  The intricate relationship between human decision-making and emotions,
particularly guilt and regret, has significant implications on behavior and
well-being. Yet, these emotions subtle distinctions and interplay are often
overlooked in computational models. This paper introduces a dataset tailored to
dissect the relationship between guilt and regret and their unique textual
markers, filling a notable gap in affective computing research. Our approach
treats guilt and regret recognition as a binary classification task and employs
three machine learning and six transformer-based deep learning techniques to
benchmark the newly created dataset. The study further implements innovative
reasoning methods like chain-of-thought and tree-of-thought to assess the
models interpretive logic. The results indicate a clear performance edge for
transformer-based models, achieving a 90.4% macro F1 score compared to the
85.3% scored by the best machine learning classifier, demonstrating their
superior capability in distinguishing complex emotional states.
