Explainable AI for Sentiment Analysis of Human Metapneumovirus (HMPV) Using XLNet
It addresses public health monitoring for HMPV outbreaks, but is incremental as it applies existing methods to a new domain.
This paper tackled the problem of understanding public reactions to the Human Metapneumovirus (HMPV) outbreak by applying sentiment analysis to social media data, achieving 93.50% accuracy in classification using XLNet and enhancing transparency with explainable AI.
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.