Explainable AI for Sentiment Analysis of Human Metapneumovirus (HMPV) Using XLNet
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913676348358656 |
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| author | Apu, Md. Shahriar Hossain Islam, Md Saiful Aurpa, Tanjim Taharat |
| author_facet | Apu, Md. Shahriar Hossain Islam, Md Saiful Aurpa, Tanjim Taharat |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01663 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Explainable AI for Sentiment Analysis of Human Metapneumovirus (HMPV) Using XLNet Apu, Md. Shahriar Hossain Islam, Md Saiful Aurpa, Tanjim Taharat Computation and Language 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. |
| title | Explainable AI for Sentiment Analysis of Human Metapneumovirus (HMPV) Using XLNet |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2502.01663 |