@InProceedings{kar-maharjan-solorio:2018:C18-1,
  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},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {2879--2891},
  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 our system with multiple baselines and found that the addition of emotion flows boosts the performance of the network by learning ≈18% more tags than a traditional machine learning system.},
  url       = {http://www.aclweb.org/anthology/C18-1244}
}