Summary of Guret: Distinguishing Guilt and Regret Related Text, by Sabur Butt et al.
GuReT: Distinguishing Guilt and Regret related Text
by Sabur Butt, Fazlourrahman Balouchzahi, Abdul Gafar Manuel Meque, Maaz Amjad, Hector G. Ceballos Cancino, Grigori Sidorov, Alexander Gelbukh
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper delves into the intricacies of human decision-making and emotions, specifically guilt and regret, to better understand their impact on behavior and well-being. A novel dataset is introduced to analyze the relationship between guilt and regret, filling a gap in affective computing research. The study employs various machine learning and transformer-based deep learning techniques to recognize and interpret emotional states. Notably, transformer-based models demonstrate a significant performance edge, achieving a 90.4% macro F1 score compared to the best machine learning classifier’s 85.3%. This highlights their ability to distinguish complex emotional states. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how our emotions, like guilt and regret, affect our decisions and well-being. It creates a new dataset to study these emotions better. The researchers use different computer models to recognize and understand emotions. They find that special AI models called transformers are very good at understanding these complex emotions. This could help us make better choices and feel happier. |
Keywords
* Artificial intelligence * Deep learning * F1 score * Machine learning * Transformer
