Social Media Sentiment Analysis Related to COVID-19 Vaccinations

ABSTRACT

The wake of the COVID-19 pandemic has yet again highlighted how vital immunization is for public health. Despite the dramatic spread of SARS-CoV-2 and its variants, there is a rising trend of people refusing to be vaccinated. As a result, governments and health experts must gather and understand public ideas and perceptions about vaccines to design engagement and education efforts about vaccine advantages. Sentiment analysis is a common method for acquiring a broad picture of public opinion, that enables the classification of people as those who are in favor or against vaccination, as well as the determination of the factors that influence their attitudes and beliefs. The purpose of this chapter is to describe the general approach to sentiment analysis in the context of vaccinations and review its different use cases. The chapter's experimental component integrates the utilization of a dataset retrieved from Kaggle, which contains COVID-19 vaccine-related Twitter data. When attempting to perform sentiment analysis, certain methodological steps need to be considered after data collection, including data pre-processing, technique selection and model construction, as well as model evaluation and results interpretation. Both supervised and unsupervised sentiment analysis methods are investigated in the model construction step, with the former involving the implementation of Support Vector Machines and Logistic Regression algorithms, and the latter involving the use of TextBlob and Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analysis tools. The performance of each algorithm and tool is evaluated, as is the performance of each sentiment detection approach in order to select the best performing one. Social media platforms have become a common source of information and misinformation regarding vaccines. Our effort aims to emphasize the importance of mining such readily available public attitudes, as well as forecast opinions and reactions related to vaccine uptake in near real-time. Such insights could be critical in dealing with health emergency situations like the ongoing coronavirus pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.