Sentiment Analysis of Covid Vaccine Myths using Various Data Visualization Tools - Bhatia, Tarandeep Kaur; Rathi, Samagya; Singh, Thipendra P; Naha, Biswayan (EAI Endorsed Transactions on Pervasive Health and Technology, 2024)
INTRODUCTION: Anti-vaccination agitation is on the rise, both in-person and online, notably on social media. The Internet has become the principal source of health-related information and vaccines for an increasing number of individuals. This is worrisome since, on social media, any comment, whether from a medical practitioner or a layperson, has the same weight. As a result, low-quality data may have a growing influence on vaccination decisions for children. OBJECTIVES: This paper will evaluate the scale and type of vaccine-related disinformation, the main purpose was to discover what caused vaccine fear and anti-vaccination attitudes among social media users. METHODS: The vaccination-related data used in this paper was gathered from Reddit, an information-sharing social media network with about 430 million members, to examine popular attitudes toward the vaccine. The materials were then pre-processed. External links, punctuation, and bracketed information were the first things to go. All text was also converted to lowercase. This was followed by a check for missing data. This paper is novel and different as Matplotlib, pandas, and word cloud was used to create word clouds and every result has a visual representation. The Sentiment analysis was conducted using the NLTK library as well as polarity and subjectivity graphs were generated. RESULTS: It was discovered that the majority population had neutral sentiments regarding vaccination. Data visualization methods such as bar charts showed that neutral sentiment outnumbers both positive and negative sentiment. CONCLUSION: Prevalent Sentiment has a big influence on how people react to the media and what they say, especially as people utilize social media platforms more and more. Slight disinformation and/or indoctrination can quickly turn a neutral opinion into a negative one.