This project leverages a variety of technologies to visualize a real-time stream of twitter data using sentiment analysis and word vector space mapping via word2vec. It runs on a local Flask instance with a Redis/Celery back-end and uses Socket-IO to push events to the web client. Twitter integration is managed by the pattern library. NVD3 is used on the front-end to create the visualization.

• Clone this repository
• Install dependencies (Flask, Flask-SocketIO, Redis, Celery, Pattern, Numpy, Pandas, Scikit-learn, Gensim)
• Download twitter sentiment dataset here
• Unzip the .csv file and save a copy in the 'scripts' folder
• Run 'build_models.py' from the 'scripts' folder (can take a while on a slower machine)
• Open a terminal window and start a local Redis server ('bash redis.sh')
• Open another terminal window and start a Celery worker ('celery worker -A app.celery --loglevel=info')
• Open a third terminal window and start the Flask application ('python app.py')
• Browse to the app at 'http://127.0.0.1:5000/'