PsyAttention: Psychological Attention Model for Personality Detection

Baohua Zhang, Yongyi Huang, Wenyao Cui, Zhang Huaping, Jianyun Shang

Submission Type: Regular Long Paper

Submission Track: Linguistic Theories, Cognitive Modeling, and Psycholinguistics

Submission Track 2: Computational Social Science and Cultural Analytics

Keywords: personality detection; BigFive; PsyAttention; psychological features

TL;DR: This paper adapts different psychological models in the proposed PsyAttention for personality detection, which can effectively encode psychological features.

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.

Submission Number: 231