COVID-19 vaccine sensing: Sentiment analysis and subject distillation from twitter data

Abstract

The COVID-19 outbreak a pandemic, which poses a serious threat to global public health and result in 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 December 31st, 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. After calculating the number of topics, Latent Dirichlet Allocation (LDA) model is used to extract topics and keywords. We find that people had different sentiments between Chinese vaccine and those in other countries, and the sentiment value might be affected by the number of daily news cases and deaths, and the nature of key issues in the communication network, as well as revealing the intensity and evolution of 10 topics of major public concern, and provides insights into vaccine trust.