Publications

@online{Shafaei2019cb,
title = {Rating for Parents: Predicting Children Suitability Rating for Movies Based on Language of the Movies},
author = {Mahsa Shafaei and Niloofar Safi Samghabadi and Sudipta Kar and Thamar Solorio},
editor = {arXiv},
url = {https://arxiv.org/abs/1908.07819},
year = {2019},
date = {2019-08-21},
urldate = {2019-08-21},
abstract = {The film culture has grown tremendously in recent years. The large number of streaming services put films as one of the most convenient forms of entertainment in today's world. Films can help us learn and inspire societal change. But they can also negatively affect viewers. In this paper, our goal is to predict the suitability of the 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 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 78% weighted F1-score for the classification model that outperforms the traditional machine learning method by 6%.},
keywords = {Abusive Language detection, Sentiment analysis, Text Classification},
pubstate = {published},
tppubtype = {online}
}

@online{Shafaei2019b,
title = {Rating for Parents: Predicting Children Suitability Rating for Movies Based on Language of the Movies},
author = {Mahsa Shafaei, Niloofar Safi Samghabadi, Sudipta Kar, Thamar Solorio},
editor = { arXiv},
url = {https://arxiv.org/abs/1908.07819},
abstract = {The film culture has grown tremendously in recent years. The large number of streaming services put films as one of the most convenient forms of entertainment in today's world. Films can help us learn and inspire societal change. But they can also negatively affect viewers. In this paper, our goal is to predict the suitability of the 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 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 78% weighted F1-score for the classification model that outperforms the traditional machine learning method by 6%.},
keywords = {Abusive Language detection, Text Classification},
pubstate = {published},
tppubtype = {online}
}

@online{Shafaei2019c,
title = {Rating for Parents: Predicting Children Suitability Rating for Movies Based on Language of the Movies},
author = {Mahsa Shafaei and Niloofar Safi Samghabadi and Sudipta Kar and Thamar Solorio},
editor = {arXiv},
url = {https://arxiv.org/abs/1908.07819},
abstract = {The film culture has grown tremendously in recent years. The large number of streaming services put films as one of the most convenient forms of entertainment in today's world. Films can help us learn and inspire societal change. But they can also negatively affect viewers. In this paper, our goal is to predict the suitability of the 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 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 78% weighted F1-score for the classification model that outperforms the traditional machine learning method by 6%.},
keywords = {Abusive Language detection, Sentiment analysis, Text Classification},
pubstate = {published},
tppubtype = {online}
}