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.

翻译：人类决策与情感（尤其是内疚与悔恨）之间的复杂关系对个体行为与幸福感具有重要影响，然而这些情感间的细微差异与相互作用在计算模型中常被忽视。本文构建了一个专用于剖析内疚与悔恨情感及其独特文本标记的数据集，填补了情感计算研究中的显著空白。我们将内疚与悔恨识别视为二分类任务，采用三种机器学习方法与六种基于Transformer的深度学习技术对该新数据集进行基准测试。研究进一步引入链式推理与树状推理等创新推理方法，评估模型的解释性逻辑。结果表明，基于Transformer的模型性能显著更优，其宏F1分数达到90.4%，而最佳机器学习分类器仅为85.3%，凸显了前者在区分复杂情感状态方面的卓越能力。