Coronavirus (COVID-19) has been the central topic of discussion since early 2020 and has drastically changed our lives. Browsing through Twitter for the latest updates of the situation has highlighted the fact that the top tweets that appear at the top or the most popular retweets often have political motives. Rarely do individuals' tweets appear as the popular tweets when searching through Twitter and I wanted to create an artistic experiment by visualizing recent tweets filtered by #coronavirus and generate them by mapping the subjectiveness and polarity (positive and negative sentiment) of the tweet to various elements of the visualizations.

PROJECT TYPE: Solo project


CODE: github

TECHNICAL DETAILS: This project uses Tweepy to stream recent tweets filtered by #coronavirus and uses TextBlob for sentiment analysis to generate the visualizations of the tweets. 3 to 4 tweets are displayed at a given time, and new tweets are continuously retrieved for new visualizations. The server is written in python with Flask and uses to pass on data (points of the font data for text, raw tweet text, sentiment analysis score) to the web client. Text visualizations are generated by extracting points of font data of the text of the tweets using Bezmerizing.