Publications

@conference{Shafaei2019,
title = {Exploiting Textual, Visual and Product Features for Predicting the Likeability of Movies},
author = {Mahsa Shafaei and Adrian Pastor Lopez-Monroy and Thamar Solorio},
url = {https://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS19/paper/view/18305},
year = {2019},
date = {2019-05-01},
publisher = {The 32nd International FLAIRS Conference},
abstract = {Watching movies is one of the most popular entertainments among people. Every year, a huge amount of money goes to the movie industry to release movies to the market. In this paper, we propose a multimodal model to predict the likability of movies using textual, visual and product features. With the help of these features, we capture different aspects of movies and feed them as inputs to binary and multi-class classification and regression models to predict IMDB rating of movies at early steps of production. We also propose our own dataset consisting of about 15000 movie subtitles along with their metadata and poster images. We achieve 76% and 63% weighted F1-score for binary and multiclass classification respectively, and 0.7 mean square error for the regression model. Using prediction methods and data analysis, this research helps the movie business to be more productive. },
keywords = {Sentiment analysis, Text Classification},
pubstate = {published},
tppubtype = {conference}
}

@conference{Kar2018b,
title = {Folksonomication: Predicting Tags for Movies from Plot Synopses using Emotion Flow encoded Neural Network},
author = {Sudipta Kar and Suraj Maharjan and Thamar Solorio},
url = {http://ritual.uh.edu/folksonomication-2018},
year = {2018},
date = {2018-08-23},
booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
keywords = {CNN, Narrative Analysis, Sentiment analysis},
pubstate = {published},
tppubtype = {conference}
}

@conference{Kar2018,
title = {MPST: A Corpus of Movie Plot Synopses with Tags},
author = {Sudipta Kar and Suraj Maharjan and A. Pastor López-Monroy and Thamar Solorio},
url = {http://sudiptakar.info/wp-content/uploads/2018/05/322_LREC_2018.pdf, Slide
http://sudiptakar.info/wp-content/uploads/2018/02/mpst-corpus-movie-2.pdf, Paper},
year = {2018},
date = {2018-05-10},
booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
publisher = {European Language Resources Association (ELRA)},
abstract = {Social tagging of movies reveals a wide range of heterogeneous information about movies, like the genre, plot structure, soundtracks, metadata, visual and emotional experiences. Such information can be valuable in building automatic systems to create tags for movies. Automatic tagging systems can help recommendation engines to improve the retrieval of similar movies as well as help viewers to know what to expect from a movie in advance. In this paper, we set out to the task of collecting a corpus of movie plot synopses and tags. We describe a methodology that enabled us to build a fine-grained set of around 70 tags exposing heterogeneous characteristics of movie plots and the multi-label associations of these tags with some 14K movie plot synopses. We investigate how these tags correlate with movies and the flow of emotions throughout different types of movies. Finally, we use this corpus to explore the feasibility of inferring tags from plot synopses. We expect the corpus will be useful in other tasks where analysis of narratives is relevant.},
keywords = {Information Extraction, Narrative Analysis, Sentiment analysis, Text Classification},
pubstate = {published},
tppubtype = {conference}
}