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- W4384407209 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." @default.
- W4384407209 created "2023-07-16" @default.
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- W4384407209 date "2023-07-15" @default.
- W4384407209 modified "2023-09-25" @default.
- W4384407209 title "Machine learning-based guilt detection in text" @default.
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- W4384407209 doi "https://doi.org/10.1038/s41598-023-38171-0" @default.
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