使用机器学习模型对希腊COVID-19的流行病监测报告的情绪分析
Christos Stefanis1, Elpida Giorgi1, Konstantinos Kalentzis1
1Laboratory of Hygiene and Environmental Protection, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece.
Frontiers in public health
|August 3, 2023
概括
对希腊COVID-19Facebook帖子的情绪分析揭示了公众对EODY监控报告的负面情绪. 机器学习模型准确地对这些情绪进行了分类,为公共卫生沟通提供了见解.
科学领域:
- 公共卫生信息学 公共卫生信息学
- 计算社会科学 计算社会科学
- 机器学习应用 机器学习应用
背景情况:
- 公共卫生组织越来越多地使用社交媒体在危机期间传播信息.
- 了解公众对健康建议的情绪对于有效的沟通策略至关重要.
- 随着COVID-19的流行,人们需要快速分析公众对健康措施的看法.
研究的目的:
- 在COVID-19大流行期间,对希腊国家公共卫生组织 (EODY) 的Facebook帖子中表达的公众情绪 (积极,消极,中立) 进行分类.
- 分析与EODY日常COVID-19监测报告的公众情绪相关的关键术语和主题.
- 评估机器学习模型在对公共卫生相关社交媒体数据的情绪分析中的表现.
主要方法:
- 利用微软Azure机器学习工作室对来自EODY页面的300条Facebook帖子进行情绪分析 (2021年11月 - 2022年1月).
- 应用自然语言处理技术来识别单词频率和情绪极性.
- 实施并评估机器学习分类器的效率,包括两类神经网络和两类贝叶斯点机.
主要成果:
- 确定了对COVID-19监测报告的主要负面公众情绪.
- 常见的术语包括"政府"",接种疫苗"",未接种疫苗"",健康措施"和"COVID-19测试".
- 使用双类神经网络和贝叶斯点机分类器,实现了高精度 (87%) 和F1分数 (超过35%) .
结论:
- 机器学习情绪分析为大流行期间公众对健康信息的看法提供了宝贵的见解.
- 调查结果强调需要有针对性的沟通策略,以解决公众对COVID-19监测的担忧.
- 这项研究是希腊首次尝试将机器学习应用于分析社交媒体上的公共卫生情绪.
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