Movie Genre Detection Using Topological Data Analysis

We show that by applying discourse features derived through topological data analysis (TDA), namely homological persistence, we can improve classification results on the task of movie genre detection, including identification of overlapping movie genres. On the IMDB dataset we improve prior art results, namely we increase the Jaccard score by 4.7% over a recent results by Hoang. We also significantly improve the F-score (by over 15%) and slightly improve the hit rate (by 0.5%, ibid.). We see our contribution as threefold: (a) for general audience of computational linguists, we want to increase their awareness about topology as a possible source of semantic features; (b) for researchers using machine learning for NLP tasks, we want to propose the use of topological features when the number of training examples is small; and (c) for those already aware of the existence of computational topology, we see this work as contributing to the discussion about the value of topology for NLP, in view of mixed results reported by others.