Topic mining and analysis of Chinese and English literature on disaster nursing based on LDA model
10.3760/cma.j.cn115682-20211123-05277
- VernacularTitle:基于LDA主题模型的灾害护理领域中英文文献主题挖掘和分析
- Author:
Huifeng WANG
1
;
Wangdui NIMA
;
Hongmei DUAN
Author Information
1. 北京中医药大学护理学院，北京 102488
- Keywords:
Bibliometrics;
Disaster nursing;
Latent Dirichlet Allocation model;
Topic mining
- From:
Chinese Journal of Modern Nursing
2022;28(16):2116-2121
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the subject content and hot changes of disaster nursing in the Chinese and English databases, so as to provide references for related research, and to provide theoretical basis and information support for promoting the healthy development of disaster nursing.Methods:The Latent Dirichlet Allocation (LDA) model was used to model the literature topics on disaster nursing in Chinese and English databases such as China National Knowledge Infrastructure (CNKI), WanFang, and Web of Science until September 6, 2021, and generate a "topic-word" distribution probability matrix and analyze the results.Results:The analysis of the LDA model showed that there were 13 research topics in the Chinese literature, which could be grouped into 4 research directions, including the nursing and treatment of disaster victims, status of disaster nursing at home and abroad and development of pre-hospital first aid, assessment and guarantee of emergency rescue capacity in public health emergencies, capacity training and assessment of disaster nurses. The English literature had 6 research themes.Conclusions:Based on the extraction results of the LDA model, the research topics in the field of disaster nursing are accurately excavated, which is conducive to understanding the development trends and research hotspots of disaster nursing, and can provide a reliable reference for related research.
