Computational thematics: Comparing algorithms for clustering the genres of literary fiction
This work addresses the problem of automatically grouping literary texts by theme for researchers and librarians, but it is incremental as it focuses on comparing existing methods rather than introducing new ones.
The paper compared various unsupervised learning algorithms for clustering literary genres by thematic similarity, finding that the best combinations achieved high accuracy in genre classification while the worst performed poorly, as demonstrated on a corpus of 5000 novels.
What are the best methods of capturing thematic similarity between literary texts? Knowing the answer to this question would be useful for automatic clustering of book genres, or any other thematic grouping. This paper compares a variety of algorithms for unsupervised learning of thematic similarities between texts, which we call "computational thematics". These algorithms belong to three steps of analysis: text preprocessing, extraction of text features, and measuring distances between the lists of features. Each of these steps includes a variety of options. We test all the possible combinations of these options: every combination of algorithms is given a task to cluster a corpus of books belonging to four pre-tagged genres of fiction. This clustering is then validated against the "ground truth" genre labels. Such comparison of algorithms allows us to learn the best and the worst combinations for computational thematic analysis. To illustrate the sharp difference between the best and the worst methods, we then cluster 5000 random novels from the HathiTrust corpus of fiction.