Examples in this project are based on the Twitter assignment in Coursera "Introduction to Data Science" MOOC (https://www.coursera.org/course/datasci)

• 1k_tweets.txt
• AFINN-111.txt
• AFINN-README.txt
• oauth.cfg
• sample_oauth.cfg
• search_twitter.py
• stream_twetter.py
• term_sentiment.py
• tweet_sentiment.py
• happiest_state.py
• SimpleOauth.py

1. On Windows use Python 2.7.5 Windows Installer - http://www.python.org/ftp/python/2.7.5/python-2.7.5.msi
2. Install easy_install.exe by running https://pypi.python.org/packages/2.7/s/setuptools/setuptools-0.6c11.win32-py2.7.exe#md5=57e1e64f6b7c7f1d2eddfc9746bbaf20
3. Install Oauth module by running "c:\Python27\Scripts\easy_install.exe oauth2"

1. Register on http://dev.twitter.com
2. Create Twitter Application
3. Create Access Token for your Twitter Application
4. Copy sample_oauth.cfg to oauth.cfg and copy Consumer key, Consumer secret, Access token, and Access token Secret values from your Twitter Application to oauth.cfg

• API v1.1 is the current version. It only supports JSON and requires Oauth 1.0a.
• API v1.0 has been retired on June 11, 2013. It was a simpler API in that it didn't require authentication.

• "From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series" - http://www.cs.cmu.edu/~rbalasub/publications/oconnor_balasubramanyan_routledge_smith.icwsm2010.tweets_to_polls.pdf
• "Twitter mood predicts the stock market" - http://www.relevantdata.com/pdfs/IUStudy.pdf
• http://www.laurentluce.com/posts/twitter-sentiment-analysis-using-python-and-nltk/

• Free and Open Textbook on Statistics - http://www.openintro.org/stat/
• Course on Natrual Language Processing - https://www.coursera.org/course/nlp

• Google's Python Class - https://developers.google.com/edu/python
• Code Academy Python - http://www.codecademy.com/tracks/python