Date of Award


Document Type

Masters Thesis

Degree Name

M. S.

Organizational Unit

Daniel Felix Ritchie School of Engineering and Computer Science, Computer Science

First Advisor

Chris GauthierDickey

Second Advisor

Adam Rovner

Third Advisor

Scott Leutenegger


Natural language processing, Plot lines, Fiction, Computer science


When following a plot in a story, categorization is something that humans do without even thinking; whether this is simple classification like “This is science fiction” or more complex trope recognition like recognizing a Chekhov's gun or a rags to riches storyline, humans group stories with other similar stories. Research has been done to categorize basic plots and acknowledge common story tropes on the literary side, however, there is not a formula or set way to determine these plots in a story line automatically. This paper explores multiple natural language processing techniques in an attempt to automatically compare and cluster a fictional story into categories in an unsupervised manner. The aim is to mimic how a human may look deeper into a plot, find similar concepts like certain words being used, the types of words being used, for example an adventure book may have more verbs, as well as the sentiment of the sentences in order to group books into similar clusters.

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Rights Holder

Dalton J. Crutchfield


Received from ProQuest

File Format




File Size

74 p.


Computer science