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arXiv · 2303.03510

Guilt Detection in Text: A Step Towards Understanding Complex Emotions

Abdul Gafar Manuel Meque·Nisar Hussain·Grigori Sidorov·Alexander Gelbukh
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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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Abdul Gafar Manuel Meque, Nisar Hussain, Grigori Sidorov, Alexander Gelbukh. 2023-03-06. Guilt Detection in Text: A Step Towards Understanding Complex Emotions. https://arxiv.org/abs/2303.03510

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