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The Text-Score Allocation Model: Finding Latent Topics of Online Review Documents and Multi-Item Ratings

Author

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  • Sotaro Katsumata

    (Graduate School of Economics, Osaka University)

  • Seungjin Kim

    (Graduate School of Economics, Osaka University)

Abstract

This studyfocusesononlinereviewdatainwhichcommentsarewritteninnaturallanguages and evaluationsareattachedasintegers.Thisstudydevelopsatopicmodelincorporatingboth natural languagesandevaluationscores,expandinglatentDirichletallocation(LDA).Themodel consists oftwocomponents:LDAandaDirichlet-binomialclusteringmodel.Thelatterassumes binomial distributionsforthereviewscores.Sincethemodelassumesconjugatedistributions,we can applyafastandstableestimatorbasedoncollapsedGibbssamplingtoestimatetheparameters. Further,themodelenablesustoexaminetherelationshipbetweenvocabularywordsandreview scores basedonthetopicallocationresults.

Suggested Citation

  • Sotaro Katsumata & Seungjin Kim, 2020. "The Text-Score Allocation Model: Finding Latent Topics of Online Review Documents and Multi-Item Ratings," Discussion Papers in Economics and Business 20-01, Osaka University, Graduate School of Economics.
    • Handle: RePEc:osk:wpaper:2001
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    References listed on IDEAS

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    1. Kannan, P.K. & Li, Hongshuang “Alice”, 2017. "Digital marketing: A framework, review and research agenda," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 22-45.
    2. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
    3. Floyd, Kristopher & Freling, Ryan & Alhoqail, Saad & Cho, Hyun Young & Freling, Traci, 2014. "How Online Product Reviews Affect Retail Sales: A Meta-analysis," Journal of Retailing, Elsevier, vol. 90(2), pages 217-232.
    4. Steven T. Berry, 1994. "Estimating Discrete-Choice Models of Product Differentiation," RAND Journal of Economics, The RAND Corporation, vol. 25(2), pages 242-262, Summer.
    5. Seshadri Tirunillai & Gerard J. Tellis, 2012. "Does Chatter Really Matter? Dynamics of User-Generated Content and Stock Performance," Marketing Science, INFORMS, vol. 31(2), pages 198-215, March.
    6. Michael Trusov & Liye Ma & Zainab Jamal, 2016. "Crumbs of the Cookie: User Profiling in Customer-Base Analysis and Behavioral Targeting," Marketing Science, INFORMS, vol. 35(3), pages 405-426, May.
    7. Joachim Büschken & Greg M. Allenby, 2016. "Sentence-Based Text Analysis for Customer Reviews," Marketing Science, INFORMS, vol. 35(6), pages 953-975, November.
    8. Onishi, Hiroshi & Manchanda, Puneet, 2012. "Marketing activity, blogging and sales," International Journal of Research in Marketing, Elsevier, vol. 29(3), pages 221-234.
    9. Xueming Luo, 2009. "Quantifying the Long-Term Impact of Negative Word of Mouth on Cash Flows and Stock Prices," Marketing Science, INFORMS, vol. 28(1), pages 148-165, 01-02.
    10. Dinesh Puranam & Vishal Narayan & Vrinda Kadiyali, 2017. "The Effect of Calorie Posting Regulation on Consumer Opinion: A Flexible Latent Dirichlet Allocation Model with Informative Priors," Marketing Science, INFORMS, vol. 36(5), pages 726-746, September.
    Full references (including those not matched with items on IDEAS)

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