GuReT: Distinguishing Guilt and Regret related Text
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911767416799232 |
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| author | Butt, Sabur Balouchzahi, Fazlourrahman Meque, Abdul Gafar Manuel Amjad, Maaz Cancino, Hector G. Ceballos Sidorov, Grigori Gelbukh, Alexander |
| author_facet | Butt, Sabur Balouchzahi, Fazlourrahman Meque, Abdul Gafar Manuel Amjad, Maaz Cancino, Hector G. Ceballos Sidorov, Grigori Gelbukh, Alexander |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16541 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GuReT: Distinguishing Guilt and Regret related Text Butt, Sabur Balouchzahi, Fazlourrahman Meque, Abdul Gafar Manuel Amjad, Maaz Cancino, Hector G. Ceballos Sidorov, Grigori Gelbukh, Alexander Computation and Language Artificial Intelligence 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. |
| title | GuReT: Distinguishing Guilt and Regret related Text |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2401.16541 |