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Encoding Text Information with Graph Convolutional Networks for Personality Recognition

School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China
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Appl. Sci. 2020, 10(12), 4081; https://doi.org/10.3390/app10124081
Received: 15 May 2020 / Revised: 9 June 2020 / Accepted: 10 June 2020 / Published: 13 June 2020
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. View Full-Text
Keywords: personality recognition; word co-occurrence; information sharing; correlation; personality GCN personality recognition; word co-occurrence; information sharing; correlation; personality GCN
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MDPI and ACS Style

Wang, Z.; Wu, C.-H.; Li, Q.-B.; Yan, B.; Zheng, K.-F. Encoding Text Information with Graph Convolutional Networks for Personality Recognition. Appl. Sci. 2020, 10, 4081. https://doi.org/10.3390/app10124081

AMA Style

Wang Z, Wu C-H, Li Q-B, Yan B, Zheng K-F. Encoding Text Information with Graph Convolutional Networks for Personality Recognition. Applied Sciences. 2020; 10(12):4081. https://doi.org/10.3390/app10124081

Chicago/Turabian Style

Wang, Zhe, Chun-Hua Wu, Qing-Biao Li, Bo Yan, and Kang-Feng Zheng. 2020. "Encoding Text Information with Graph Convolutional Networks for Personality Recognition" Applied Sciences 10, no. 12: 4081. https://doi.org/10.3390/app10124081

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