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Peer-reviewedCC BY 4.0PLOS One

Explainable AI for sentiment analysis of human metapneumovirus (HMPV) using XLNet

  • Md Shahriar Hossain Apu1
  • Md Saiful Islam2
  • Tanjim Taharat Aurpa3
  • Sharad Hasan
  1. 1Department of IoT and Robotics Engineering, University of Frontier Technology, Bangladesh, Bangladesh
  2. 2Department of Educational Technology and Engineering, University of Frontier Technology, Bangladesh, Bangladesh
  3. 3Department of Data Science and Engineering, University of Frontier Technology, Bangladesh, Bangladesh
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Abstract

The outbreak of Human Metapneumovirus (HMPV) in China, which later spread to the UK and other countries, raised significant public concern due to its potential impact on vulnerable populations. While HMPV typically causes mild symptoms, its effects on the elderly and immunocompromised individuals prompted health authorities to emphasize preventive measures. Moreover, continuous monitoring of respiratory viruses like HMPV remains important, as new factors (such as emerging variants) could alter their behavior over time. These factors have led to mixed public reactions, with some individuals expressing anxiety while others exhibit carelessness regarding the virus. This paper explores how sentiment analysis can enhance our understanding of public reactions to HMPV by analyzing data from social media platforms like YouTube. It highlights the importance of tracking public sentiment—ranging from fear to trust—to guide health messaging, inform policies, address misinformation, and encourage compliance with preventive measures during outbreaks. This study focuses on the use of sentiment analysis to understand public reactions to HMPV during the 2024 outbreak. The research applies advanced transformer models, particularly XLNet, achieving an accuracy of 93.50% in sentiment classification tasks. Additionally, We incorporate explainable AI (XAI) through SHAP to provide transparency in how the model identifies key factors influencing public sentiment.

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Cite

BibTeXRIS
BibTeX
@article{apu2026explainable,
  title = {{Explainable AI for sentiment analysis of human metapneumovirus (HMPV) using XLNet}},
  author = {Md Shahriar Hossain Apu and Md Saiful Islam and Tanjim Taharat Aurpa and Sharad Hasan},
  journal = {PLOS One},
  year = {2026},
  month = sep,
  doi = {10.1371/journal.pone.0351265},
  url = {https://doi.org/10.1371/journal.pone.0351265},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Explainable AI for sentiment analysis of human metapneumovirus (HMPV) using XLNet
AU  - Md Shahriar Hossain Apu
AU  - Md Saiful Islam
AU  - Tanjim Taharat Aurpa
AU  - Sharad Hasan
PY  - 2026
DA  - 2026/09/21
JO  - PLOS One
DO  - 10.1371/journal.pone.0351265
UR  - https://doi.org/10.1371/journal.pone.0351265
AB  - The outbreak of Human Metapneumovirus (HMPV) in China, which later spread to the UK and other countries, raised significant public concern due to its potential impact on vulnerable populations. While HMPV typically causes mild symptoms, its effects on the elderly and immunocompromised individuals prompted health authorities to emphasize preventive measures. Moreover, continuous monitoring of respiratory viruses like HMPV remains important, as new factors (such as emerging variants) could alter their behavior over time. These factors have led to mixed public reactions, with some individuals expressing anxiety while others exhibit carelessness regarding the virus. This paper explores how sentiment analysis can enhance our understanding of public reactions to HMPV by analyzing data from social media platforms like YouTube. It highlights the importance of tracking public sentiment—ranging from fear to trust—to guide health messaging, inform policies, address misinformation, and encourage compliance with preventive measures during outbreaks. This study focuses on the use of sentiment analysis to understand public reactions to HMPV during the 2024 outbreak. The research applies advanced transformer models, particularly XLNet, achieving an accuracy of 93.50% in sentiment classification tasks. Additionally, We incorporate explainable AI (XAI) through SHAP to provide transparency in how the model identifies key factors influencing public sentiment.
SN  - 1932-6203
ER  -