  At the peak of the COVID-19 pandemic, numerous countries worldwide sought to
mobilize vaccination campaigns in an attempt to curb the spread and number of
deaths caused by the virus. One avenue in which information regarding COVID
vaccinations is propagated is that of scientific articles, which provide a
certain level of credibility regarding this. Hence, this increases the
probability that people who view these articles would get vaccinated if the
articles convey a positive message on vaccinations and conversely decreases the
probability of vaccinations if the articles convey a negative message. This
being said, this study aims to investigate the correlation between article
sentiments and the corresponding increase or decrease in vaccinations in the
United States. To do this, a lexicon-based sentiment analysis was performed in
two steps: first, article content was scraped via a Python library called
BeautifulSoup, and second, VADER was used to obtain the sentiment analysis
scores for each article based on the scraped text content. Results suggest that
there was a relatively weak correlation between the average sentiment score of
articles and the corresponding increase or decrease in COVID vaccination rates
in the US.
