使用BiGRU DistilBERT融合模型对COVID-19推文进行基于背景的情绪分析
Utkarsh Sharma1, Prateek Pandey2, Shishir Kumar3
1Department of Computer Science & Engineering, Jaypee University of Engineering and Technology, Guna, India. utkarsh_shar@yahoo.co.in.
Scientific reports
|November 11, 2025
概括
这项研究引入了一种新的融合模型,用于分析Twitter上的COVID-19公众情绪. 该模型准确地跟踪了疫情期间的情绪变化,为决策者提供了宝贵的见解.
科学领域:
- 计算社会科学 计算社会科学
- 自然语言处理自然语言处理.
- 公共卫生信息学 公共卫生信息学
背景情况:
- 随着COVID-19的流行,在Twitter等社交媒体平台上引发了广泛的公众讨论.
- 了解卫生危机期间的公众情绪对于有效的政策制定和危机管理至关重要.
研究的目的:
- 开发和评估一种混合深度学习模型,用于准确分析COVID-19相关推文的情绪.
- 调查疫情早期公共情绪的时间演变和区域变化.
主要方法:
- 开发了一个融合模型,结合了双向门循环单元 (BiGRU) 和DistilBERT变压器.
- 来自BiGRU和DistilBERT的特征被连接并输入到XGBoost元分类器.
- 该模型在来自八个国家的100多万条英语推特上进行了训练和验证 (2020年1月至4月).
主要成果:
- 融合模型实现了85.8%的分类准确度,超过了像DistilBERT (85.5%) 这样的单个模型.
- 公共情绪反映了大流行阶段,在病例/死亡激增期间负面情绪达到顶峰,在康复期间的积极情绪.
- 各个国家在情绪趋势中观察到显著的区域差异.
结论:
- 拟议的背景注入的融合模型有效地捕捉了卫生危机期间公众情绪的动态.
- 调查结果为决策者提供了关于社交媒体监控和公共沟通策略的可操作的见解.
- 该研究强调了在情绪分析中考虑时间和区域因素的重要性.
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