  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.
