Summary of Eerpd: Leveraging Emotion and Emotion Regulation For Improving Personality Detection, by Zheng Li et al.
EERPD: Leveraging Emotion and Emotion Regulation for Improving Personality Detection
by Zheng Li, Dawei Zhu, Qilong Ma, Weimin Xiong, Sujian Li
First submitted to arxiv on: 23 Jun 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 proposes a new approach to detecting personality traits from text, called EERPD (Emotion-based Emotion Regulation Personality Detection). The authors draw on psychological knowledge, incorporating emotion regulation as a key feature in conjunction with traditional emotion features. This innovative method enables few-shot learning and provides process context templates for inferring labels from text. By leveraging Large Language Models (LLMs) to understand personality within text, EERPD improves the accuracy and robustness of personality detection. Experimental results demonstrate a significant enhancement over previous state-of-the-art methods by 15.05% and 4.29%, respectively, on two benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how computers can better understand people’s personalities based on what they write. The team developed a new method called EERPD that uses emotions to help predict personality. This approach combines emotions with psychological knowledge about how people regulate their feelings. By using this combined information, the method improves its ability to accurately detect personality traits from text. In tests, EERPD performed much better than previous methods, showing great promise for understanding personalities through writing. |
Keywords
» Artificial intelligence » Few shot
