Date of Completion
2022
Document Type
Honors College Thesis
Department
Mathematics
Thesis Type
Honors College
First Advisor
Chris Danforth
Second Advisor
Peter Dodds
Keywords
timeseries, Stephen King, language, literature, word frequency, emotion
Abstract
Sentiment analysis, the computational inference of emotion in text through Natural Language Processing, is increasingly used to analyze social and cultural trends. In this thesis, we create narrative time-series and word-shift graphs for each of Stephen King’s novels using the Hedonometer, quantifying the lexical changes responsible for emotional arcs found in each story. Our results suggest King’s work has increasingly shifted in genre from horror to science fiction. The work contributes to a growing science of stories being developed by the Computational Story Lab.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
Recommended Citation
Woods, Delaney, "The emotional arcs of horror: a distant reading of Stephen King’s novels" (2022). UVM Honors College Senior Theses. 509.
https://scholarworks.uvm.edu/hcoltheses/509