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Genre analysis of movies using a topic model of plot summaries

Matthews, Paul; Glitre, Kathrina

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Authors

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Dr Paul Matthews Paul2.Matthews@uwe.ac.uk
Senior Lecturer in Information and Data Science



Abstract

Genre plays an important role in the description, navigation, and discovery of movies, but it is rarely studied at large scale using quantitative methods. This allows an analysis of how genre labels are applied, how genres are composed and how these ingredients change, and how genres compare. We apply unsupervised topic modeling to a large collection of textual movie summaries and then use the model's topic proportions to investigate key questions in genre, including recognizability, mapping, canonicity, and change over time. We find that many genres can be quite easily predicted by their lexical signatures and this defines their position on the genre landscape. We find significant genre composition changes between periods for westerns, science fiction and road movies, reflecting changes in production and consumption values. We show that in terms of canonicity, canonical examples are often at the high end of the topic distribution profile for the genre rather than central as might be predicted by categorization theory.

Citation

Matthews, P., & Glitre, K. (2021). Genre analysis of movies using a topic model of plot summaries. Journal of the Association for Information Science and Technology, 72(12), 1511-1527. https://doi.org/10.1002/asi.24525

Journal Article Type Article
Acceptance Date May 11, 2021
Online Publication Date May 27, 2021
Publication Date 2021-12
Deposit Date May 28, 2021
Publicly Available Date Mar 28, 2024
Journal Journal of the Association for Information Science and Technology
Electronic ISSN 2330-1643
Publisher Association for Information Science and Technology (ASIS&T)
Peer Reviewed Peer Reviewed
Volume 72
Issue 12
Pages 1511-1527
DOI https://doi.org/10.1002/asi.24525
Keywords genre, film, topic modelling, text analytics, categories, knowledge organisation
Public URL https://uwe-repository.worktribe.com/output/7428938
Publisher URL https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24525
Additional Information Received: 2020-07-24; Accepted: 2021-05-11; Published: 2021-05-27

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