Deciphering word-of-mouth in social media: Text-based metrics of consumer reviews

ACM Transactions on Management Information Systems

Volumn 3, Issue 1, 2012, Pages

  Zhang, Zhu ^a   Li, Xin ^b   Chen, Yubo ^a  

^a UNIVERSITY OF ARIZONA   (United States)

^b CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Consumer reviews; Design; Performance; Sentiment analysis; Social media; Text mining; Word of mouth

Indexed keywords

PERFORMANCE; SENTIMENT ANALYSIS; SOCIAL MEDIA; TEXT MINING; WORD-OF-MOUTH;

COMPETITIVE INTELLIGENCE; CONSUMER PRODUCTS; DESIGN; PURCHASING;

SALES;

EID: 84859727202     PISSN: 2158656X     EISSN: 21586578     Source Type: Journal    
DOI: 10.1145/2151163.2151168     Document Type: Review

References (80)

1. 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.
2. 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. (Pubitemid 44850555)
3. 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.
4. 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. (Pubitemid 38799762)
5. ASQUITH, P. AND MULLINS, D. 1986. Equity issues and offering dilution. J. Finan. Econ. 15, 1- 2, 61-89.
6. BAGNOLI, M., BENEISH, M. D., AND WATTS, S. G. 1999. Whisper forecasts of quarterly earnings per share. J. Account. Econ. 28, 1, 27-50.
7. 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.
8. BOIY, E. AND MOENS, M. F. 2009. A machine learning approach to sentiment analysis in multilingual web texts. Infor. Retriev. 12, 5, 526-558.
9. CHANEY, P. K., DEVINNEY, T. M., ANDWINER, R. S. 1991. The impact of new product introductions on the market value of firms. J. Business 64, 4, 573-610.
10. CHEN, H. 2010. Business and market intelligence 2.0. IEEE Intell. Syst. 25, 6, 2-5.
11. 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. (Pubitemid 351463128)
12. 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.
13. CHEVALIER, J. AND GOOLSBEE, A. 2003. Measuring prices and price competition online: Amazon.com and Barnes and Noble. com. Quant. Market. Econ. 1, 203-222.
14. 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. (Pubitemid 44367333)
15. CHUNG, W. 2009. Automatic summarization of customer reviews: An integrated approach. In Proceedings of the Americas Conference on Information Systems.
16. DAS, S., MARTINEZ-JEREZ, A., AND TUFANO, P. 2005. E Information: A clinical study of investor discussion and sentiment. Financ. Manag. 34, 3, 103-137. (Pubitemid 41730755)
17. 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. (Pubitemid 351159590)
18. 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.
19. DELLAROCAS, C. 2003. The digitization of word-of-mouth: Promise and challenges of online feedback mechanisms. Manag. Science 49, 10, 1407-1424.
20. 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.
21. 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.
22. 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.
23. ESULI, A. AND SEBASTIANI, F. 2006. Senti Word Net: A publicly available lexical resource for opinion mining. In Proceedings of the 5th Conference on Language Resources and Evaluation.
24. 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.
25. GENTZKOW, M. AND SHAPIRO, J. M. 2006, Media bias and reputation. J. Political Econ. 114, 2, 280-316.
26. 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. (Pubitemid 43439311)
27. 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.
28. GODES, D. AND MAYZLIN, D. 2004. Using online conversations to study word-of-mouth communication. Market. Science 23, 4, 545-560.
29. 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. (Pubitemid 43201203)
30. HAIR, J., ANDERSON, R., TATHAM, R., AND BLACK, W. 1995. Multivariate Data Analysis, Prentice-Hall.
31. 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.
32. HOLTHAUSEN, R. AND LEFTWICH, R. 1986. The effect of bond rating changes on common stock prices. J. Financ. Econo. 17, 1, 57-89.
33. HOTELLING, H. 1929. Stability in competition. Econ. J. 39, 153, 41-57.
34. HU, Y. AND LI, W. J. 2011. Document sentiment classification by exploring description model of topical terms. Comput. Speech Lang. 25, 2, 386-403.
35. KAHN, B. AND MEYER, R. 1991. Consumer multiattribute judgments under attribute uncertainty. J. Consum. Res. 17, 4, 508-522.
36. KAHNEMAN, D. AND TVERSKY, A. 1979. Prospect theory: An analysis of decision under risk. Econometrica 47, 3, 263-291.
37. KENNEDY, A. AND INKPEN, D. 2006. Sentiment classification of movie reviews using contextual valence shifters. Comput. Intell. 22, 2, 110-125. (Pubitemid 43787303)
