Chemistry and Physics Faculty Articles

Title

How Words Matter: Machine Learning & Movie Success

Document Type

Article

Publication Date

10-11-2019

Publication Title

Applied Economics Letters

Keywords

Machining learning, Neural network, Movies, Box office sales, Critic ratings

ISSN

1350-4851

First Page

1

Last Page

5

Abstract

We employed a machine learning structure to examine the relationships between word choice in Internet Movie Database (IMDB) comedy movie descriptions and overall performance. Our measures of success were ticket sales, user ratings, and Metacritic scores. We used linear regressions, along with recurrent neural networks implementing a Long Short-Term Memory framework, for textual sentiment analysis. Employing conservative p-values, our results revealed the possible influence of gender bias in movies that favoured male-centric themes, as well as negative effects for holiday comedies, paranormal movies, and crime films.

Comments

©2019 Informa UK Limited, trading as Taylor & Francis Group

ORCID ID

0000-0003-4692-9539

DOI

10.1080/13504851.2019.1676868

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Peer Reviewed

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