COVIDHealth:一种新的标记数据集和基于机器学习的Web应用程序,用于对Twitter上的COVID-19话语进行分类
Mahathir Mohammad Bishal1, Md Rakibul Hassan Chowdory1, Anik Das2
1Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chattogram, 4349, Bangladesh.
Heliyon
|August 5, 2024
概括
这项研究引入了一种机器学习工具,用于对Twitter上的COVID-19健康讨论进行分类 (现在是X). 卷积神经网络 (CNN) 实现了90.4%的准确性,超过了公共卫生数据分析的其他模型.
科学领域:
- 计算语言学和公共卫生信息学.
- 机器学习在分析社交媒体健康话语中的应用.
背景情况:
- 随着COVID-19大流行,在Twitter (现在是X) 等社交媒体平台上产生了广泛的与健康相关的对话.
- 分析这些数据的一个重大挑战是缺乏标记数据集以进行基于主题的分类和聚合.
- 这阻碍了有效的公共卫生监测和应对策略.
研究的目的:
- 开发一个基于机器学习的自动化网络应用程序,用于分类COVID-19健康话语.
- 将推特分为五个主要主题:健康风险,预防,症状,传播和治疗.
- 为公共卫生研究人员和从业人员提供一个有价值的工具.
主要方法:
- 使用Twitter API收集并手动标记了6667条与COVID-19相关的推文.
- 采用各种特征提取技术进行数据预处理.
- 对比了七种经典机器学习算法 (决策树,随机森林,SGD,Adaboost,KNN,物流回归,线性SVC) 和四种深度学习技术 (LSTM,CNN,RNN,BERT) 的性能.
主要成果:
- 卷积神经网络 (CNN) 模型实现了最高的性能指标:精度为90.41%,回忆率为90.4%,F1得分为90.4%,准确率为90.4%.
- 在经典的机器学习算法中,线性支持向量分类 (Linear SVC) 以85.71%的精度,86.94%的回忆率和86.13%的F1得分表现出最佳表现.
- 开发的Web应用程序为分析COVID-19健康数据提供了一个功能性和可访问的平台.
结论:
- 机器学习,特别是像CNN这样的深度学习模型,可以有效地分类和分类大量与公共卫生危机相关的社交媒体数据.
- 该研究提供了一套经过验证的数据集和一个实用的网络工具,以帮助理解和应对流行病期间的公共卫生挑战.
- 这些发现有助于推进与健康相关的数据分析和分类方法,以提高公共卫生意识和反应.
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