Guilt Detection in Text: A Step Towards Understanding Complex Emotions
This work addresses the lack of research on guilt detection in NLP, providing a foundational step for future studies in emotion analysis.
The paper tackled the problem of detecting guilt in text, a previously unstudied complex emotion in NLP, by creating a dataset of 4622 texts and achieving a 72% f1 score with traditional machine learning methods.
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.