Based on Coursera's Introduction to Data Science Project 1
Used to fetch live stream data from twitter.
Requires oauth2, which is not part of the EnThought Python library.
Open the program and replace access_token_key, access_token_secret, consumer_key, and consumer_secret with the appropriate values. Then run
python twitterstream.py To get credentials:
- Create a twitter account if you do not already have one.
- Go to https://dev.twitter.com/apps and log in with your twitter credentials.
- Click "create an application"
- Fill out the form and agree to the terms. Put in a dummy website if you don't have one you want to use.
- On the next page, scroll down and click "Create my access token"
- Copy your "Consumer key" and your "Consumer secret" into twitterstream.py
- Click "Create my access token." You can read more about Oauth authorization.
- Open twitterstream.py and set the variables corresponding to the consumer key, consumer secret, access token, and access secret
access_token_key = ""
access_token_secret = ""
consumer_key = ""
consumer_secret = ""
- Run the following and make sure you see data flowing.
python twitterstream.py To get sufficient data and run any of the py files, pipe the output into a text file, like
python twitterstream.py > output.txt , and make sure that you let it run for at least 10-15 minutes.
$ python tweet_sentiment.py <sentiment_file> <tweet_file>
to print to stdout the sentiment of each tweet you retrieve in your tweet_file. The sentiment_file is AFINN-111.txt
$ python term_sentiment.py <sentiment_file> <tweet_file>
to derive the sentiment for new terms that wasn't provided in the sentiment file
$ python frequency.py <tweet_file>
to compute term's frequency
$ python happiest_state.py <sentiment_file> <tweet_file>
to determine the happiest state, based on the number of tweets collected in tweet_file
$ python top_ten.py <tweet_file>
to find the top ten hash tags in the number of tweets collected