Computational thematics: Comparing algorithms for clustering the genres
  of literary fiction
Oleg Sobchuk Artjoms Šeļa
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.