Zhu Zhang
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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 thereof are related to consumer purchases and product sales. By integrating network analysis with text sentiment mining techniques, we propose product comparison networks as a novel construct, computed from consumer product reviews. To test the validity of these product ranking measures, we conduct an empirical study based on a digital camera dataset from Amazon.com. The results demonstrate significant linkage between network-based measures and product sales, which is not fully captured by existing review measures such as numerical ratings. 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 WOM in social media also constitutes a competitive landscape for firms to understand and manipulate.
- Abbasi, A., France, S., Zhang, Z., and Chen, H. 2011. Selecting attributes for sentiment classification using feature relation networks. IEEE Trans. Knowl. Data Eng. 23, 3, 447--462. Google Scholar
Digital Library
- Airoldi, E., Bai, X., and Padman, R. 2006. Markov blankets and meta-heuristics search: Sentiment extraction from unstructured texts. Lect. Notes Artif. Intell. 3932, 167--187. Google ScholarDigital Library
- 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
- Archak, N., Ghose, A., and Ipeirotis, P. G. 2011. Deriving the pricing power of product features by mining consumer reviews. Manage. Sci. 57, 8, 1185--1509. Google ScholarDigital Library
- 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. Inform. Retriev. 12, 5, 526--558. Google ScholarDigital Library
- 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 new element of marketing communication mix. Manage. 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. Marketing Res. 48, 2, 238--254.Google ScholarCross Ref
- Chevalier, J. and Goolsbee, A. 2003. Measuring prices and price competition online: Amazon.com and BarnesandNoble.com. Quant. Market. Econ. 1, 2, 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 15th American 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. Manage. 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. Manage. Sci. 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 12th International World Wide Web Conference. Google ScholarDigital Library
- Dellarocas, C. 2003. The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Manage. Sci. 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. Interactive Market. 21, 4, 23--45.Google ScholarCross Ref
- Duan, W., Gu, B., and Whinston, A. B. 2008. Do online reviews matter? An empirical investigation of panel data. Dec. Supp. 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, Music, and Design.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, 417--422.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. Inform. Syst. Res. 19, 3, 291--313.Google ScholarDigital Library
- Ghose, A. and Ipeirotis, P. G. 2011. Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Trans. Knowl. Data Eng. 23, 10, 1498--1512. Google ScholarDigital Library
- Godes, D. and Mayzlin, D. 2004. Using online conversations to study word-of-mouth communication. Market. Sci. 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., and Verlegh, P. 2005. The firm’s management of social interactions. Market. Lett. 16, 3--4, 415--428.Google ScholarCross Ref
- Hausman, J. A. 1978. Specification tests in econometrics. Econometrica 46, 6, 1251--1271.Google ScholarCross Ref
- 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. Consumer Res. 17, 4, 454--462.Google ScholarCross Ref
- Hu, M. and Liu, B. 2004. Mining and summarizing customer reviews. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Google ScholarDigital Library
- Hu, Y. and Li, W. 2011. Document sentiment classification by exploring description model of topical terms. Comput. Speech Lang. 25, 2, 386--403. Google ScholarDigital Library
- Jindal, N. and Liu, B. 2006a. Identifying comparative sentences in text documents. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development on Information Retrieval. Google ScholarDigital Library
- Jindal, N. and Liu, B. 2006b. Mining comparative sentences and relations. In Proceedings of the 21st National Conference on Artificial Intelligence. Google ScholarDigital Library
- Kennedy, A. and Inkpen, D. 2006. Sentiment classification of movie reviews using contextual valence shifters. Comput. Intell. 22, 2, 110--125.Google ScholarCross Ref
- Kleinberg, J. 1999. Authoritative sources in a hyperlinked environment. J. ACM. 46, 5, 604--632. Google ScholarDigital Library
- Langville, A. N. and Meyer, C. D. 2006. Google’s PageRank and Beyond: The Science of Search Engine Rankings. Princeton University Press, Princeton, NJ. Google ScholarDigital Library
- Li, N. and Wu, D. D. 2010. Using text mining and sentiment analysis for online forums hotspot detection and forecast. Decis. Supp. Syst. 48, 2, 354--368. Google ScholarDigital Library
