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 tweets are displayed at a given time, and new tweets are continuously retrieved for new visualizations.
The size of the text and the amount of sideways movement depend on how subjective the tweet is. Tweets with positive sentiments travel towards the top, and those with negative sentiments move towards the bottom. If sentiment of a tweet is very negative (score of less than -0.5) then there is added noise to the generated visualization, making the tweets less readable.
The server is written in python with Flask and uses socket.io to pass on data to the web client. Text visualizations are generated by extracting points of font data of the text of the tweets using Bezmerizing.
Created by Jiwon Shin.