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.
@article{arxiv.2303.03510,
title = {Guilt Detection in Text: A Step Towards Understanding Complex Emotions},
author = {Abdul Gafar Manuel Meque and Nisar Hussain and Grigori Sidorov and Alexander Gelbukh},
journal= {arXiv preprint arXiv:2303.03510},
year = {2023}
}