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Twitter Visualization Demo


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.

Quick Setup Instructions

  • 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 '' from the 'scripts' folder (can take a while on a slower machine)
  • Open a terminal window and start a local Redis server ('bash')
  • 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')
  • Browse to the app at ''