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Jafarbeglou, Zohreh, AfsharKazemi, MohammadAli, jokar, soheila. (1405). A Multilingual BERT Framework for Intelligent Screenplay Analysis: Emotion Recognition through Character Behavioral Patterns. سامانه مدیریت نشریات علمی, 15(56), 55-84. doi: 10.22054/ims.2026.87943.2666
Zohreh Jafarbeglou; MohammadAli AfsharKazemi; soheila jokar. "A Multilingual BERT Framework for Intelligent Screenplay Analysis: Emotion Recognition through Character Behavioral Patterns". سامانه مدیریت نشریات علمی, 15, 56, 1405, 55-84. doi: 10.22054/ims.2026.87943.2666
Jafarbeglou, Zohreh, AfsharKazemi, MohammadAli, jokar, soheila. (1405). 'A Multilingual BERT Framework for Intelligent Screenplay Analysis: Emotion Recognition through Character Behavioral Patterns', سامانه مدیریت نشریات علمی, 15(56), pp. 55-84. doi: 10.22054/ims.2026.87943.2666
Jafarbeglou, Zohreh, AfsharKazemi, MohammadAli, jokar, soheila. A Multilingual BERT Framework for Intelligent Screenplay Analysis: Emotion Recognition through Character Behavioral Patterns. سامانه مدیریت نشریات علمی, 1405; 15(56): 55-84. doi: 10.22054/ims.2026.87943.2666

A Multilingual BERT Framework for Intelligent Screenplay Analysis: Emotion Recognition through Character Behavioral Patterns

مطالعات مدیریت کسب و کار هوشمند
مقاله 3، دوره 15، شماره 56، تیر 1405، صفحه 55-84    اصل مقاله (1.64 M)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.22054/ims.2026.87943.2666
نویسندگان
Zohreh Jafarbeglou1؛ MohammadAli AfsharKazemi* 2؛ soheila jokar3
1PhD Candidate, Department of Information Technology Management, Qeshm Branch, Islamic Azad University, Qeshm, Iran
2Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
3Department of Mathematics, Islamic Azad University, Qeshm Branch, Qeshm, Iran
چکیده
This study introduces EmoBERTScr, an intelligent Multilingual BERT-based model for detecting and analyzing emotional and behavioral patterns in Persian and multilingual screenplays. A dataset of 35,800 dialogue samples from 1,700 Persian and English screenplays was manually collected and annotated by linguistics and psychology experts into eight emotional categories according to Plutchik’s (1980) wheel of emotions. The methodology involved data acquisition from cinematic archives, preprocessing (noise removal, normalization, and tokenization), and supervised learning with a fine-tuned Multilingual BERT architecture. The model achieved an overall accuracy of 98.22%, with F1-scores ranging from 95.53% (surprise) to 100% (joy, fear, sadness, trust) across all emotional categories. The primary contribution of this research lies in developing a practical AI assistant for screenwriters, capable of providing real-time feedback to enhance narrative coherence and maintain emotional consistency, which can reduce rewriting and production costs in the film industry. While certain overlapping emotional states, such as anticipation and surprise, remain challenging to distinguish, the proposed approach demonstrates robust performance in both dominant and subtle emotional expressions. This work establishes a foundation for future advancements in intelligent narrative analysis, particularly for low-resource languages like Persian, and highlights the potential for integrating AI-driven emotion recognition into professional screenplay development workflows.
کلیدواژه‌ها
Multilingual BERT؛ EmoBERTScr؛ Emotion Recognition؛ Intelligent Assistant؛ Character Behavior؛ Persian Screenplays؛ Plutchik’s Emotion Wheel؛ Narrative Coherence
مراجع
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