Date of Award
2022
Document Type
Thesis
Degree Name
M.S.
Department
Computer Science
First Advisor
Scott Leutenegger
Second Advisor
Sarah Gjertson
Third Advisor
Kerstin Haring
Keywords
Computational literature analysis, Data visualization, Natural language processing, Sentiment analysis
Abstract
Inequalities in gender representation and characterization in fictional works are issues that have long been discussed by social scientists. This work addresses these inequalities with two interrelated components. First, it contributes a sentiment and word frequency analysis task focused on gender-specific nouns and pronouns in 15,000 fictional works taken from the online library, Project Gutenberg. This analysis allows for both quantifying and offering further insight on the nature of this disparity in gender representation. Then, the outcomes of the analysis are harnessed to explore novel data visualization formats using computational and studio art techniques. Our results call attention to the need for several identified trends in this data set that require more cross-disciplinary investigation to unpack. Further, our combined approach demonstrate both the need for, and possible methods of, communicating data that warrants social change in an evocative and powerful way.
Publication Statement
Copyright is held by the author. User is responsible for all copyright compliance.
Recommended Citation
Leto, Alexandria, "Humanizing Computational Literature Analysis Through Art-Based Visualizations" (2022). Electronic Theses and Dissertations. 2130.
https://digitalcommons.du.edu/etd/2130
Provenance
Received from ProQuest
Rights holder
Alexandria Leto
File size
91 pgs
Copyright date
2022
File format
application/pdf
Language
en
Discipline
Computer science