Realtime Twitter Sentiment Analyzer Dashboard

1. Requirements

• python 2.7
• python2.7-dev
• mongodb server
• Flask

2. Installation & Setup apt-get install python2.7 python2.7-dev mongodb-server

Download and install required libs and data

python setup.py develop
python toolbox/setup-app.py

3. Data Collection The base of data training is an assumption that tweets with happy emoticons :) are positive and tweets with sad :( emoticons have negative sentiment polarity

Collect 2000 'happy' tweets

python toolbox/collect-tweets.py happy 2000

Collect 2000 'sad' tweets

python toolbox/collect-tweets.py sad 2000

4. Train classifier Create and save new classifier trained from collected tweets

   python toolbox/train-classifier.py bayes 1000

5. Start server stack open 3 shells and type in each:

   python start-collector.py

   python start-classifier.py

   python start-server.py

open browser on http://127.0.0.1:5000

6. ToDo Run everything behind nginx>=1.3.13, automate processes management with supervisor.

Since nginx 1.3.13 supports websockets, so you should probably use latest stable version.

This is only one way of many to deploy the app.