  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.
