coviD3.js is a web application specializing in telling stories through beautiful and engaging data visualizations. The application analyzes the ways the COVID-19 pandemic affects society outside of the hospital. Currently we have data visualizations of the changes in media sentiment and transportation due to the COVID-19 pandemic. I created this project with two fellow students: Eric Lau and Raymond Lee.

The application is split into four sections: dashboard, sentiment analysis, transportation, and numbers. The dashboard uses the D3.js and the Covid Python package to display live statistics regarding COVID-19 in the United States. The sentiment analysis section uses D3.js, spaCy for natural language processing, and TextBlob for sentiment analysis to analyze data from public media and tweets from the President of the United States. The transportation section analyzes and displays data from the New York City MTA with D3.js. The numbers section displays fascinating statistics regarding COVID-19.

Demo Link

The application is live at https://covid3js.solonedu.com/
A video demo of the application can be found at https://youtu.be/EcRPQK6-89Q

Link to Code

python -m venv hero
# replace hero with anything you want
# If the above does not work, run with

How I built it

The application was created with HTML, CSS, JavaScript, D3.js, Python, Flask, and the following Python packages: spaCy, TextBlob, and Covid. We used the GitHub Student Developer Pack on our project. This was mainly to utilize the DigitalOcean credit required to host our application on a Droplet with Apache and Flask. My entire team was new to using D3.js along with sentiment analysis packages, so we had to spend some time to learn how to use them individually, and in conjunction. This was also our first project analyzing large datasets. We learned a lot about data analysis and sentiment analysis.