在口腔健康教育中分析恐慌和焦虑情绪的可解释的Roberta方法YouTube评论
Pradeep Kumar Yadalam1, Mohamed Thaha1, Prabhu Manickam Natarajan2
1Department of Periodontics, Saveetha Institute of Medical and Technical Sciences, Saveetha Dental College and Hospital, Saveetha University, Chennai, Tamil Nadu, 600077, India.
Scientific reports
|July 2, 2025
概括
这项研究使用语言模型RoBERTa来检测在线口腔健康视频中引起焦虑的评论. 该模型实现了75%的准确性,显示了改善健康教育和用户体验的潜力.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 人工智能 (AI) 是一种人工智能.
- 卫生沟通健康沟通
背景情况:
- 在线视频对于健康教育至关重要,但在评论中经常包含错误信息,导致用户焦虑.
- 在线健康内容中的错误信息会对心理健康,教育效率和用户信任产生负面影响.
- 需要自动化系统来调节评论,并确保在线健康信息的质量.
研究的目的:
- 在口腔健康教育视频中识别和分类引起压力的评论.
- 利用先进的自然语言处理 (NLP) 模型进行实时评论分析.
- 提高在线健康教育平台的安全性和可靠性.
主要方法:
- 利用基于变压器的架构RoBERTa语言模型进行评论分类.
- 微调了RoBERTa模型,在口腔健康视频评论数据集上优化了超参数.
- 实施了一种工作流,涉及数据摄入,代币化,嵌入,编码和分类.
主要成果:
- 罗伯塔模型在分类引起恐慌和焦虑的评论方面取得了75.00%的整体准确性.
- 获得了74.76%的精度和0.800的回忆力来识别引起焦虑的评论.
- 展示了深度学习在与健康相关的在线讨论中分析情绪的潜力.
结论:
- 基于RoBERTa的深度学习显示出对分类与牙科相关的评论和检测引起焦虑的内容的承诺.
- 开发的框架可以为在线健康教育的自动评论调节系统做出贡献.
- 需要进一步优化模型以提高准确性,特别是对于恐慌和信息评论类别.
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