Machine learning-based guilt detection in text
- PMID: 37454207
- PMCID: PMC10349868
- DOI: 10.1038/s41598-023-38171-0
Machine learning-based guilt detection in text
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.
© 2023. The Author(s).
Conflict of interest statement
The authors declare no competing interests.
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