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EmoPerso: Enhancing Personality Detection with Self-Supervised Emotion-Aware Modelling
Shen, Lingzhi Cai, Xiaohao Long, Yunfei Razzak, Imran Chen, Guanming Jameel, Shoaib
Shen, Lingzhi
Cai, Xiaohao
Long, Yunfei
Razzak, Imran
Chen, Guanming
Jameel, Shoaib
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3746252.3761247.pdf
Adobe PDF, 7.44 MB
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Department
Computational Biology
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Type
Conference proceeding
Date
2025
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Language
English
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Abstract
Personality detection from text is commonly performed by analysing users' social media posts. However, existing methods heavily rely on large-scale annotated datasets, making it challenging to obtain high-quality personality labels. Moreover, most studies treat emotion and personality as independent variables, overlooking their interactions. In this paper, we propose a novel self-supervised framework, EmoPerso, which improves personality detection through emotion-aware modelling. EmoPerso first leverages generative mechanisms for synthetic data augmentation and rich representation learning. It then extracts pseudo-labeled emotion features and jointly optimizes them with personality prediction via multi-task learning. A cross-attention module is employed to capture fine-grained interactions between personality traits and the inferred emotional representations. To further refine relational reasoning, EmoPerso adopts a self-taught strategy to enhance the model's reasoning capabilities iteratively. Extensive experiments on two benchmark datasets demonstrate that EmoPerso surpasses state-of-the-art models. The source code is available at https://github.com/slz0925/EmoPerso.
Citation
L. Shen, X. Cai, Y. Long, I. Razzak, G. Chen, and S. Jameel, “EmoPerso: Enhancing Personality Detection with Self-Supervised Emotion-Aware Modelling,” CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management, pp. 2577–2587, Nov. 2025, doi: 10.1145/3746252.3761247
Source
Proceedings of the 34th ACM International Conference on Information and Knowledge Management
Conference
34th ACM International Conference on Information and Knowledge Management, CIKM 2025
Keywords
emotion modelling, multi-task learning, personality detection, reasoning chains, self-supervised learning
Subjects
Source
34th ACM International Conference on Information and Knowledge Management, CIKM 2025
Publisher
Association for Computing Machinery