Age Suitability Rating: Predicting the MPAA Rating Based on Movie Dialogues

Mahsa Shafaei, Niloofar Safi Samghabadi, Sudipta Kar, Thamar Solorio


Abstract
Movies help us learn and inspire societal change. But they can also contain objectionable content that negatively affects viewers’ behaviour, especially children. In this paper, our goal is to predict the suitability of movie content for children and young adults based on scripts. The criterion that we use to measure suitability is the MPAA rating that is specifically designed for this purpose. We create a corpus for movie MPAA ratings and propose an RNN based architecture with attention that jointly models the genre and the emotions in the script to predict the MPAA rating. We achieve 81% weighted F1-score for the classification model that outperforms the traditional machine learning method by 7%.
Anthology ID:
2020.lrec-1.166
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
1327–1335
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.166
DOI:
Bibkey:
Cite (ACL):
Mahsa Shafaei, Niloofar Safi Samghabadi, Sudipta Kar, and Thamar Solorio. 2020. Age Suitability Rating: Predicting the MPAA Rating Based on Movie Dialogues. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 1327–1335, Marseille, France. European Language Resources Association.
Cite (Informal):
Age Suitability Rating: Predicting the MPAA Rating Based on Movie Dialogues (Shafaei et al., LREC 2020)
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PDF:
https://aclanthology.org/2020.lrec-1.166.pdf