Guilt Detection in Text: A Step Towards Understanding Complex Emotions

• URL: http://arxiv.org/abs/2303.03510v1
• Date: Mon, 6 Mar 2023 21:36:19 GMT
• Title: Guilt Detection in Text: A Step Towards Understanding Complex Emotions
• Authors: Abdul Gafar Manuel Meque, Nisar Hussain, Grigori Sidorov, and Alexander Gelbukh
• Abstract summary: We introduce a novel Natural Language Processing task called Guilt detection. We identify guilt as a complex and vital emotion that has not been previously studied in NLP. To address the lack of publicly available corpora for guilt detection, we created VIC, a dataset containing 4622 texts.
• Score: 58.720142291102135
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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