mlsanityData to 7 Oct 2026

Representation of professions in entertainment media: Insights into frequency and sentiment trends through computational text analysis

arXiv 2110.03873 ↗First posted 8 Oct 2021cs.CL cs.CY cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Societal ideas and trends dictate media narratives and cinematic depictions which in turn influences people's beliefs and perceptions of the real world. Media portrayal of culture, education, government, religion, and family affect their function and evolution over time as people interpret and perceive these representations and incorporate them into their beliefs and actions. It is important to study media depictions of these social structures so that they do not propagate or reinforce negative stereotypes, or discriminate against any demographic section. In this work, we examine media representation of professions and provide computational insights into their incidence, and sentiment expressed, in entertainment media content. We create a searchable taxonomy of professional groups and titles to facilitate their retrieval from speaker-agnostic text passages like movie and television (TV) show subtitles. We leverage this taxonomy and relevant natural language processing (NLP) models to create a corpus of professional mentions in media content, spanning more than 136,000 IMDb titles over seven decades (1950-2017). We analyze the frequency and sentiment trends of different occupations, study the effect of media attributes like genre, country of production, and title type on these trends, and investigate if the incidence of professions in media subtitles correlate with their real-world employment statistics. We observe increased media mentions of STEM, arts, sports, and entertainment occupations in the analyzed subtitles, and a decreased frequency of manual labor jobs and military occupations. The sentiment expressed toward lawyers, police, and doctors is becoming negative over time, whereas astronauts, musicians, singers, and engineers are mentioned favorably. Professions that employ more people have increased media frequency, supporting our hypothesis that media acts as a mirror to society.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 98 references that are in the map. First posted on arXiv 8 Oct 2021.

Builds on

9 of 98 references.

References · 9

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2019BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysiscited by 176
  4. 2019Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentencecited by 121
  5. 2015Effective LSTMs for Target-Dependent Sentiment Classificationcited by 82
  6. 2019GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledgecited by 78
  7. 2019Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networkscited by 62
  8. 2016SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoodscited by 32
  9. 2018Integrating Weakly Supervised Word Sense Disambiguation into Neural Machine Translationcited by 7

Built on by

Not cited yet.