  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.
