Using Film-Trope Connections for Clustering Similar Movies

Tropes are storytelling devices or conventions that can be found in storytelling media, for example in movies. DBTropes is an RDF-dataset with media-trope connections extracted from Tv Tropes, which is a community-edited wiki listing tropes for various creative works. This study investigates whether the tropes of films can be used to cluster similar movies. First, we extracted film-trope connections from the DBTropes dataset. We then took four samples from the dataset, three for clustering films, and one for clustering tropes. We used the film–trope connections to calculate euclidean and cosine distances between movies and for the last sample between tropes. Then we clustered the samples with hierarchical clustering using complete and ward linkage. Our results show that hierarchical clustering can group similar films together using this dataset. For calculating distances the cosine distance method works significantly better than euclidean distance. Both hierarchical clustering methods complete and ward work well. It depends on the chosen sample, which of them results in a clearer and more interpretable output. We conclude that the data works well for clustering similar films or similar tropes

Structures in Tropes Networks: Toward a Formal Story Grammar

International audienceTropes are cultural narrative conventions that shape our expectations of stories. This paper proposes a new approach to examine movie content relying on tropes. It first presents the architecture of tropes ontology extracted from tvtropes.org, then studies the link prediction problem in trope bipartite networks and discusses the next challenges and numerous applications that ensue from this approach. In addition, we propose to assess the potential of tropes to be the lexicon units of a formal story grammar