# PsyAttention: Psychological Attention Model for Personality Detection

arXiv: https://arxiv.org/abs/2312.00293 · HTML: https://mlsanity.com/paper/2312.00293/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 49 references that are in the map.
- First posted on arXiv 1 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Recent Trends in Deep Learning Based Personality Detection](https://mlsanity.com/paper/1908.03628/) (2019)
- [Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensembles](https://mlsanity.com/paper/2010.01309/) (2020)
- [Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling](https://mlsanity.com/paper/2007.04571/) (2020)
- [Pushing on Personality Detection from Verbal Behavior: A Transformer Meets Text Contours of Psycholinguistic Features](https://mlsanity.com/paper/2204.04629/) (2022)
- [Measuring the Impact of (Psycho-)Linguistic and Readability Features and Their Spill Over Effects on the Prediction of Eye Movement Patterns](https://mlsanity.com/paper/2203.08085/) (2022)
- [Data Augmentation for Modeling Human Personality: The Dexter Machine](https://mlsanity.com/paper/2301.08606/) (2023)

## Built on by (most cited first, 3 of 3)

- [Integrating Multi-view Analysis: Multi-view Mixture-of-Expert for Textual Personality Detection](https://mlsanity.com/paper/2408.08551/) (2024)
- [Humanity in AI: Detecting the Personality of Large Language Models](https://mlsanity.com/paper/2410.08545/) (2024)
- [HIPPD: Brain-Inspired Hierarchical Information Processing for Personality Detection](https://mlsanity.com/paper/2510.09893/) (2025)

## Abstract (arXiv)

Work on personality detection has tended to incorporate psychological features from different personality models, such as BigFive and MBTI. There are more than 900 psychological features, each of which is helpful for personality detection. However, when used in combination, the application of different calculation standards among these features may result in interference between features calculated using distinct systems, thereby introducing noise and reducing performance. This paper adapts different psychological models in the proposed PsyAttention for personality detection, which can effectively encode psychological features, reducing their number by 85%. In experiments on the BigFive and MBTI models, PysAttention achieved average accuracy of 65.66% and 86.30%, respectively, outperforming state-of-the-art methods, indicating that it is effective at encoding psychological features.