对COVID-19疫苗研究的全面分析:从主题建模到主题分类
Saeed Rouhani1, Fatemeh Mozaffari1
1Department of IT Management, College of Management, University of Tehran, Tehran, Iran.
Artificial intelligence in medicine
|September 27, 2024
概括
这项研究使用数据分析分析了COVID-19疫苗研究,确定了8个关键研究领域,并随着时间的推移跟踪了研究人员的情绪. 开发了一个分类模型来预测新的研究主题和情绪.
科学领域:
- 疫苗学 疫苗学 疫苗学
- 数据科学数据科学数据科学
- 图书统计学 图书统计学
背景情况:
- COVID-19疫苗的研究是广泛和快速发展的.
- 二级研究对于从这个庞大的知识体系中提取见解至关重要.
- 现有的数据分析方法需要推进疫苗研究分析.
研究的目的:
- 对COVID-19疫苗研究摘要进行全面分析.
- 为了确定关键的研究领域和跟踪情绪趋势.
- 为新研究文档开发一个预测分类模型.
主要方法:
- 主题建模应用于Scopus和PubMed的4803个摘要.
- 情绪分析以追踪随时间推移 (2020-2022) 的主题变化.
- 为主题和情绪预测开发CNN-LSTM分类模型.
主要成果:
- 确定了八个不同的研究领域:报告,接受,反应,调查意见,怀孕,变种量,分类调查和国际方法.
- 研究人员的态度和重点转移从2020年到2022年被确定.
- 一个CNN-LSTM模型在预测主导主题和情绪方面取得了75%的准确性.
结论:
- 该研究提供了当前COVID-19疫苗研究的结构概述,有助于未来的研究设计.
- 开发的分类模型可以根据主题和情绪对新研究文章进行分类.
- 调查结果为疫苗制造商提供了洞察力,以确定投资缺口和利基市场.
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