Joint Estimation and Analysis of Risk Behavior Ratings in Movie Scripts. Martinez, V., Somandepalli, K., Tehranian-Uhls, Y., & Narayanan, S. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4780-4790, Online, Nov, 2020. Association for Computational Linguistics. ![Joint Estimation and Analysis of Risk Behavior Ratings in Movie Scripts [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Exposure to violent, sexual, or substance-abuse content in media increases the willingness of children and adolescents to imitate similar behaviors. Computational methods that identify portrayals of risk behaviors from audio-visual cues are limited in their applicability to films in post-production, where modifications might be prohibitively expensive. To address this limitation, we propose a model that estimates content ratings based on the language use in movie scripts, making our solution available at the earlier stages of creative production. Our model significantly improves the state-of-the-art by adapting novel techniques to learn better movie representations from the semantic and sentiment aspects of a characters language use, and by leveraging the co-occurrence of risk behaviors, following a multi-task approach. Additionally, we show how this approach can be useful to learn novel insights on the joint portrayal of these behaviors, and on the subtleties that filmmakers may otherwise not pick up on.
@inproceedings{martinez-etal-2020-joint,
title = {Joint Estimation and Analysis of Risk Behavior Ratings in Movie Scripts},
author = {Martinez, Victor and Somandepalli, Krishna and Tehranian-Uhls, Yalda and Narayanan, Shrikanth},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
month = {Nov},
year = {2020},
address = {Online},
publisher = {Association for Computational Linguistics},
url = {https://www.aclweb.org/anthology/2020.emnlp-main.387},
doi = {10.18653/v1/2020.emnlp-main.387},
pages = {4780-4790},
abstract = {Exposure to violent, sexual, or substance-abuse content in media increases the willingness of children and adolescents to imitate similar behaviors. Computational methods that identify portrayals of risk behaviors from audio-visual cues are limited in their applicability to films in post-production, where modifications might be prohibitively expensive. To address this limitation, we propose a model that estimates content ratings based on the language use in movie scripts, making our solution available at the earlier stages of creative production. Our model significantly improves the state-of-the-art by adapting novel techniques to learn better movie representations from the semantic and sentiment aspects of a character{}s language use, and by leveraging the co-occurrence of risk behaviors, following a multi-task approach. Additionally, we show how this approach can be useful to learn novel insights on the joint portrayal of these behaviors, and on the subtleties that filmmakers may otherwise not pick up on.}
}
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