38. KULLBACK, S. AND LEIBLER, R. A. 1951. On information and sufficiency. Annals Math. Stat. 22, 1, 79-86.
39. 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/.
40. 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.
41. LI, X. X. AND HITT, L. M. 2008. Self-selection and information role of online product reviews. Info. Syst. Res. 19, 4, 456-474.
42. LIN, J. H. 1991. Divergence measures based on the Shannon entropy. IEEE Trans. Info. Theory 37, 1, 145-151.
43. 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.
44. LIU, Y. 2006. Word-of-mouth for movies: Its dynamics and impact on box office revenue. J. Market. 70, 3, 74-89. (Pubitemid 44116460)
45. 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.
46. 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. (Pubitemid 350165010)
47. MASON, C. H. AND PERREAULT, W. D. 1991. Collinearity, power, and interpretation of multiple-regression analysis. J. Market. Res. 28, 3, 268-280.
48. MISHNE, G. 2005. Experiments with mood classification in blog posts. In Proceedings of the 1st Workshop on Stylistic Analysis of Text for Information Access.
49. MISHNE, G. 2006. Multiple ranking strategies for opinion retrieval in blogs. In Proceedings of the Text Retrieval Conference.
50. MIZERSKI, R.W. 1982. An attribution explanation of the disproportionate influence of unfavorable information. J. Consum. Res. 9, 3, 301-310.
51. MULLAINATHAN, S. AND SHLEIFER, A. 2005. The market for news. Amer. Econ. Rev. 95, 1031-1053. (Pubitemid 43015658)
52. 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.
53. 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.
54. 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.
55. PANG, B. AND LEE, L. 2008. Opinion mining and sentiment analysis. Foundat. Trends Info. Retriev. 2, 1-2, 1-135.
56. 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.
57. POPESCU, A. M. AND ETZIONI, O. 2005. Extracting product features and opinions from reviews. In Proceedings of theHuman Language Technology Conference and Conference on EmpiricalMethods in Natural Language Processing. 339-346.
58. SARVARY, M. AND PARKER, P. 1997. Marketing information: A competitive analysis. Market. Science 16, 1, 24-38.
59. SUBASIC, P. AND HUETTNER, A. 2001. Affect analysis of text using fuzzy semantic typing. IEEE Trans. Fuzzy Syst. 9, 4, 483-496. (Pubitemid 32933037)
60. SUBRAHMANIAN, V. S. AND REFORGIATO, D. 2008. AVA: Adjective-verb-adverb combinations for sentiment analysis. IEEE Intell. Syst. 23, 4, 43-50.
61. SUN, M. 2009. How does variance of product ratings matter? http://papers.ssrn.com/sol3/papers.cfm?abstractid=1400173).
62. SUROWIECKI, J. 2005. The Wisdom of Crowds. Anchor Books, New York.
63. TAN, S. B. AND ZHANG, J. 2008. An empirical study of sentiment analysis for Chinese documents. Expert Syst. Appl. 34, 4, 2622-2629. (Pubitemid 351173767)
64. 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.
65. 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.
66. TUMARKIN, R. AND WHITELAW, R. F. 2001. News or noise? Internet postings and stock prices. Financ. Anal. J. 57, 3, 41-51. (Pubitemid 33674139)
67. 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.
68. 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.
69. WIEBE, J., WILSON, T., BRUCE, T., BELL, M., AND MARTIN, M. 2004. Learning subjective language. Computat. Linguist. 30, 3, 277-308. (Pubitemid 41461901)
70. WILSON, T., HOFFMANN, P., SOMASUNDARAN, S., KESSLER, J., WIEBE, J., CHOI, Y., CARDIE, C., RILOFF, E., AND PATWARDHAN, S. 2005. Opinion Finder: A system for subjectivity analysis. In Proceedings of the Human Language Technology Conference/Conference on Empirical Methods in Natural Language Processing.
71. 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.
72. 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.
73. WOODS, B. 2002. Should online shoppers have their say? E-Commerce Times. http://www.ecommercetimes.com/perl/story/20049.html.
74. WOOLDRIDGE, J. 2002. Econometric Analysis of Cross Section and Panel Data, MIT Press.
75. 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.
76. 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.
77. 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.
78. 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.
79. ZHANG, Z. 2008.Weighing stars: Aggregating Online product reviews for intelligent e-commerce applications. IEEE Intell. Syst. 23, 5, 42-49.
80. 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.