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A Distant Reading-Based Framework for the Evaluation of Screenplays

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Zhong Ooi
Markus Eger
Published in
Interactive Storytelling
Publisher
Springer Nature Switzerland

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Abstract

This chapter introduces a framework for evaluating AI-generated screenplays using distant reading techniques. The framework analyzes linguistic features, syntactic structures, screenplay composition, and character dynamics to compare AI-generated screenplays with human-authored ones. It employs both qualitative and quantitative methods, including Yngve and Frazier mean complexity scores, sentiment analysis, part-of-speech distribution, and Heap’s Law. The evaluation also covers scene length metrics, gender distribution, and visual character graphs. The results highlight differences between AI models like GPT-3.5 and GPT-4, showing that GPT-4 produces screenplays more aligned with human-authored works. The framework aims to provide valuable insights into the narrative and structural composition of screenplays, paving the way for future research in generative AI and creative writing.
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previous chapter Interactive Digital Narratives to Represent Complexity: A Review
next chapter Combining Experiential and Spatial Data for Immersive AR Narrative Creation: A Phased Methodology
Title
A Distant Reading-Based Framework for the Evaluation of Screenplays
Authors
Zhong Ooi
Markus Eger
Copyright Year
2026
Book
Interactive Storytelling

Print ISBN: 978-3-032-12407-4

Electronic ISBN: 978-3-032-12408-1

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-12408-1_7
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