PS-GCN: psycholinguistic graph and sentiment semantic fused graph convolutional networks for personality detection-Reference-Cited by-同舟云学术

PS-GCN: psycholinguistic graph and sentiment semantic fused graph convolutional networks for personality detection

Published:2023-12-30 Issue:1 Volume:36 Page:
ISSN:0954-0091
Container-title:Connection Science
language:en
Short-container-title:Connection Science

Author:

Liu Wenjuan¹²^ORCID,Sun Zhengyan³^ORCID,Wei Subo⁴^ORCID,Zhang Shunxiang¹²^ORCID,Zhu Guangli¹²^ORCID,Chen Lei³^ORCID

Affiliation:

1. School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan, People’s Republic of China

2. Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, People's Republic of China

3. School of Computer Science, Huainan normal University, Huainan, People’s Republic of China

4. School of Computer Engineering and Science, Shanghai University, Shanghai, People's Republic of China

Funder

the Opening Foundation of State Key Laboratory of Cognitive Intelligence, iFLYTEK

the National Natural Science Foundation of China

the University Synergy Innovation Program of Anhui Province

Publisher

Informa UK Limited

Subject

Artificial Intelligence,Human-Computer Interaction,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/09540091.2023.2295820

Reference47 articles.

1. The impact of personality traits on users’ information-seeking behavior

2. SenticNet 5: Discovering Conceptual Primitives for Sentiment Analysis by Means of Context Embeddings

3. Cui B. & Qi C. (2017). Survey analysis of machine learning methods for natural language processing for MBTI personality type prediction. https://arxiv.org/abs/1707.07012.

4. Personality Structure: Emergence of the Five-Factor Model

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