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

Abstract

We investigate the use of unsupervised text mining methods for the analysis of prose literature works, using Thomas Pynchon's novel V. as a case study. 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.

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