- Li, X. and Hitt, L. M. 2008. Self-selection and information role of online product reviews. Inform. Syst. Res. 19, 4, 456--474.Google ScholarCross Ref
- Li, X. and Hitt, L. M. 2010. Price effects in online product reviews: An analytical model and empirical analysis, MIS Quart. 34, 4, 809--831. Google ScholarDigital Library
- Liu, B. 2012. Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers.Google Scholar
- Liu, B., Hu, M., and Cheng, J. 2005. Opinion observer: Analyzing and comparing opinions on the Web. In Proceedings of the 14th International World Wide Web Conference, 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 15th Text Retrieval Conference.Google Scholar
- Mizerski, R.W. 1982. An attribution explanation of the disproportionate influence of unfavorable information. J. Consumer Res. 9, 3, 301--310.Google ScholarCross Ref
- Nasukawa, T. and Yi, J. 2003. Sentiment analysis: Capturing favorability using natural language processing. In Proceedings of the 2nd International Conference on Knowledge Capture. 70--77. Google ScholarDigital Library
- Netzer, O., Feldman, R., Goldenberg, J., and Fresko, M. 2012. Mine your own business: Market structure surveillance through text mining. Market. Sci. 31, 3, 521--543. Google ScholarDigital Library
- Nigam, K. and Hurst, M. 2004. Towards a robust metric of opinion. In Proceedings of AAAI Spring Symposium on Exploring Attitude and Affect in Text. 598--603.Google Scholar
- Oestreicher-Singer, G. and Sundararajan, A. 2012. Recommendation networks and the long tail of economic networks. MIS Quart. 36, 1, 65--83. Google ScholarDigital Library
- O’Madadhain, J., Fisher, D., Smyth, P., White, S., and Boey, Y.-B. 2005. Analysis and visualization of network data using JUNG. J. Statist. Softw. VV 1--35.Google Scholar
- Page, L., Brin, S., Motwani, R., and Winograd, T. 1998. The pagerank citation ranking: Bringing order to the web, Stanford InfoLab. http://ilpubs.stanford.edu:8090/422/.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 43rd Annual Meeting on Association for Computational Linguistics. 115--124. Google ScholarDigital Library
- Pang, B. and Lee, L. 2008. Opinion mining and sentiment analysis. Found. Trends Inform. Retrieval 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 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 Conference on Human Language Technology and Empirical Methods in Natural Language Processing. 339--346. Google ScholarDigital Library
- Porter, M. E. 1980. Competitive Strategy: Techniques for Analyzing Industries and Competitors. Free Press, NY.Google Scholar
- Porter, M. E. 1985. The Competitive Advantage: Creating and Sustaining Superior Performance. Free Press, NY.Google Scholar
- Prescott, J. 1999. The Evolution of Competitive Intelligence, Designing a Process for Action. APMP Professional J. Spring.Google Scholar
- 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
- Surowiecki, J. 2005. The Wisdom of Crowds. Anchor Books, New York. Google ScholarDigital Library
- Teo, T. S. H. and Choo, W. Y. 2001. Assessing the impact of using the Internet for competitive intelligence. Inform. Manage. 39, 1, 67--83. 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. Inform. Sci. Tech. 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
- Walker, M., Joshi, A. K., and Prince, E. F. 1998. Centering Theory in Discourse. Clarendon Press, Oxford, UK.Google Scholar
- Wiebe, J., Wilson, T., Bruce, T., Bell, M., and Martin, M. 2004. Learning subjective language. Comput. Ling. 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 Conference on Human Language Technology and 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. Comput. Ling. 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
- Yang, C., Tang, X., Wong, Y. C., and Wei, C.-P. 2010. Understanding online consumer review opinion with sentiment analysis using machine learning. Pacific Asia J. Assoc. Inform. 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
- Xu, K., Liao, S. S., Li, J., and Song, Y. 2011. Mining comparison opinions from customer reviews for competitive Intelligence. Dec. Supp. Syst. 50, 4, 743--754. 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., Zeng, D., Li J., Wang, F.-Y., and Zuo, W. 2009. Sentiment analysis of Chinese documents: From sentence to document level. J. Amer. Soc. Inform. Sci. Tech. 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
- Zhang, Z., Li, X., and Chen, Y. 2012. Deciphering word-of-mouth in social Media: Text-based metrics of consumer reviews. ACM Trans. Manage. Inf. Syst. 3, 1. 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, 2, 133--148.Google ScholarCross Ref
Index Terms
- Product Comparison Networks for Competitive Analysis of Online Word-of-Mouth
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