用XLNet分析社交媒体情绪，看公众如何反应新冠后新病毒
Explainable AI for Sentiment Analysis of Human Metapneumovirus (HMPV) Using XLNet
- 用XLNet模型分析社交网络情绪，准确率达93.50%
- 结合SHAP方法揭示模型决策依据，提升可解释性
- 适合关注公共健康舆情与AI可解释性的研究者
2024年，中国爆发人偏肺病毒（HMPV）疫情，随后蔓延至英国等国家，引发公众高度关注。尽管该病毒通常引起轻微症状，但对易感人群的影响促使卫生部门强调预防措施。本文探讨通过情感分析理解公众对HMPV的反应，利用社交媒体数据进行研究。采用基于Transformer的XLNet模型，在情感分类任务中达到93.50%的准确率。同时引入可解释人工智能（XAI）方法，使用SHAP工具增强模型透明度，揭示关键预测特征。
原文摘要 · Abstract (English)
In 2024, the outbreak of Human Metapneumovirus (HMPV) in China, which later spread to the UK and other countries, raised significant public concern. While HMPV typically causes mild symptoms, its effects on vulnerable individuals prompted health authorities to emphasize preventive measures. This paper explores how sentiment analysis can enhance our understanding of public reactions to HMPV by analyzing social media data. We apply transformer models, particularly XLNet, achieving 93.50% accuracy in sentiment classification. Additionally, we use explainable AI (XAI) through SHAP to improve model transparency.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。