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Covid 19 Vaccine Public Opinion Analysis on Twitter Using Naive Bayes

EasyChair Preprint no. 10212

10 pagesDate: May 18, 2023


Twitter is a viable data source for studying public opinion. The study aims to identify public opinion and sentiments toward the Covid-19 vaccine and examine conversations posted on Twitter. The study examined two Datasets; one of 7500 tweets collected using RapidMiner from June 7- 17, 2021, and 9865 tweets collected from Kaggle on the 3rd of January 2021. It used the Naive Bay model to classify, analyze and visualize tweets according to polarity, K-means clustering, and key tweet topics. The study showed that positive sentiments were dominant in both times; it also realized that positive polarity increased over time from January to June 2021. In addition, vaccine acceptance became more prevalent in the tweets’ discussions and topics. Understanding sentiments and opinions toward Covid-19 vaccine using Twitter is critical to supporting public health organizations to execute promotions plans and encourage positive messages towards Covid-19 to improve vaccination mitigation and vaccine intake.

Keyphrases: COVID-19 vaccine, datasets, Naive Bay, public opinion, Twitter

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Samar Ibrahim and Sherief Abdallah},
  title = {Covid 19 Vaccine Public Opinion Analysis on Twitter Using Naive Bayes},
  howpublished = {EasyChair Preprint no. 10212},

  year = {EasyChair, 2023}}
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