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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
    as

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    References listed on IDEAS

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