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Machine learning-based guilt detection in text.
- Source :
- Scientific Reports; 7/15/2023, p1-12, 12p
- Publication Year :
- 2023
-
Abstract
- We introduce a novel Natural Language Processing (NLP) task called guilt detection, which focuses on detecting guilt in text. We identify guilt as a complex and vital emotion that has not been previously studied in NLP, and we aim to provide a more fine-grained analysis of it. To address the lack of publicly available corpora for guilt detection, we created VIC, a dataset containing 4622 texts from three existing emotion detection datasets that we binarized into guilt and no-guilt classes. We experimented with traditional machine learning methods using bag-of-words and term frequency-inverse document frequency features, achieving a 72% f1 score with the highest-performing model. Our study provides a first step towards understanding guilt in text and opens the door for future research in this area. [ABSTRACT FROM AUTHOR]
- Subjects :
- NATURAL language processing
GUILT (Psychology)
MACHINE learning
Subjects
Details
- Language :
- English
- ISSN :
- 20452322
- Database :
- Complementary Index
- Journal :
- Scientific Reports
- Publication Type :
- Academic Journal
- Accession number :
- 164947364
- Full Text :
- https://doi.org/10.1038/s41598-023-38171-0