@inproceedings{shafaei-etal-2020-age,
title = "Age Suitability Rating: Predicting the {MPAA} Rating Based on Movie Dialogues",
author = "Shafaei, Mahsa and
Safi Samghabadi, Niloofar and
Kar, Sudipta and
Solorio, Thamar",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.166",
pages = "1327--1335",
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{\%}.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<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%.</abstract>
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%0 Conference Proceedings
%T Age Suitability Rating: Predicting the MPAA Rating Based on Movie Dialogues
%A Shafaei, Mahsa
%A Safi Samghabadi, Niloofar
%A Kar, Sudipta
%A Solorio, Thamar
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F shafaei-etal-2020-age
%X 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%.
%U https://aclanthology.org/2020.lrec-1.166
%P 1327-1335
Markdown (Informal)
[Age Suitability Rating: Predicting the MPAA Rating Based on Movie Dialogues](https://aclanthology.org/2020.lrec-1.166) (Shafaei et al., LREC 2020)
ACL