在一个服务不足的社区中感知COVID-19的影响:一种自然语言处理方法
Ashleigh Holmes1, Amanjot Singh Sachar2, Yu-Ping Chang1
1School of Nursing, The State University of New York, University at Buffalo, Buffalo, New York, USA.
Journal of advanced nursing
|October 7, 2024
概括
自然语言处理 (NLP) 增强了在服务不足的社区进行COVID-19影响研究的传统主题分析. NLP为流行病经验提供了新的见解,补充了定性研究方法.
科学领域:
- 定性研究方法 定性研究方法
- 公共卫生 公共卫生
- 计算语言学 计算语言学
背景情况:
- COVID-19显著影响了社会活动,精神,情感,身体和精神福祉在服务不足的社区.
- 传统的主题分析是定性数据的常见方法,但对于大型数据集可能需要大量的时间.
研究的目的:
- 应用自然语言处理 (NLP) 来分析COVID-19在服务不足的社区的影响.
- 将NLP分析与采访数据的传统专题分析进行比较.
主要方法:
- 分析了来自美国服务不足社区的50次采访记录 (2021年6月至2022年5月).
- 自然语言处理涉及数据提取,预处理,文字嵌入,主题建模和可视化.
- 10个采访的子集进行了传统的主题分析以进行比较.
主要成果:
- 主题分析确定了与流行病经验相关的六个关键主题.
- NLP确定了四个语境上相似的单词集群,基于频率的词嵌入显示了与主题分析的强烈对齐.
- NLP揭示了对时间的流逝和流行病影响的相互联系的见解.
结论:
- NLP分析补充了主题分析,为时间和语义联系提供了新的视角.
- 在分析广泛的定性数据方面,NLP和主题分析的综合方法被证明是高效和有效的.
- 在定性分析方面,NLP是一个有前途的工具,它能够捕捉到超越传统方法的独特元素和单词组合.
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