A Systematic View of Sentiment Analysis on Different Techniques, Challenges, and Future Directions in COVID-19

Abstract

In the last few years, social networking sites have exploded at a rapid pace. Sentiment analysis (SA) is a way of mining data and reading text using natural language processing(NLP) In today's era, corona virus is a hot issue and pandemic all over the world. Millions of citizens use blogs or microblogging tools to communicate their viewpoints. Twitter is one of the handiest platforms and most popular social media for users to share their thoughts and ideas. Through blogging sites, the public can share their feelings and opinions such as “panic, anger, misery,” and it can be divided the text into three categories: positive, negative, and neutral. Sentiment analysis is employed in a variety of industries, including healthcare, finance, sports, politics, hospitality, and tourism. According to a comparison of ten primary studies, Naive Bayes (NB) and support vector machine (SVM) are widely used algorithms for sentiment analysis in COVID-19.