Analysing tweets on COVID-19 vaccine : A text mining approach

• Main Authors: GOTTIPATI, Swetha, GUHA, Debashis
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Vaccine sentiment analytics; COVID-19 vaccine; Text mining; Categorical Data Analysis; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/9891; https://ink.library.smu.edu.sg/context/sis_research/article/10891/viewcontent/IEEE_Final_13_3_1570784231.pdf
• Institution: Singapore Management University

The COVID-19 pandemic has caused large scale health, economic, and social crisis. Scientists throughout the globe have been working on producing effective vaccines to combat this pandemic. COVID-19 vaccine release started in 2020, and low take-up rates among the public have been observed initially. There has been a soar in social media data on vaccines. This paper presents a comprehensive analysis of COVID-19 vaccine-related tweets. Sentiments shared by people through tweets and common topics have been extracted using classification and sentiment analysis. Our results showed a higher negative sentiment when the pandemic was declared, and it gradually changed to positive with the COVID-19 vaccine development/rollout. Tweet sentiment analysis offers health departments around the globe a quick sense of public sentiment towards the vaccine. Dominant topics or areas of concern have been identified using topic modelling that might need to be addressed.