The emotional arcs of horror: a distant reading of Stephen King’s novels
Conference Year
January 2022
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 poster, we analyze each of Stephen King’s novels using the Hedonometer. We create narrative time-series and word-shift graphs for each novel, quantifying the lexical changes responsible for the 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.
Primary Faculty Mentor Name
Chris Danforth
Secondary Mentor Name
Mikaela Fudolig, Peter Dodds
Status
Undergraduate
Student College
College of Engineering and Mathematical Sciences
Program/Major
Mathematics
Primary Research Category
Arts & Humanities
The emotional arcs of horror: a distant reading of Stephen King’s novels
Sentiment analysis, the computational inference of emotion in text through Natural Language Processing, is increasingly used to analyze social and cultural trends. In this poster, we analyze each of Stephen King’s novels using the Hedonometer. We create narrative time-series and word-shift graphs for each novel, quantifying the lexical changes responsible for the 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.