Twitter Sentiment Analysis on Worldwide COVID-19 Outbreaks

TLDR

Social media data has grown rapidly, providing researchers with real‑time information on events such as the worldwide spread of COVID‑19, which is widely discussed by individuals, media, and governments. The study collected tweets containing the hashtags “COVID‑19” and “coronavirus” between 9 and 15 April 2020 using Tweepy, performed sentiment analysis with TextBlob, and visualized the results. The analysis produced a visualized sentiment overview of the collected tweets, illustrating public opinion during the study period.

Abstract

In the past two decades, the growth of social data on the web has rapidly increased. This leads to researchers to access the data and information for many academic research and commercial uses. Social data on the web contains many real life events that occurred in daily life, today the global COVID-19 disease is spread worldwide. Many individuals including media organizations and government agencies are presenting the latest news and opinions regarding the coronavirus. In this study, the twitter data has been pulled out from Twitter social media, through python programming language, using Tweepy library, then by using TextBlob library in python, the sentiment analysis operation has been done. After the measuring sentiment analysis, the graphical representation has been provided on the data. The data we have collected on twitter are based on two specified hashtag keywords, which are (“COVID-19, coronavirus”). The date of searching data is seven days from 09-04-2020 to 15-04-2020. In the end a visualized presentation regarding the results and further explanation are provided.