PsyAttention: Psychological Attention Model for Personality Detection
Baohua Zhang, Yongyi Huang, Wenyao Cui, Zhang Huaping, Jianyun Shang
Abstract
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.- Anthology ID:
- 2023.findings-emnlp.222
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3398–3411
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2023.findings-emnlp.222/
- DOI:
- 10.18653/v1/2023.findings-emnlp.222
- Cite (ACL):
- Baohua Zhang, Yongyi Huang, Wenyao Cui, Zhang Huaping, and Jianyun Shang. 2023. PsyAttention: Psychological Attention Model for Personality Detection. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 3398–3411, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- PsyAttention: Psychological Attention Model for Personality Detection (Zhang et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2023.findings-emnlp.222.pdf