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    Sentiment analysis tracking of COVID-19 vaccine through tweets

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    Type
    Article
    Author
    Sarirete, Akila cc
    Date
    2022
    
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    Abstract
    Recent studies on the COVID-19 pandemic indicated an increase in the level of anxiety, stress, and depression among people of all ages. The World Health Organization (WHO) recently warned that even with the approval of vaccines by the Food and Drug Administration (FDA), population immunity is highly unlikely to be achieved this year. This paper aims to analyze people's sentiments during the pandemic by combining sentiment analysis and natural language processing algorithms to classify texts and extract the polarity, emotion, or consensus on COVID-19 vaccines based on tweets. The method used is based on the collection of tweets under the hashtag #COVIDVaccine while the nltk toolkit parses the texts, and the tf-idf algorithm generates the keywords. Both n-gram keywords and hashtags mentioned in the tweets are collected and counted. The results indicate that the sentiments are divided into positive and negative emotions, with the negative ones dominating.
    Journal title
    Journal of Ambient Intelligence and Humanized Computing
    DOI
    10.1007/s12652-022-03805-0
    ae974a485f413a2113503eed53cd6c53
    10.1007/s12652-022-03805-0
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