MBTI Personality Prediction for Fictional Characters Using Movie Scripts
Yisi Sang, Xiangyang Mou, Mo Yu, Dakuo Wang, Jing Li, Jeffrey Stanton
Abstract
An NLP model that understands stories should be able to understand the characters in them. To support the development of neural models for this purpose, we construct a benchmark, Story2Personality. The task is to predict a movie character’s MBTI or Big 5 personality types based on the narratives of the character. Experiments show that our task is challenging for the existing text classification models, as none is able to largely outperform random guesses. We further proposed a multi-view model for personality prediction using both verbal and non-verbal descriptions, which gives improvement compared to using only verbal descriptions. The uniqueness and challenges in our dataset call for the development of narrative comprehension techniques from the perspective of understanding characters.- Anthology ID:
- 2022.findings-emnlp.500
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6715–6724
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.500
- DOI:
- 10.18653/v1/2022.findings-emnlp.500
- Cite (ACL):
- Yisi Sang, Xiangyang Mou, Mo Yu, Dakuo Wang, Jing Li, and Jeffrey Stanton. 2022. MBTI Personality Prediction for Fictional Characters Using Movie Scripts. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6715–6724, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- MBTI Personality Prediction for Fictional Characters Using Movie Scripts (Sang et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.findings-emnlp.500.pdf