Analysing Tweets on COVID-19 Vaccine: A Text Mining Approach

被引:1
作者
Gottipati, Swetha [1 ]
Guha, Debashis [2 ]
机构
[1] SP Jain Sch Global Management, Sydney, NSW, Australia
[2] SP Jain Sch Global Management, Mumbai, Maharashtra, India
来源
2022 IEEE 12TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE (CCWC) | 2022年
关键词
vaccine; analytics; COVID-19; text mining; SENTIMENT ANALYSIS;
D O I
10.1109/CCWC54503.2022.9720793
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
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.
引用
收藏
页码:467 / 474
页数:8
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