Unmasking the Sentiments of People Towards Pandemic: Twitter Sentiment Analysis in RealTime

Social media provides a wealth of user-generated data, including ratings and comments on various causes, products, diseases, and public policies. A new field of text mining called sentiment analysis uses a variety of techniques to filter out people's moods and emotions. The World Health Organization (WHO) has declared COVID-19 a pandemic, and people worldwide are fighting for their lives. As a result, people experience various physical and mental problems such as fear, anxiety, irritability, and unhappiness. This study uses sentiment analysis to examine how individuals feel about the COVID-19 epidemic affecting Indians. Tweets were collected from January 2020 to March 2020. Data have been extracted from Twitter using TweepyAPI, and Numpy, Pandas, and Matplotlib perform analysis based on subjectivity and polarity. Through an automated system, we analyzed the tweets and categorized them into three categories: positive, negative, and neutral. From our analysis, we discovered that initially, people started putting negative tweets, but over time, people's sentiments changed to positive and neutral comments. The results from the study concluded that initially, the situation was terrible and tragic, but with time, people were able to handle the situation. They got accustomed to a new lifestyle following measures to prevent infection from the COVID-19 virus.

Keywords: COVID-19, Polarity, Subjectivity, Sentimental analysis, Social media.