Longitudinal abuse and sentiment analysis of Hollywood movie dialogues using language models

Over the past decades, there has been an increase in the prevalence of abusive and violent content in Hollywood movies. In this study, we use language models to explore the longitudinal abuse and sentiment analysis of Hollywood Oscar and blockbuster movie dialogues from 1950 to 2024. We provide an analysis of subtitles for over a thousand movies, which are categorised into four genres. We employ fine-tuned language models to examine the trends and shifts in emotional and abusive content over the past seven decades. Findings reveal significant temporal changes in movie dialogues, which reflect broader social and cultural influences. Overall, the emotional tendencies in the films are diverse, and the detection of abusive content also exhibits significant fluctuations. The results show a gradual rise in abusive content in recent decades, reflecting social norms and regulatory policy changes. Genres such as thrillers still present a higher frequency of abusive content that emphasises the ongoing narrative role of violence and conflict. At the same time, underlying positive emotions such as humour and optimism remain prevalent in most of the movies. Furthermore, the gradual increase of abusive content in movie dialogues has been significant over the last two decades, where Oscar-nominated movies overtook the top ten blockbusters. • We use Large Language Models (LLMs) for abuse and sentiment analysis of Hollywood movie dialogues. • We analyze subtitles for over a thousand movies to examine the trends and shifts in emotional and abusive content. • Our findings reveal significant temporal changes in movie dialogues, which reflect broader social and cultural influences. • We found a gradual rise in abusive content in recent decades, reflecting social norms and regulatory policy changes. • Abuse in Oscar-nominated movies overtook the top ten blockbusters in last two decades.

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Publication Details

Journal
Machine Learning with Applications
Published
2025-10-15
DOI
https://doi.org/10.1016/j.mlwa.2025.100749
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
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article

Longitudinal abuse and sentiment analysis of Hollywood movie dialogues using language models

Rohitash Chandra, Guoxiang Ren
Machine Learning with Applications
Hate Speech and Cyberbullying Detection
article

Longitudinal abuse and sentiment analysis of Hollywood movie dialogues using language models

Rohitash Chandra, Guoxiang Ren
article en

Abstract

Over the past decades, there has been an increase in the prevalence of abusive and violent content in Hollywood movies. In this study, we use language models to explore the longitudinal abuse and sentiment analysis of Hollywood Oscar and blockbuster movie dialogues from 1950 to 2024. We provide an analysis of subtitles for over a thousand movies, which are categorised into four genres. We employ fine-tuned language models to examine the trends and shifts in emotional and abusive content over the past seven decades. Findings reveal significant temporal changes in movie dialogues, which reflect broader social and cultural influences. Overall, the emotional tendencies in the films are diverse, and the detection of abusive content also exhibits significant fluctuations. The results show a gradual rise in abusive content in recent decades, reflecting social norms and regulatory policy changes. Genres such as thrillers still present a higher frequency of abusive content that emphasises the ongoing narrative role of violence and conflict. At the same time, underlying positive emotions such as humour and optimism remain prevalent in most of the movies. Furthermore, the gradual increase of abusive content in movie dialogues has been significant over the last two decades, where Oscar-nominated movies overtook the top ten blockbusters. • We use Large Language Models (LLMs) for abuse and sentiment analysis of Hollywood movie dialogues. • We analyze subtitles for over a thousand movies to examine the trends and shifts in emotional and abusive content. • Our findings reveal significant temporal changes in movie dialogues, which reflect broader social and cultural influences. • We found a gradual rise in abusive content in recent decades, reflecting social norms and regulatory policy changes. • Abuse in Oscar-nominated movies overtook the top ten blockbusters in last two decades.

Machine Learning with ApplicationsVol. 22
UNSW Sydney (AU), Artificial Intelligence in Medicine (Canada) (CA)
Openalex Percentile: Top 85%
Hate Speech and Cyberbullying Detection
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