Contributors

Abstract

Folksonomy of movies covers a wide range of heterogeneous information about movies, like the genre, plot structure, visual experiences, soundtracks, metadata, and emotional experiences from watching a movie. Being able to automatically generate or predict tags for movies can help recommendation engines improve retrieval of similar movies, and help viewers know what to expect from a movie in advance. In this work, we explore the problem of creating tags for movies from plot synopses. We propose a novel neural network model that merges information from synopses and emotion flows throughout the plots to predict a set of tags for movies. We compare the system with multiple baselines and found that the addition of emotion flows boosts the performance of the network by learning ≈17% more tags than a traditional machine learning system.

Funded By :

National Science Foundation under grant number 1462141 and U.S. Department of Defense under grant W911NF-16-1-0422

Live Demo :

** First few tags are the most relevant ones according to the model.

Links: Paper | Slide | Source Code | Dataset

Cite the paper using

@InProceedings{C18-1244,
  author = 	"Kar, Sudipta
		and Maharjan, Suraj
		and Solorio, Thamar",
  title = 	"Folksonomication: Predicting Tags for Movies from Plot Synopses using Emotion Flow Encoded Neural Network",
  booktitle = 	"Proceedings of the 27th International Conference on Computational Linguistics",
  year = 	"2018",
  publisher = 	"Association for Computational Linguistics",
  pages = 	"2879--2891",
  location = 	"Santa Fe, New Mexico, USA",
  url = 	"http://aclweb.org/anthology/C18-1244"
}

For any query, please contact the first author skar3 AT uh DOT edu.