COVID-19 Vaccine Sensing: Sentiment Analysis from Twitter Data

ABSTRACT

The COVID-19 outbreak a pandemic, which poses a serious threat to global public health and lead to a tsunami of online social media. Individuals frequently express their views, opinions and emotions about the events of the pandemic on Twitter, Facebook, etc. Many researches try to analyze the sentiment of the COVID-19-related content from these social networks. However, they have rarely focused on the vaccine. In this paper, we study the COVID-19 vaccine topic from Twitter. Specifically, all the tweets related to COVID-19 vaccine from December 15th, 2020 to February 10th, 2021 are collected by using the Twitter API, then the unsupervised learning VADER model is used to judge the emotion categories (positive, neutral, negative) and calculate the sentiment value of the dataset. Based on the interaction between users, a communication topological network is constructed and the emotional direction is explored. We find that people had different sentiments between Chinese vaccine and those in other countries. The sentiment value might be affected by the number of daily news cases and deaths, the nature of key issues in the communication network. And revealing that the key nodes in the social network can produce emotional contagion to other nodes. © 2021 IEEE.