| Issue |
E3S Web Conf.
Volume 409, 2023
International Conference on Management Science and Engineering Management (ICMSEM 2023)
|
|
|---|---|---|
| Article Number | 02007 | |
| Number of page(s) | 8 | |
| Section | Decision Support Systems | |
| DOI | https://doi.org/10.1051/e3sconf/202340902007 | |
| Published online | 01 August 2023 | |
A Research on Hot Spots of Psychological Warning Based on Co-word Analysis and Visualization
1 School of Intelligent Medical, Chengdu University of Traditional Chinese Medicine, People’s Republic of China
2 School of Management, Chengdu University of Traditional Chinese Medicine, People’s Republic of China
3 Adelaide Medical School, University of Adelaide, Australia
* e-mail: 420783639@qq.com
Offering reference for sustainable researches of psychological warning field by analyzing hotspots in it in the latest decade. 589 literatures relevant to psychological warning conformed to standard, were extracted keywords. Cooccurrence matrix and lexical matrix were exported by using python software, and the correlations among high frequency keywords were received by analyzing the combination between social network and semantic network which also drew the semantic network graph. The hierarchical clustering function was used to draw the high frequency keywords clustering analysis diagram, and the Eucidean range model was used to draw the two-dimensional scale analysis graph. The combination between co-word analysis and knowledge graph revealed, explored, and summarized the current research hotspots and their relationships in the field of psychological warning. Hotspots research showed the necessity of researches in the psychological warning field. The construction of psychological warning evaluation system and the analysis of key elements of psychological warning should be attached importance.
Key words: Psychological warning / Co-word analysis / Knowledge graph / Research hotspots
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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