Twitter Sentiment Analysis of Covid Vacciness
This work addresses the need to monitor public opinion on vaccines for public health stakeholders, but it appears incremental as it applies existing NLP methods to a new dataset.
The researchers tackled the problem of analyzing public sentiment about COVID-19 vaccines from Twitter data, using natural language processing techniques to categorize opinions with high accuracy.
In this paper, we look at a database of tweets sorted by various keywords that could indicate the users sentiment towards covid vaccines. With social media becoming such a prevalent source of opinion, sorting and ranking tweets that hold important information such as opinions on covid vaccines is of utmost importance. Two different ranking scales were used, and ranking a tweet in this way could represent the difference between an opinion being lost and an opinion being featured on the site, which affects the decisions and behavior of people, and why researchers were interested in it. Using natural language processing techniques, our aim is to determine and categorize opinions about covid vaccines with the highest accuracy possible.