提高多标签分类不平衡的COVID-19疫苗接种犹的推文使用集体学习.
1Computer Engineering Department, Arab Academy for Science, Technology and Maritime Transport, Abukir, Alexandria, 1029, Egypt.
Computers in biology and medicine
|November 24, 2024
概括
这项研究通过改善推特分类来增强社交媒体上的疫苗话语分析. 一个带有过量采样的整体BERT模型显著提高了不平衡公共卫生数据集的性能.
科学领域:
- 社交媒体分析.
- 公共卫生信息学 公共卫生信息学
- 计算语言学计算语言学
背景情况:
- 接种疫苗是公共卫生的关键,但疫苗犹在全球范围内仍然存在.
- 分析社交媒体话语对于了解公众对疫苗的情绪至关重要.
- 社交媒体数据中的阶级不平衡对准确分析具有重大挑战.
研究的目的:
- 改进与公共卫生和疫苗话语相关的推特的分类.
- 为了应对多标签,多类推文分类中阶级不平衡的挑战.
- 用BERT模型进行增强的推特分析来评估基于集体的框架.
主要方法:
- 研究过量采样技术来处理不平衡的数据集.
- 开发了一个整体框架,利用多个BERT模型对各种文本实体进行了微调.
- 将拟议的模型应用于12个不平衡类的基准数据集.
主要成果:
- 与现有方法相比,在绩效指标方面取得了显著的改进.
- 观察到微观,宏观和加权平均F1分数增加了2%.
- 显示精度增加了4%,平均Jaccard指数增加了3%.
结论:
- 微调的BERT模型采用整体方法和过量采样有效地改善了对不平衡的公共卫生数据集的推特分类.
- 拟议的方法对分析社交媒体上的疫苗话语充满希望.
- 这种方法可以通过更好地了解公众论来支持公共卫生倡议.
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