EERPD: Leveraging Emotion and Emotion Regulation for Improving Personality Detection

TL;DR

This paper introduces EERPD, a novel personality detection method that incorporates emotion regulation insights to improve accuracy and robustness in predicting personality traits from text.

Contribution

It proposes integrating emotion regulation features with emotion cues for personality detection, leveraging psychological knowledge to enhance model performance.

Findings

Outperforms previous state-of-the-art by 15.05/4.29 in average F1 scores.

Significantly improves accuracy and robustness in personality detection.

Utilizes few-shot examples and process CoTs for better inference.

Abstract

Personality is a fundamental construct in psychology, reflecting an individual's behavior, thinking, and emotional patterns. Previous researches have made some progress in personality detection, primarily by utilizing the whole text to predict personality. However, these studies generally tend to overlook psychological knowledge: they rarely apply the well-established correlations between emotion regulation and personality. 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 feature with emotion features, it retrieves few-shot examples and provides process CoTs for inferring labels from text. This approach enhances the understanding of LLM for personality 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.