@inproceedings{zhang-etal-2023-psyattention,
title = "{P}sy{A}ttention: Psychological Attention Model for Personality Detection",
author = "Zhang, Baohua and
Huang, Yongyi and
Cui, Wenyao and
Huaping, Zhang and
Shang, Jianyun",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.222",
doi = "10.18653/v1/2023.findings-emnlp.222",
pages = "3398--3411",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T PsyAttention: Psychological Attention Model for Personality Detection
%A Zhang, Baohua
%A Huang, Yongyi
%A Cui, Wenyao
%A Huaping, Zhang
%A Shang, Jianyun
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F zhang-etal-2023-psyattention
%X 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.
%R 10.18653/v1/2023.findings-emnlp.222
%U https://aclanthology.org/2023.findings-emnlp.222
%U https://doi.org/10.18653/v1/2023.findings-emnlp.222
%P 3398-3411
Markdown (Informal)
[PsyAttention: Psychological Attention Model for Personality Detection](https://aclanthology.org/2023.findings-emnlp.222) (Zhang et al., Findings 2023)
ACL