@proceedings{Shafaei2020,
title = {Age Suitability Rating: Predicting the MPAA Rating Based on Movie Dialogues},
author = {Mahsa Shafaei and Niloofar Safi Samghabadi and Sudipta Kar and Thamar Solorio },
url = {http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.166.pdf},
year = {2020},
date = {2020-05-01},
publisher = {LREC},
abstract = {Movies help us learn and inspire societal change. But they can also contain objectionable content that negatively affects viewers' behavior, 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%.},
keywords = {MPAA Rating, Story Analysis, Text Classification},
pubstate = {published},
tppubtype = {proceedings}
}
Movies help us learn and inspire societal change. But they can also contain objectionable content that negatively affects viewers’ behavior, 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%.
