SENTIMENTAL ANALYSIS OF COVID 19 VACCINATION ON TWITTER DATA

Badrikanath Praharaj

Abstract:

Delving into the Twitterverse, where millions of voices converge in the succinct language of tweets, this
study harnesses the power of sentiment analysis and natural language processing to unveil the public's
stance on COVID-19 vaccinations.
With a treasure trove of over ten thousand tweets spanning from January to April 2021, our research
journey begins. By employing cutting-edge techniques like counting and text summarization, we
uncover the prevailing themes within this sea of tweets and transform them into digestible narratives.
But what makes this study truly groundbreaking is its real-time applicability. This analytical pipeline,
custom-crafted for Twitter's fast-paced world, offers healthcare experts a unique window into public
reactions to health interventions as they unfold.
To paint this sentiment-driven landscape, we kick-start with Twitter's API and authentication tokens,
sifting through raw tweets with NLP wizardry. Then, the magic of sentiment analysis steps in,
categorizing tweets as positive, negative, or neutral, enabling us to gauge the collective emotional pulse.
From these, we unearth the key themes, summarizing them for a bird's-eye view of the overarching
conversations. It's like painting a masterpiece, one stroke at a time, except our canvas is a million tweets,
and our brush is an algorithm.
In a nutshell, this study is a deep dive into the world of tweets, translating them into insights, and offering
a real-time radar for the health community to navigate the turbulent waters of public opinion.