(This repo is meant to be an example for Big Data club presentations)
Big data involves storing and analyzing difficult-to-work with data sets, either in scale or in lack of structure. Over the semester, we'll look at technologies and algorithms that will allow us to do both on some interesting data sets.
We will start with doing sentiment analysis on data collected from the Twitter streaming API. Among many other things, the Twitter API lets you search for tweets matching certain keywords. Sentiment analysis lets us analyze what Twitter users think about the topics of their tweets.
After this, we will be able to answer questions like "What do people think about {Nexus 5, the election, Miley Cyrus, Facebook, etc}?"
Using pip,
pip install -r requirements.txt
You may need sudo permissions, unless you are using virtualenv.
The project comes with sample data sets you can analyze. The file sentiments.py is used to analyze downloaded
tweets in json format. Run it with
python sentiments.py data/atl.json
to view "interesting" tweets and the average sentiment of a sample of tweets about the Falcons - 49ers game.
You can also download more data with hashtag_downloader.py, which streams data from the search criteria to
standard out.
Run like
python hashtag_downloader.py <topic>
where topic can be anything you're interested in or that people might write interesting things about.
HOWEVER The downloader requires you to have Twitter API keys to stream the data, and we can't make these public on the internet... so get your own!
- Register your Twitter account with http://dev.twitter.com
- Log in to dev.twitter.com and go to "My applications" (hover over your avatar)
- Create a new app (gt-big-data, for instance)
- Create access tokens in the new app.
Then use the information from this app (gt-big-data) to fill out an api_keys.py
The python file hashtag_downloader.py is expecting is a api_keys.py file with these four lines:
KEY='<consumer key>'
SECRET='<consumer secret>'
TOKEN= '<access (oath) token>'
TOKEN_SECRET= '<access (oath) token secret>'
from your Twitter app page. To generate access tokens, you'll need to create them within the Twitter app (there's a big button for it.)
- Machine learning is used heavily with sentiment analysis. Look into NLTK to see far more sophistocated algorithms for sentiment analysis beyond the rudimentary one used here.
- Use the Twitter API to see how sentiment about a topic varies by location.