991. Collinearity, power, and interpretation of multiple-regression analysis. J. Market. Res. 28, 3, 268--280.Google ScholarCross Ref
- Mishne, G. 2005. Experiments with mood classification in blog posts. In Proceedings of the 1st Workshop on Stylistic Analysis of Text for Information Access.Google Scholar
- Mishne, G. 2006. Multiple ranking strategies for opinion retrieval in blogs. In Proceedings of the Text Retrieval Conference.Google Scholar
- Mizerski, R. W. 1982. An attribution explanation of the disproportionate influence of unfavorable information. J. Consum. Res. 9, 3, 301--310.Google ScholarCross Ref
- Mullainathan, S. and Shleifer, A. 2005. The market for news. Amer. Econ. Rev. 95, 1031--1053.Google ScholarCross Ref
- Nasukawa, T. and Yi, J. 2003. Sentiment analysis: Capturing favorability using natural language processing. In Proceedings of the International Conference on Knowledge Capture. 70--77. Google ScholarDigital Library
- Nigam, K. and Hurst, M. 2004. Towards a robust metric of opinion. In Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text. 598--603.Google Scholar
- Pang, B. and Lee, L. 2005. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the Meeting of the Association for Computer Learning. 115--124. Google ScholarDigital Library
- Pang, B. and Lee, L. 2008. Opinion mining and sentiment analysis. Foundat. Trends Info. Retriev. 2, 1--2, 1--135. Google ScholarDigital Library
- Pang, B., Lee, L., and Vaithyanathan, S. 2002. Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 79--86. Google ScholarDigital Library
- Popescu, A. M. and Etzioni, O. 2005. Extracting product features and opinions from reviews. In Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing. 339--346. Google ScholarDigital Library
- Sarvary, M. and Parker, P. 1997. Marketing information: A competitive analysis. Market. Science 16, 1, 24--38.Google ScholarDigital Library
- Subasic, P. and Huettner, A. 2001. Affect analysis of text using fuzzy semantic typing. IEEE Trans. Fuzzy Syst. 9, 4, 483--496. Google ScholarDigital Library
- Subrahmanian, V. S. and Reforgiato, D. 2008. AVA: Adjective-verb-adverb combinations for sentiment analysis. IEEE Intell. Syst. 23, 4, 43--50. Google ScholarDigital Library
- Sun, M. 2009. How does variance of product ratings matter? (http://papers.ssrn.com/sol3/papers.cfm?abstract _id=1400173).Google Scholar
- Surowiecki, J. 2005. The Wisdom of Crowds. Anchor Books, New York. Google ScholarDigital Library
- Tan, S. B. and Zhang, J. 2008. An empirical study of sentiment analysis for chinese documents. Expert Syst. Appl. 34, 4, 2622--2629. Google ScholarDigital Library
- Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., and Kappas, A. 2010. Sentiment strength detection in short informal text. J. Amer. Soc. Info. Sci. Tec. 61, 12, 2544--2558. Google ScholarDigital Library
- Thomas, M., Pang, B., and Lee, L. 2006. Get out the vote: Determining support or opposition from congressional floor-debate transcripts. In Proceedings of the Conference on Empirical Methods on Natural Language Processing. 327--335. Google ScholarDigital Library
- Tumarkin, R. and Whitelaw, R. F. 2001. News or noise? Internet postings and stock prices. Financ. Anal. J. 57, 3, 41--51.Google ScholarCross Ref
- Turney, P. D. 2001. Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. 417--424. Google ScholarDigital Library
- West, P. and Broniarczyk, S. 1998. Integrating multiple opinions: The role of aspiration level on consumer response to critic consensus. J. Consum. Res. 25, 1, 38--51.Google ScholarCross Ref
- Wiebe, J., Wilson, T., Bruce, T., Bell, M., and Martin, M. 2004. Learning subjective language. Computat. Linguist. 30, 3, 277--308. Google ScholarDigital Library
- Wilson, T., Hoffmann, P., Somasundaran, S., Kessler, J., Wiebe, J., Choi, Y., Cardie, C., Riloff, E., and Patwardhan, S. 2005. OpinionFinder: A system for subjectivity analysis. In Proceedings of the Human Language Technology Conference/Conference on Empirical Methods in Natural Language Processing. Google ScholarDigital Library
- Wilson, T., Wiebe, J., and Hoffmann, P. 2009. Recognizing contextual polarity: an exploration of features for phrase-level sentiment analysis. Computat. Linguist. 35, 3, 399--433. Google ScholarDigital Library
- Wilson, T., Wiebe, J., and Hwa, R. 2004. Just how mad are you? Finding strong and weak opinion clauses. In Proceedings of the 19th National Conference on Artificial Intelligence. 761--769. Google ScholarDigital Library
- Woods, B. 2002. Should online shoppers have their say? E-Commerce Times. http://www.ecommercetimes.com/perl/story/20049.html.Google Scholar
- Wooldridge, J. 2002. Econometric Analysis of Cross Section and Panel Data, MIT Press.Google Scholar
- Yang, C., Tang, X., Wong, Y. C., and Wei, C.-P. 2010. Understanding online consumer review opinion with sentiment analysis using machine learning. Pacif. Asia J. Assoc. Info. Syst. 2, 3, 7.Google Scholar
- Yi, J., Nasukawa, T., Bunescu, R., Niblack, W., and Center, C. 2003. Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In Proceedings of the 3rd IEEE International Conference on Data Mining. 427--434. Google ScholarDigital Library
- Yu, H. and Hatzivassiloglou, V. 2003. Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 129--136. Google ScholarDigital Library
- Zhang, C. L., Zeng, D., Li, J. X., Wang, F. Y., and Zuo, W. L. 2009. Sentiment analysis of chinese documents: From sentence to document level. J. Amer. Soc. Info. Sci. Tec. 60, 12, 2474--2487. Google ScholarDigital Library
- Zhang, Z. 2008. Weighing stars: Aggregating Online product reviews for intelligent e-commerce applications. IEEE Intell. Syst. 23, 5, 42--49. Google ScholarDigital Library
- Zhu, F. and Zhang, Z. 2010. Impacts of online consumer reviews on sales: The moderating role of product and consumer characteristics. J. Market. 74, 133--148.Google ScholarCross Ref
Index Terms
- Deciphering word-of-mouth in social media: Text-based metrics of consumer reviews
Recommendations
Product Comparison Networks for Competitive Analysis of Online Word-of-Mouth
Enabled by Web 2.0 technologies social media provide an unparalleled platform for consumers to share their product experiences and opinions---through word-of-mouth (WOM) or consumer reviews. It has become increasingly important to understand how WOM ...
Herding and social media word-of-mouth: evidence from groupon
Modern online retailing practices provide consumers with new types of real-time information that can potentially increase demand. In particular, showing sales information to a customer can increase certainty about product quality, inducing consumers to ...
An Empirical Study of Word-of-Mouth Generation and Consumption
Word-of-mouth (WOM) plays an increasingly important role in shaping consumers' attitudes and buying behaviors. Prior work in marketing has mainly focused on the aggregate impact of WOM on product sales as well as the generation of WOM. Very little ...
Comments