2023 Fiscal Year Annual Research Report
Negative emotions in literature: a computational approach to tone and mood
| Project/Area Number |
22K18154
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| Research Institution | Waseda University |
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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)
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| Project Period (FY) |
2022-04-01 – 2024-03-31
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| Keywords | sentiment analysis / CLS / emotion detection / NLP / machine learning / word embeddings / affect studies / literary analysis |
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| Outline of Annual Research Achievements |
For this project emotion intensity lexicons were enhanced using word embeddings. Using this method several literary corpora were annotated for emotion arcs with a total of more than 10,000 annotated books. We were able 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.
Furthermore, we 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.
The results were disseminated over 7 peer-reviewed publications.
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[Journal Article] EmotionArcs: Emotion Arcs for 9,000 Literary Texts2024
Author(s)
Emily Ohman, Yuri Bizzoni, Pascale Feldkamp Moreira, Kristoffer Nielbo
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Journal Title
Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)
Volume: -
Pages: 51-66
Peer Reviewed / Open Access / Int'l Joint Research
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