Negative emotions in literature: a computational approach to tone and mood

2023 Fiscal Year Final Research Report

• Project/Area Number: 22K18154
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 90020:Library and information science, humanistic and social informatics-related
• Research Institution: Waseda University
• Principal Investigator: Ohman Emily 早稲田大学, 国際学術院, 講師テニュアトラック (60906543)
• Project Period (FY): 2022-04-01 – 2024-03-31
• Keywords: Emotion detection / sentiment analysis / digital humanities / literary analysis / NLP / machine learning

Outline of Final Research Achievements

With this project we were able to develop new tools for the computational detection, recognition, and analysis of mood in literary texts. We were able to
to show that the mood of a novel can be computationally detected with high accuracy using only the first 500 words of the first chapter. Additonally, we created emotion arc corpora for 1,000 Finnish novels and nearly 10,000 English novels.
Furthermore, we focused on negative emotions and examined specifically shame and guilt as cultural concepts in English and Japanese to show that shame is often portrayed as a public experience and guilt as an emotion that encompasses both private elements, akin to sadness, and public aspects, such as the motivation to openly acknowledge a transgression.

Free Research Field

Computational humanities

Academic Significance and Societal Importance of the Research Achievements

This research has added to the understanding of the literary concept of mood and how to detect it computationally. Additionally, the project has helped with the understanding of shame and guilt across and within cultures. It has also provided several open access corpora and other tools.