@inproceedings{kar-etal-2018-folksonomication,
    title = "{F}olksonomication: Predicting Tags for Movies from Plot Synopses using Emotion Flow Encoded Neural Network",
    author = "Kar, Sudipta  and
      Maharjan, Suraj  and
      Solorio, Thamar",
    editor = "Bender, Emily M.  and
      Derczynski, Leon  and
      Isabelle, Pierre",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/C18-1244/",
    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 {\ensuremath{\approx}}18{\%} more tags than a traditional machine learning system."
}