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• W4386003100 abstract "The COVID-19 pandemic has had far-reaching consequences globally, including a significant loss of lives, escalating unemployment rates, economic instability, deteriorating mental well-being, social conflicts, and even political discord. Vaccination, recognized as a pivotal measure in mitigating the adverse effects of COVID-19, has evoked a diverse range of sentiments worldwide. In particular, numerous users on social media platforms have expressed concerns regarding vaccine availability and potential side effects. Therefore, it is imperative for governmental authorities and senior health policy strategists to gain insights into the public's perspectives on vaccine mandates in order to effectively implement their vaccination initiatives. Despite the critical importance of comprehending the underlying factors influencing COVID-19 vaccine sentiment, the existing literature offers limited research studies on this subject matter. This paper presents an innovative methodology that harnesses Twitter data to extract sentiment pertaining to COVID-19 vaccination through the utilization of Artificial Intelligence techniques such as sentiment analysis, entity detection, linear regression, and logistic regression. The proposed methodology was applied and tested on live Twitter feeds containing COVID-19 vaccine-related tweets, spanning from February 14, 2021, to April 2, 2023. Notably, this approach successfully processed tweets in 45 languages originating from over 100 countries, enabling users to select from an extensive scenario space of approximately 3.55 × 10249 possible scenarios. By selecting specific scenarios, the proposed methodology effectively identified numerous determinants contributing to vaccine sentiment across iOS, Android, and Windows platforms. In comparison to previous studies documented in the existing literature, the presented solution emerges as the most robust in detecting the fundamental drivers of vaccine sentiment and demonstrates the vaccination sentiments over a substantially longer period exceeding 24 months." @default.
• W4386003100 created "2023-08-20" @default.
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• W4386003100 date "2023-09-01" @default.
• W4386003100 modified "2023-10-10" @default.
• W4386003100 title "Identifying drivers of COVID-19 vaccine sentiments for effective vaccination policy" @default.
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• W4386003100 doi "https://doi.org/10.1016/j.heliyon.2023.e19195" @default.
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