PS-GCN: psycholinguistic graph and sentiment semantic fused graph convolutional networks for personality detection
Personality detection identifies personality traits in text. Current approaches often rely on deep learning networks for text representation but they overlook the significance of psychological language knowledge in connecting user language expression to psychological characteristics. Consequently, t...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Connection Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/09540091.2023.2295820 |
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| _version_ | 1850230009593069568 |
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| author | Wenjuan Liu Zhengyan Sun Subo Wei Shunxiang Zhang Guangli Zhu Lei Chen |
| author_facet | Wenjuan Liu Zhengyan Sun Subo Wei Shunxiang Zhang Guangli Zhu Lei Chen |
| author_sort | Wenjuan Liu |
| collection | DOAJ |
| description | Personality detection identifies personality traits in text. Current approaches often rely on deep learning networks for text representation but they overlook the significance of psychological language knowledge in connecting user language expression to psychological characteristics. Consequently, the accuracy of personality detection is compromised. To address this issue, this paper presents PS-GCN, a model integrating Psychological knowledge and Sentiment semantic features through Graph Convolution Networks. Firstly, the Bi-LSTM network captures local features of preprocessed sentences to accurately represent the output of sentence sentiment features. Secondly, GCNs map psycholinguistic knowledge, forming semantic networks of entities and relationships. P-GCN is designed to capture the dependency information between psycholinguistic features, while S-GCN utilises syntactic structure analysis to gather more abundant information features and enhance semantic understanding ability. Finally, attention calculation is employed to reinforce key features and weaken irrelevant information. Additionally, a sentence group model captures combined features of related sentences, effectively utilising the text structure to mine sentimental features. Experimental results on multiple datasets demonstrate that the proposed method significantly improves the classification accuracy in personality detection tasks. |
| format | Article |
| id | doaj-art-2a4db33bb2a64d14ba4b67843faebd15 |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-2a4db33bb2a64d14ba4b67843faebd152025-08-20T02:04:00ZengTaylor & Francis GroupConnection Science0954-00911360-04942024-12-0136110.1080/09540091.2023.2295820PS-GCN: psycholinguistic graph and sentiment semantic fused graph convolutional networks for personality detectionWenjuan Liu0Zhengyan Sun1Subo Wei2Shunxiang Zhang3Guangli Zhu4Lei Chen5School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan, People’s Republic of ChinaSchool of Computer Science, Huainan normal University, Huainan, People’s Republic of ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai, People's Republic of ChinaSchool of Computer Science and Engineering, Anhui University of Science & Technology, Huainan, People’s Republic of ChinaSchool of Computer Science and Engineering, Anhui University of Science & Technology, Huainan, People’s Republic of ChinaSchool of Computer Science, Huainan normal University, Huainan, People’s Republic of ChinaPersonality detection identifies personality traits in text. Current approaches often rely on deep learning networks for text representation but they overlook the significance of psychological language knowledge in connecting user language expression to psychological characteristics. Consequently, the accuracy of personality detection is compromised. To address this issue, this paper presents PS-GCN, a model integrating Psychological knowledge and Sentiment semantic features through Graph Convolution Networks. Firstly, the Bi-LSTM network captures local features of preprocessed sentences to accurately represent the output of sentence sentiment features. Secondly, GCNs map psycholinguistic knowledge, forming semantic networks of entities and relationships. P-GCN is designed to capture the dependency information between psycholinguistic features, while S-GCN utilises syntactic structure analysis to gather more abundant information features and enhance semantic understanding ability. Finally, attention calculation is employed to reinforce key features and weaken irrelevant information. Additionally, a sentence group model captures combined features of related sentences, effectively utilising the text structure to mine sentimental features. Experimental results on multiple datasets demonstrate that the proposed method significantly improves the classification accuracy in personality detection tasks.https://www.tandfonline.com/doi/10.1080/09540091.2023.2295820Personality detectionBi-LSTMGCNattention |
| spellingShingle | Wenjuan Liu Zhengyan Sun Subo Wei Shunxiang Zhang Guangli Zhu Lei Chen PS-GCN: psycholinguistic graph and sentiment semantic fused graph convolutional networks for personality detection Connection Science Personality detection Bi-LSTM GCN attention |
| title | PS-GCN: psycholinguistic graph and sentiment semantic fused graph convolutional networks for personality detection |
| title_full | PS-GCN: psycholinguistic graph and sentiment semantic fused graph convolutional networks for personality detection |
| title_fullStr | PS-GCN: psycholinguistic graph and sentiment semantic fused graph convolutional networks for personality detection |
| title_full_unstemmed | PS-GCN: psycholinguistic graph and sentiment semantic fused graph convolutional networks for personality detection |
| title_short | PS-GCN: psycholinguistic graph and sentiment semantic fused graph convolutional networks for personality detection |
| title_sort | ps gcn psycholinguistic graph and sentiment semantic fused graph convolutional networks for personality detection |
| topic | Personality detection Bi-LSTM GCN attention |
| url | https://www.tandfonline.com/doi/10.1080/09540091.2023.2295820 |
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