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The future of film-making: Data-driven movie-making techniques
Open accessAbstract
Since the term ‘big data’ came to the scene, it has left almost no industry unaffected. Even the art world has taken advantage of the benefits of big data. One of the latest art forms, cinema, eventually started using analytics to predict their audience and their tastes through data mining. In addition to online platforms like Netflix, Amazon Prime and many more, which act on a different basis, the industry itself evolved to a new phase that uses AI in pre-production, production, post-production and distribution phases. This paper researches software, such as Cinelytic, ScriptBook and LargoAI, and their working strategies to understand the role of directors and producers in the age of the digital era in film-making. The research aims to find answers to the capabilities of data-driven movie-making techniques and, accordingly, it makes a number of predictions about the role of human beings in the production of an artwork and analyses the role of the software. The research also investigates the pros and cons of using big data in the film-making industry. Keywords: Artificial intelligence, cinema, data mining, film-making.
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Existing tools leverage big data for screenwriting and feedback analysis.
“Additionally, while existing tools leverage big data for screenwriting and feedback analysis [72, 77, 84]”
From Understanding Screenwriters' Practices, Attitudes, and Future Expectations in Human-AI Co-Creation (2025) · cites this paper as [72]
Section 4.2, “ScriptBook” (page unavailable), describes a tool that analyzes thousands of scripts and gives filmmakers reports on their scripts, supporting the big-data and screenwriting-related part of the claim. Section 5, “Discussion and conclusion” (page unavailable), also describes suggestions for scene modifications. Neither passage establishes that the tools analyze audience feedback; the reported audience analysis concerns predicted demographics and demands. (LLM, conf=0.82)
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Research works printed but not linked: 3
- Bordwell, D., Thompson, K. & Smith, J. (2016). Film art an introduction (11th ed.). New York: McGraw-Hill Education.
- Marr, B. (2016). Big Data in practice how 45 successful companies used big data analytics to deliver extraordinary results. Sussex, UK: Wiley.
- Parkinson, D. (2002). History of film (2nd ed.). London, UK: Thames & Hudson Ltd.
Cited by
Sorted by citations
Understanding Screenwriters' Practices, Attitudes, and Future Expectations in Human-AI Co-Creation
Tang, Y., Li, H., Lan, M., Ma, X., & Qu, H. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714120 · Cited by 18
“Social computing methods analyze audience feedback derived from social media activity, fan discussions, and sentiment trends.”
Metadata
- Authors
- Akgülgil Mutlu, N. G.
- Venue
- Global Journal of Arts Education
- Published
- 2020-08-31
- Volume
- 10
- Issue
- 2
- Pages
- 167-174
- ISSN
- 2301-2560 (ISSN-L)
- OpenAlex
- W3122518528
- Cited by
- 9
- References
- 0
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 1 · 2023: 1 · 2022: 1 · 2021: 2
- FWCI
- 0.85
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Global Journal of Arts Education (publishedVersion)
- Topics
- Social Sciences › Arts and Humanities › Visual Arts and Performing Arts › Cinema and Media Studies
- Social Sciences › Arts and Humanities › Visual Arts and Performing Arts › Art, Technology, and Culture
- Social Sciences › Arts and Humanities › Visual Arts and Performing Arts › Cinema History and Criticism
- Keywords
- artificial intelligence in film production, data mining, film industry, film distribution
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