Abstract
The world faced a major health crisis during the COVID-19 pandemic for nearly two and a half years following its emergence in late 2019. Receiving prompt and accurate news played a crucial role in making various decisions to control the pandemic situation. In recent years, social media has demonstrated the effectiveness of disseminating information very rapidly. A spectrum of research is ongoing in the field of Twitter sentiment analysis. However, most of these studies consider only the local viewpoint to understand public sentiment. In this work, a fine-tuned transformer-based model is used to determine the sentiments of tweets locally. Additionally, various real data sources are augmented to enhance the understanding of sentiment polarity. Moreover, it is challenging to obtain a labeled dataset for analyzing sentiment from Twitter based on both global and local viewpoints. Hence, we propose three methods to achieve precise sentiment analysis by considering both global and local perspectives. The classification results obtained from these methods are used to train various traditional classification models. These results can serve as ground truth for further analysis. Our extensive analysis shows that more precise sentiments, based on the global perspective, can be extracted and are useful for training datasets in different machine learning techniques.
Data availability
Due to Twitter’s Terms of Service and privacy policies, the tweet texts and associated user-level information cannot be publicly shared. The dataset with Tweet IDs and labels are made available. These Tweet IDs can be hydrated by other researchers using the Twitter API, in accordance with Twitter’s Developer Policy. The link of the dataset is available here https://docs.google.com/document/d/1XOENZGV00utJrsYEnLVIZTy4hIgZMwB09QPEA9WGKWc/edit?tab=t.0.
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Acknowledgements
A.M. acknowledges the support received from the MATRICS project grant (MTR/2020/000326) of SERB, DST, Govt. of India.
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D.C.: Conceptualization, Data curation, Writing - original draft, Methodology, Validation. S.C.: Conceptualization, Data curation, Methodology, Writing - original draft. A.M.: Conceptualization, Investigation, Supervision
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Chakrabarty, D., Chatterjee, S. & Mukhopadhyay, A. A global twitter sentiment analysis model for COVID-vaccination. Sci Rep (2026). https://doi.org/10.1038/s41598-026-38553-0
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DOI: https://doi.org/10.1038/s41598-026-38553-0
