How Do People View COVID-19 Vaccines: Analyses on Tweets About COVID-19 Vaccines Using Natural Language Processing and Sentiment Analysis
Document Type
Article
Publication Title
Journal of Global Information Management
Abstract
The COVID-19 pandemic has been the most devastating public health crisis in the recent decade and vaccination is anticipated as the means to terminate the pandemic. People’s views and feelings over COVID-19 vaccines determine the success of vaccination. This study was set to investigate sentiments and common topics about COVID-19 vaccines by machine learning sentiment and topic analyses with natural language processing on massive tweets data. Findings revealed that concern on COVID-19 vaccine grew alongside the introduction and start of vaccination programs. Overall positive sentiments and emotions were greater than negative ones. Common topics include vaccine development for progression, effectiveness, safety, availability, sharing of vaccines received, and updates on pandemics and government policies. Outcomes suggested the current atmosphere and its focus over the COVID-19 vaccine issue for the public health sector and policymakers for better decision-making. Evaluations on analytical methods were performed additionally.
DOI
10.4018/JGIM.300817
Publication Date
7-29-2022
Keywords
Clustering, COVID-19, Machine Learning, Sentiment Analysis, Topic Analysis, Twitter, Vaccine
Recommended Citation
V. Chang et al., "How Do People View COVID-19 Vaccines: Analyses on Tweets About COVID-19 Vaccines Using Natural Language Processing and Sentiment Analysis," Journal of Global Information Management, vol. 30, no. 10, Jul 2022.
The definitive version is available at https://doi.org/10.4018/JGIM.300817