EmoPerso: Enhancing Personality Detection with Self-Supervised Emotion-Aware Modelling
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911134439702528 |
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| author | Shen, Lingzhi Cai, Xiaohao Long, Yunfei Razzak, Imran Chen, Guanming Jameel, Shoaib |
| author_facet | Shen, Lingzhi Cai, Xiaohao Long, Yunfei Razzak, Imran Chen, Guanming Jameel, Shoaib |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02450 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EmoPerso: Enhancing Personality Detection with Self-Supervised Emotion-Aware Modelling Shen, Lingzhi Cai, Xiaohao Long, Yunfei Razzak, Imran Chen, Guanming Jameel, Shoaib Computation and Language Machine Learning 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. |
| title | EmoPerso: Enhancing Personality Detection with Self-Supervised Emotion-Aware Modelling |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2509.02450 |