Skip to main content

Research Repository

Advanced Search

Film genre prediction and analysis using multimodal embeddings from large models: The case of noir

Matthews, Paul; Glitre, Kathrina

Authors

Profile image of Paul Matthews

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



Abstract

We used embeddings (numerical representations) from open-sourced language and image models to compare and contrast films in the “noir” and “neo-noir” genres with each other and with those in other genres, using as proxy sources the available plot summaries for text, and posters as images. Noir as a genre was found to be more distinctive, and therefore predictable through machine learning, than neo-noir, which was more easily confused with other genres. The process of attempted classification itself reveals interesting similarities across genres (e.g. screwball comedy murder-mystery and classic noir) as well as the shortcomings of the sources used, which fail to capture less tangible aspects of noir. Overall, we demonstrate the value of these approaches for genre analytics and potential incorporation into discovery tools.

Journal Article Type Article
Acceptance Date Jan 16, 2025
Deposit Date Jan 17, 2025
Print ISSN 0024-2594
Electronic ISSN 1559-0682
Publisher Johns Hopkins University Press
Peer Reviewed Peer Reviewed
Keywords genre; film; movie; noir; neo-noir; embeddings; multimodal; machine learning
Public URL https://uwe-repository.worktribe.com/output/13622368