Sentiment Analysis and Greek Tragedy
Dates
2019 -
Project Abstract
Aristotle famously described tragedy as a literary form that evokes feelings of pity and fear in its audience that lead to a sense of purification or catharsis. This project asks the extent to which the computational techniques of sentiment analysis and topic modeling can be used to identify and map these emotions on the collection of tragedies written by Aeschylus, Sophocles, and Euripides.
Our current project builds on previous work that constructed visualization tools that allowed for the exploration of linguistic data in Greek Tragedy using social networks that were integrated with linguistic data. This work is available online at https://daedalus.umkc.edu/VisualExplorer/ and it was published in the Proceedings of the 2011 Chicago Colloquium on Digital Humanities.
We have published two articles on this work that explore the ways that human readers understand the sentiments of passages from the text and how this compares to the ways that the computer understands sentiments. Our goal in these initial publications has been to validate the applicability of computational sentiment analysis methods that are designed for Tweets and Facebook posts to literary texts.
- "Interpretation of Sentiment Analysis in Aeschylus's Greek Tragedy" Vijaya Kumari Yeruva, Mayanka Chandrashekar, Yugyung Lee, Jeff Rydberg-Cox, Virginia Blanton, Nathan A Oyler. Proceedings of The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, December 2020. https://www.aclweb.org/anthology/2020.latechclfl-1.17/.
- "Interpretation of Sentiment Analysis with Human-in-the-Loop" Vijaya Kumari Yeruva, Mayanka Chandrashekar, Yugyung Lee, Jeff Rydberg-Cox, Virginia Blanton, Nathan A Oyler. Forthcoming in Proceedings of The 4th IEEE Workshop on Human-in-the-Loop Methods and Future of Work in BigData, December 2020. https://humanmachinedata.org.
Our current work focuses on computationally identifying the main topic clusters from corpus of Greek Tragedy, assigning sentiments to these topic clusters, associating these topic-sentiment pairs with characters and character types in the plays.
Project Team
Jeff Rydberg-Cox
Classics Program
Department of English
College of Arts & Sciences
School of Computing and Engineering
University of Missouri-Kansas City
Virginia Blanton
Department of English
College of Arts & Sciences
University of Missouri-Kansas City
Yugi Lee
Department of Computer
Science Electrical Engineering
School of Computing and Engineering
University of Missouri-Kansas City
Nathan Oyler
Department of Chemistry
School of Biological and Chemical Sciences
University of Missouri-Kansas City
Graduate Students
Vijaya Kumari Yeruva
Doctoral Student
Department of Computer Science and Electrical Engineering
School of Computing and Engineering
Mayanka Chandrashekar
Doctoral Student
Department of Computer Science and Electrical Engineering
School of Computing and Engineering