  The understanding of the public response to COVID-19 vaccines is the key
success factor to control the COVID-19 pandemic. To understand the public
response, there is a need to explore public opinion. Traditional surveys are
expensive and time-consuming, address limited health topics, and obtain
small-scale data. Twitter can provide a great opportunity to understand public
opinion regarding COVID-19 vaccines. The current study proposes an approach
using computational and human coding methods to collect and analyze a large
number of tweets to provide a wider perspective on the COVID-19 vaccine. This
study identifies the sentiment of tweets using a machine learning rule-based
approach, discovers major topics, explores temporal trend and compares topics
of negative and non-negative tweets using statistical tests, and discloses top
topics of tweets having negative and non-negative sentiment. Our findings show
that the negative sentiment regarding the COVID-19 vaccine had a decreasing
trend between November 2020 and February 2021. We found Twitter users have
discussed a wide range of topics from vaccination sites to the 2020 U.S.
election between November 2020 and February 2021. The findings show that there
was a significant difference between tweets having negative and non-negative
sentiment regarding the weight of most topics. Our results also indicate that
the negative and non-negative tweets had different topic priorities and
focuses. This research illustrates that Twitter data can be used to explore
public opinion regarding the COVID-19 vaccine.
