Abstract
Online customer reviews complement information from product and service providers. While the latter is directly from the source of the product and/or service, the former is generally from users of these products and/or services. Clearly, these two information sets are generated from different perspectives with possibly different sets of intentions. For a prospective customer, both these perspectives together provide a complementary set of information and support their purchase decisions. Given the different perspective and incentive structure, the information from these two source sets tends to be necessarily biased, clearly with the high probability of negative information omission from that provided by the product/service providers. Moreover, customers oftentimes face information overload during their attempts at deciphering existing online customer reviews. We attempt to alleviate this through mining hidden information in online customer reviews. We use a variant of the Latent Dirichlet Allocation (LDA) model and clustering to generate equivalent options that the customer could then use in their purchase decisions. We illustrate this using online hotel review data.
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References
Akerlof, G. A. (1970). The market for “Lemons”: quality uncertainty and the market mechanism. The Quarterly Journal of Economics, 84(3), 488–500.
Ba, S. (2001). Establishing online trust through a community responsibility system. Decision Support Systems, 31, 323–336.
Baron, D. P. (2002). Private Ordering on the Internet: The Ebay Community of Traders. Business and Politics (4:3).
Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. The Journal of Machine Learning Research, 3(2003), 993–1022.
Bose, I., & Chen, X. (2015). Detecting the migration of mobile service customers using fuzzy clustering. Information & Management, 52(2), 227–238.
Burgess, S., Sellitto, C., Cox, C., & Buultjens, J. (2011). Trust perceptions of online travel information by different content creators: some social and legal implications. Information Systems Frontiers, 13(2), 221–235.
Cantallops, A. S., & Salvi, F. (2014). New consumer behavior: a review of research on ewom and hotels. International Journal of Hospitality Management, 36, 41–51.
Dellarocas, C. (2006). Strategic manipulation of internet opinion forums: implications for consumers and firms. Management Science, 52(10), 1577–1593.
Dolnicar, S., & Otter, T. (2003). Which Hotel Attributes Matter? A Review of Previous and a Framework for Future Research. Proceedings of the 9th Annual Conference of the Asia Pacific Tourism Association (APTA) (pp. 176–188). University of Technology Sydney.
Farquad, M. A. H., & Bose, I. (2012). Preprocessing unbalanced data using support vector machine. Decision Support Systems, 53(1), 226–233.
Feldman, R., & Sanger, J. (2007). The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge: Cambridge University Press.
Healy, P. M., & Palepu, K. G. (2001). Information asymmetry, corporate disclosure, and the capital markets: a review of the empirical disclosure literature. Journal of Accounting and Economics, 31(1–3), 405–440.
Hirshleifer, D., & Teoh, S. H. (2003). Limited attention, information disclosure, and financial reporting. Journal of Accounting and Economics, 36(1–3), 337–386.
Hofmann, T. (2001). Unsupervised learning by probabilistic latent semantic analysis. Machine Learning, 42(1–2), 177–196.
Hu, N., Bose, I., Gao, Y., & Liu, L. (2011). Manipulation in digital word-of-mouth: a reality check for book reviews. Decision Support Systems, 50(3), 627–635.
Hu, N., Bose, I., Koh, N. S., & Liu, L. (2012). Manipulation of online reviews: an analysis of ratings, readability, and sentiments. Decision Support Systems, 52(3), 674–684.
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999). An introduction to variational methods for graphical models. Machine Learning, 37(2), 183–233.
Leung, D., Law, R., Van Hoof, H., & Buhalis, D. (2013). Social media in tourism and hospitality: a literature review. Journal of Travel & Tourism Marketing, 30(1–2), 3–22.
Litvin, S. W., Goldsmith, R. E., & Pan, B. (2008). Electronic word-of-mouth in hospitality and tourism management. Tourism Management, 29(3), 458–468.
McCarthy, L., Stock, D., & Verma, R. (2010). How travelers use online and social media channels to make hotel-choice decisions. Cornell Hospitality Report, 10(18), 4–18.
Piramuthu, S. (1999). Feature selection for financial credit-risk evaluation decisions. INFORMS Journal on Computing, 11(3), 258–266.
Piramuthu, S. (2004). Evaluating feature selection methods for learning in data mining applications. European Journal of Operational Research, 156(2), 483–494.
Piramuthu, S., Kapoor, G., Zhou, W., & Mauw, S. (2012). Input online review data and related bias in recommender systems. Decision Support Systems, 53(3), 418–424.
Seret, A., vanden Broucke, S. K., Baesens, B., & Vanthienen, J. (2014). A dynamic understanding of customer behavior processes based on clustering and sequence mining. Expert Systems with Applications, 41(10), 4648–4657.
Sparks, B. A., & Browning, V. (2011). The impact of online reviews on hotel booking intentions and perception of trust. Tourism Management, 32(6), 1310–1323.
Yan, X., Wang, J., & Chau, M. (2015). Customer revisit intention to restaurants: evidence from online reviews. Information Systems Frontiers, 17(3), 645–657.
Zeveloff, J. (2013). Why You Should Never Trust the Photos Hotels Post Online. In Business Insider.
Zhang, J. (2015). Voluntary information disclosure on social media. Decision Support Systems, 73(2015), 28–36.
Zhang, J., & Aytug, H. (2016). Comparison of imputation methods for discriminant analysis with strategically hidden data. European Journal of Operational Research, 255(2), 522–530.
Zhang, J., Aytug, H., & Koehler, G. J. (2014). Discriminant analysis with strategically manipulated data. Information Systems Research, 25(3), 654–662.
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Zhang, J., Piramuthu, S. Product recommendation with latent review topics. Inf Syst Front 20, 617–625 (2018). https://doi.org/10.1007/s10796-016-9697-z
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DOI: https://doi.org/10.1007/s10796-016-9697-z