MPST: A Corpus of Movie Plot Synopses with Tags
This work addresses the need for improved movie recommendation systems and viewer insights by providing a dataset for narrative analysis, but it is incremental as it focuses on data collection rather than novel methods.
The authors tackled the problem of automatic movie tagging by collecting a corpus of 14K movie plot synopses with around 70 fine-grained tags, and they explored the feasibility of inferring tags from these synopses.
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.