# EERPD: Leveraging Emotion and Emotion Regulation for Improving Personality Detection

arXiv: https://arxiv.org/abs/2406.16079 · HTML: https://mlsanity.com/paper/2406.16079/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 66 references that are in the map.
- First posted on arXiv 23 Jun 2024.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [PANDORA Talks: Personality and Demographics on Reddit](https://mlsanity.com/paper/2004.04460/) (2020)
- [PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection](https://mlsanity.com/paper/2310.20256/) (2023)
- [LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced Personality Detection Model](https://mlsanity.com/paper/2403.07581/) (2024)
- [MBTI Personality Prediction for Fictional Characters Using Movie Scripts](https://mlsanity.com/paper/2210.10994/) (2022)
- [Orders Are Unwanted: Dynamic Deep Graph Convolutional Network for Personality Detection](https://mlsanity.com/paper/2212.01515/) (2022)
- [Using Nuances of Emotion to Identify Personality](https://mlsanity.com/paper/1309.6352/) (2013)
- [Multitask Learning for Emotion and Personality Detection](https://mlsanity.com/paper/2101.02346/) (2021)

## Built on by (most cited first, 7 of 7)

- [EmoPerso: Enhancing Personality Detection with Self-Supervised Emotion-Aware Modelling](https://mlsanity.com/paper/2509.02450/) (2025)
- [Prompting-in-a-Series: Psychology-Informed Contents and Embeddings for Personality Recognition With Decoder-Only Models](https://mlsanity.com/paper/2512.06991/) (2025)
- [HIPPD: Brain-Inspired Hierarchical Information Processing for Personality Detection](https://mlsanity.com/paper/2510.09893/) (2025)
- [Cross-Lingual Attention Distillation with Personality-Informed Generative Augmentation for Multilingual Personality Recognition](https://mlsanity.com/paper/2604.08851/) (2026)
- [Is AI Catching Up to Human Expression? Exploring Emotion, Personality, Authorship, and Linguistic Style in English and Arabic with Six Large Language Models](https://mlsanity.com/paper/2603.23251/) (2026)
- [Large-Language-Models-as-a-Judge in Theory-Agnostic Adaptive Metric-Alignment for Prototypical Networks in Personality Recognition](https://mlsanity.com/paper/2607.08374/) (2026)
- [PTEI: Integrating Personality Traits to Enhance Emotional Intelligence in Large Language Models](https://mlsanity.com/paper/2607.10245/) (2026)

## Abstract (arXiv)

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.