Encoding Text Information with Graph Convolutional Networks for Personality Recognition
Personality recognition is a classic and important problem in social engineering. Due to the small number and particularity of personality recognition databases, only limited research has explored convolutional neural networks for this task. In this paper, we explore the use of graph convolutional n...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-06-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/10/12/4081 |
| _version_ | 1797565454044102656 |
|---|---|
| author | Zhe Wang Chun-Hua Wu Qing-Biao Li Bo Yan Kang-Feng Zheng |
| author_facet | Zhe Wang Chun-Hua Wu Qing-Biao Li Bo Yan Kang-Feng Zheng |
| author_sort | Zhe Wang |
| collection | DOAJ |
| description | Personality recognition is a classic and important problem in social engineering. Due to the small number and particularity of personality recognition databases, only limited research has explored convolutional neural networks for this task. In this paper, we explore the use of graph convolutional network techniques for inferring a user’s personality traits from their Facebook status updates or essay information. Since the basic five personality traits (such as openness) and their aspects (such as status information) are related to a wide range of text features, this work takes the Big Five personality model as the core of the study. We construct a single user personality graph for the corpus based on user-document relations, document-word relations, and word co-occurrence and then learn the personality graph convolutional networks (personality GCN) for the user. The parameters or the inputs of our personality GCN are initialized with a one-hot representation for users, words and documents; then, under the supervision of users and documents with known class labels, it jointly learns the embeddings for users, words, and documents. We used feature information sharing to incorporate the correlation between the five personality traits into personality recognition to perfect the personality GCN. Our experimental results on two public and authoritative benchmark datasets show that the general personality GCN without any external word embeddings or knowledge is superior to the state-of-the-art methods for personality recognition. The personality GCN method is efficient on small datasets, and the average F1-score and accuracy of personality recognition are improved by up to approximately 3.6% and 2.4–2.57%, respectively. |
| first_indexed | 2024-03-10T19:12:26Z |
| format | Article |
| id | doaj.art-c6eba0a12c624b3ea8e55f417ee9bd77 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T19:12:26Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c6eba0a12c624b3ea8e55f417ee9bd772023-11-20T03:42:40ZengMDPI AGApplied Sciences2076-34172020-06-011012408110.3390/app10124081Encoding Text Information with Graph Convolutional Networks for Personality RecognitionZhe Wang0Chun-Hua Wu1Qing-Biao Li2Bo Yan3Kang-Feng Zheng4School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaPersonality recognition is a classic and important problem in social engineering. Due to the small number and particularity of personality recognition databases, only limited research has explored convolutional neural networks for this task. In this paper, we explore the use of graph convolutional network techniques for inferring a user’s personality traits from their Facebook status updates or essay information. Since the basic five personality traits (such as openness) and their aspects (such as status information) are related to a wide range of text features, this work takes the Big Five personality model as the core of the study. We construct a single user personality graph for the corpus based on user-document relations, document-word relations, and word co-occurrence and then learn the personality graph convolutional networks (personality GCN) for the user. The parameters or the inputs of our personality GCN are initialized with a one-hot representation for users, words and documents; then, under the supervision of users and documents with known class labels, it jointly learns the embeddings for users, words, and documents. We used feature information sharing to incorporate the correlation between the five personality traits into personality recognition to perfect the personality GCN. Our experimental results on two public and authoritative benchmark datasets show that the general personality GCN without any external word embeddings or knowledge is superior to the state-of-the-art methods for personality recognition. The personality GCN method is efficient on small datasets, and the average F1-score and accuracy of personality recognition are improved by up to approximately 3.6% and 2.4–2.57%, respectively.https://www.mdpi.com/2076-3417/10/12/4081personality recognitionword co-occurrenceinformation sharingcorrelationpersonality GCN |
| spellingShingle | Zhe Wang Chun-Hua Wu Qing-Biao Li Bo Yan Kang-Feng Zheng Encoding Text Information with Graph Convolutional Networks for Personality Recognition Applied Sciences personality recognition word co-occurrence information sharing correlation personality GCN |
| title | Encoding Text Information with Graph Convolutional Networks for Personality Recognition |
| title_full | Encoding Text Information with Graph Convolutional Networks for Personality Recognition |
| title_fullStr | Encoding Text Information with Graph Convolutional Networks for Personality Recognition |
| title_full_unstemmed | Encoding Text Information with Graph Convolutional Networks for Personality Recognition |
| title_short | Encoding Text Information with Graph Convolutional Networks for Personality Recognition |
| title_sort | encoding text information with graph convolutional networks for personality recognition |
| topic | personality recognition word co-occurrence information sharing correlation personality GCN |
| url | https://www.mdpi.com/2076-3417/10/12/4081 |
| work_keys_str_mv | AT zhewang encodingtextinformationwithgraphconvolutionalnetworksforpersonalityrecognition AT chunhuawu encodingtextinformationwithgraphconvolutionalnetworksforpersonalityrecognition AT qingbiaoli encodingtextinformationwithgraphconvolutionalnetworksforpersonalityrecognition AT boyan encodingtextinformationwithgraphconvolutionalnetworksforpersonalityrecognition AT kangfengzheng encodingtextinformationwithgraphconvolutionalnetworksforpersonalityrecognition |