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What Is Important When We Evaluate Movies? Insights from Computational Analysis of Online Reviews

Author

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  • Frank M. Schneider

    (Institute for Media and Communication Studies, University of Mannheim, Germany)

  • Emese Domahidi

    (Institute for Media and Communication Science, TU Ilmenau, Germany)

  • Felix Dietrich

    (Institute for Media and Communication Studies, University of Mannheim, Germany)

Abstract

The question of what is important when we evaluate movies is crucial for understanding how lay audiences experience and evaluate entertainment products such as films. In line with this, subjective movie evaluation criteria (SMEC) have been conceptualized as mental representations of important attitudes toward specific film features. Based on exploratory and confirmatory factor analyses of self-report data from online surveys, previous research has found and validated eight dimensions. Given the large-scale evaluative information that is available in online users’ comments in movie databases, it seems likely that what online users write about movies may enrich our knowledge about SMEC. As a first fully exploratory attempt, drawing on an open-source dataset including movie reviews from IMDb, we estimated a correlated topic model to explore the underlying topics of those reviews. In 35,136 online movie reviews, the most prevalent topics tapped into three major categories—Hedonism, Actors’ Performance, and Narrative—and indicated what reviewers mostly wrote about. Although a qualitative analysis of the reviews revealed that users mention certain SMEC, results of the topic model covered only two SMEC: Story Innovation and Light-heartedness. Implications for SMEC and entertainment research are discussed.

Suggested Citation

  • Frank M. Schneider & Emese Domahidi & Felix Dietrich, 2020. "What Is Important When We Evaluate Movies? Insights from Computational Analysis of Online Reviews," Media and Communication, Cogitatio Press, vol. 8(3), pages 153-163.
    • Handle: RePEc:cog:meanco:v8:y:2020:i:3:p:153-163
    DOI: 10.17645/mac.v8i3.3134
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    References listed on IDEAS

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    1. Grün, Bettina & Hornik, Kurt, 2011. "topicmodels: An R Package for Fitting Topic Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i13).
    2. Jehoshua Eliashberg & Jedid-Jah Jonker & Mohanbir S. Sawhney & Berend Wierenga, 2000. "MOVIEMOD: An Implementable Decision-Support System for Prerelease Market Evaluation of Motion Pictures," Marketing Science, INFORMS, vol. 19(3), pages 226-243, January.
    3. Pradeep K. Chintagunta & Shyam Gopinath & Sriram Venkataraman, 2010. "The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets," Marketing Science, INFORMS, vol. 29(5), pages 944-957, 09-10.
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