一种混合深度学习模型用于对COVID-19推文的情绪分析,并具有类平衡
Md Alamin Talukder1,2, Md Ashraf Uddin3, Suman Roy4
1Department of Computer Science and Engineering, International University of Business Agriculture and Technology, Dhaka, Bangladesh. alamin.cse@iubat.edu.
Scientific reports
|July 30, 2025
概括
这项研究引入了一种混合深度学习模型,用于分析COVID-19在社交媒体上的公众情绪. 该模型将BERT和LSTM与数据预处理和平衡相结合,显著提高了情绪分类的准确性.
科学领域:
- 自然语言处理自然语言处理.
- 计算社会科学 计算社会科学
- 人工智能的人工智能
背景情况:
- COVID-19大流行导致社交媒体上广泛的错误信息和多样化的公众情绪.
- 准确的情绪分析对于理解卫生危机期间的公共话语至关重要.
研究的目的:
- 开发和评估一种混合深度学习模型,用于分类与COVID-19相关的公众情绪.
- 通过先进的文本预处理和类平衡技术来提高情绪分析性能.
主要方法:
- 一个混合深度学习模型,集成来自变压器 (BERT) 和长短期内存 (LSTM) 网络的双向编码器表示.
- 先进的文本预处理:Unicode规范化,收缩扩展和表情符号转换.
- 使用随机过量抽样 (ROS) 缓解类失衡.
主要成果:
- 在ROS后,二进制分类准确率达到92.10%,三类准确率达到89.47%,五类准确率达到81.78%.
- 在数据集平衡后,在所有分类任务中观察到精度,灵敏度和特异性的显著改善.
- 该模型在准确分类各种COVID-19情绪方面表现出强的表现.
结论:
- 通过预处理和平衡增强的拟议的混合DL模型,对于对COVID-19社交媒体数据的多类情绪分析是有效的.
- 这种方法提供了一种可靠的方法来了解公共卫生紧急情况期间的论.
- 该研究强调了数据质量和数据平衡在社交媒体分析的深度学习中的重要性.
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