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Analyzing public sentiment towards COVID-19 vaccines is crucial for campaign success. This study used AI to analyze vaccine-related tweets, finding the BERT model accurately classified sentiments, aiding public health strategies.

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AI based modelingCOVID-19Sentiments monitoringSocial media data analysisVaccine hesitancyVaccines campaign

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Area of Science:

  • Public Health
  • Data Science
  • Computational Linguistics

Background:

  • The COVID-19 pandemic necessitates widespread vaccination for global health security.
  • Understanding public sentiment towards vaccines is vital for effective public health campaigns.

Purpose of the Study:

  • To analyze public reactions and sentiments regarding COVID-19 vaccines using social media data.
  • To evaluate the effectiveness of artificial intelligence methods in classifying vaccine-related public opinion.

Main Methods:

  • Utilized a publicly available COVID-19 vaccine dataset comprising tweets.
  • Applied Natural Language Processing (NLP) techniques, including TextBlob for sentiment polarity.
  • Employed the BERT model for classifying tweet sentiments into positive and negative categories.

Main Results:

  • The BERT model demonstrated high performance in classifying both positive and negative vaccine sentiments.
  • Achieved maximum precision, recall, and F-score values for sentiment classification tasks.
  • Sentiment analysis revealed public reactions to the vaccine campaign.

Conclusions:

  • AI, specifically the BERT model, is effective for analyzing public sentiment on COVID-19 vaccines from social media.
  • Accurate sentiment classification can inform and improve vaccine communication strategies.
  • This approach supports public health efforts in addressing vaccine hesitancy and promoting uptake.