Unsupervised text mining methods for literature analysis: a case study for Thomas Pynchon's V.

Abstract

We demonstrate the use of unsupervised text mining methods for the analysis of prose literature works, using Thomas Pynchon's novel V. as an example. Our results suggest that such methods may be employed to reveal meaningful information regarding the novel’s structure. We report results using a wide variety of clustering algorithms, several distinct distance functions, and different visualization techniques. The application of a simple topic model is also demonstrated. We discuss the meaningfulness of our results along with the limitations of our approach, and we suggest some possible paths for further study.