2019 |
Shafaei, Mahsa; Samghabadi, Niloofar Safi; Kar, Sudipta; Solorio, Thamar arXiv, (Ed.): 2019, visited: 21.08.2019. Abstract | Links | BibTeX | Tags: Abusive Language detection, Sentiment analysis, Text Classification @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} } 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%. |
2018 |
Niloofar S. Samghabadi Deepthi Mave, Sudipta Kar Thamar Solorio RiTUAL-UH at TRAC 2018 Shared Task: Aggression Identification Inproceedings 2018. Abstract | Links | BibTeX | Tags: Abusive Language detection, Aggression Identification @inproceedings{safisamghabadi-EtAl:2018:TRAC1, title = {RiTUAL-UH at TRAC 2018 Shared Task: Aggression Identification}, author = {Niloofar S. Samghabadi, Deepthi Mave, Sudipta Kar, Thamar Solorio}, url = {http://www.aclweb.org/anthology/W18-4402}, year = {2018}, date = {2018-08-25}, journal = {TRAC1 @ COLING2018}, abstract = {This paper presents our system for “TRAC 2018 Shared Task on Aggression Identification”. Our best systems for the English dataset use a combination of lexical and semantic features. However, for Hindi data using only lexical features gave us the best results. We obtained weighted F1- measures of 0.5921 for the English Facebook task (ranked 12th), 0.5663 for the English Social Media task (ranked 6th), 0.6451 for the Hindi Facebook task (ranked 1st), and 0.4853 for the Hindi Social Media task (ranked 2nd).}, keywords = {Abusive Language detection, Aggression Identification}, pubstate = {published}, tppubtype = {inproceedings} } This paper presents our system for “TRAC 2018 Shared Task on Aggression Identification”. Our best systems for the English dataset use a combination of lexical and semantic features. However, for Hindi data using only lexical features gave us the best results. We obtained weighted F1- measures of 0.5921 for the English Facebook task (ranked 12th), 0.5663 for the English Social Media task (ranked 6th), 0.6451 for the Hindi Facebook task (ranked 1st), and 0.4853 for the Hindi Social Media task (ranked 2nd). |
2017 |
Niloofar S. Samghabadi Suraj Maharjan, Alan Sprague Raquel Sprague Thamar Solorio D Detecting Nastiness in Social Media Inproceedings ALW1@ACL2017, 2017. Links | BibTeX | Tags: Abusive Language detection @inproceedings{safisamghabadi-EtAl:2017:ALW1, title = {Detecting Nastiness in Social Media}, author = {Niloofar S. Samghabadi, Suraj Maharjan, Alan Sprague, Raquel D. Sprague, Thamar Solorio}, url = {http://aclweb.org/anthology/W17-3010}, year = {2017}, date = {2017-08-04}, booktitle = {ALW1@ACL2017}, keywords = {Abusive Language detection}, pubstate = {published}, tppubtype = {inproceedings} } |
0000 |
Niloofar S. Samghabadi Suraj Maharjan, Alan Sprague Raquel Sprague Thamar Solorio D Detecting Nastiness in Social Media Inproceedings 0000. BibTeX | Tags: Abusive Language detection, Text Classification @inproceedings{Safi2017, title = {Detecting Nastiness in Social Media}, author = {Niloofar S. Samghabadi, Suraj Maharjan, Alan Sprague, Raquel D. Sprague, Thamar Solorio}, keywords = {Abusive Language detection, Text Classification}, pubstate = {published}, tppubtype = {inproceedings} } |
Mahsa Shafaei Niloofar Safi Samghabadi, Sudipta Kar Thamar Solorio arXiv, (Ed.): 0000. Abstract | Links | BibTeX | Tags: Abusive Language detection, Text Classification @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} } 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%. |
Shafaei, Mahsa; Samghabadi, Niloofar Safi; Kar, Sudipta; Solorio, Thamar arXiv, (Ed.): 0000. Abstract | Links | BibTeX | Tags: Abusive Language detection, Sentiment analysis, Text Classification @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} } 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%. |