GuReT: Distinguishing Guilt and Regret related Text

• URL: http://arxiv.org/abs/2401.16541v1
• Date: Mon, 29 Jan 2024 20:20:44 GMT
• Title: GuReT: Distinguishing Guilt and Regret related Text
• Authors: Sabur Butt, Fazlourrahman Balouchzahi, Abdul Gafar Manuel Meque, Maaz Amjad, Hector G. Ceballos Cancino, Grigori Sidorov, Alexander Gelbukh
• Abstract summary: This paper introduces a dataset tailored to dissect the relationship between guilt and regret and their unique textual markers. 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.
• Score: 44.740281698788166
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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