EERPD: Leveraging Emotion and Emotion Regulation for Improving Personality Detection

Abstract

Personality is a fundamental construct in psychology, reflecting an individual’s behavior, thinking, and emotional patterns. While previous researches have made progress in personality detection, their designed methods generally overlook the important connection between psychological knowledge “emotion regulation” and personality traits. Based on this, we propose a new personality detection method called EERPD. This method introduces the use of emotion regulation, a psychological concept highly correlated with personality, for personality prediction. By combining this concept with emotion features, EERPD retrieves few-shot examples and provides process CoTs for inferring labels from text. This approach enhances the understanding of LLM for personality implicit within text and improves the performance in personality detection. Experimental results demonstrate that EERPD significantly enhances the accuracy and robustness of personality detection, outperforming previous SOTA by 15.05/4.29 in average F1 on the two benchmark datasets.