Twitter is a popular social network where users can share short SMS-like messages called tweets. Users share thoughts, links and pictures on Twitter, journalists comment on live events, companies promote products and engage with customers. The list of different ways to use Twitter could be really long, and with 500 millions of tweets per day, there’s a lot of data to analyse and to play with.
This is the first in a series of articles dedicated to mining data on Twitter using Python. In this first part, we’ll see different options to collect data from Twitter. Once we have built a data set, in the next episodes we’ll discuss some interesting data applications.
Update July 2016: my new book on data mining for Social Media is out! Part of the content in this tutorial has been improved and expanded as part of the book, so please have a look. Chapter 2 about mining Twitter is available as a free sample from the publisher’s web site, and the companion code with many more examples is available on my GitHub
Table of Contents of this tutorial:
- Part 1: Collecting Data (this article)
- Part 2: Text Pre-processing
- Part 3: Term Frequencies
- Part 4: Rugby and Term Co-Occurrences
- Part 5: Data Visualisation Basics
- Part 6: Sentiment Analysis Basics
- Part 7: Geolocation and Interactive Maps
More updates: fixed version number of Tweepy to avoid problem with Python 3; fixed discussion on _json to get the JSON representation of a tweet; added example of process_or_store().
Register Your App
In order to have access to Twitter data programmatically, we need to create an app that interacts with the Twitter API.
The first step is the registration of your app. In particular, you need to point your browser to http://apps.twitter.com, log-in to Twitter (if you’re not already logged in) and register a new application. You can now choose a name and a description for your app (for example “Mining Demo” or similar). You will receive a consumer key and a consumer secret: these are application settings that should always be kept private. From the configuration page of your app, you can also require an access token and an access token secret. Similarly to the consumer keys, these strings must also be kept private: they provide the application access to Twitter on behalf of your account. The default permissions are read-only, which is all we need in our case, but if you decide to change your permission to provide writing features in your app, you must negotiate a new access token.
Important Note: there are rate limits in the use of the Twitter API, as well as limitations in case you want to provide a downloadable data-set, see:
Accessing the Data
Twitter provides REST APIs you can use to interact with their service. There is also a bunch of Python-based clients out there that we can use without re-inventing the wheel. In particular, Tweepy in one of the most interesting and straightforward to use, so let’s install it:
pip install tweepy==3.3.0
Update: the release 3.4.0 of Tweepy has introduced a problem with Python 3, currently fixed on github but not yet available with pip, for this reason we’re using version 3.3.0 until a new release is available.
More Updates: the release 3.5.0 of Tweepy, already available via pip, seems to solve the problem with Python 3 mentioned above.
In order to authorise our app to access Twitter on our behalf, we need to use the OAuth interface:
import tweepy from tweepy import OAuthHandler consumer_key = 'YOUR-CONSUMER-KEY' consumer_secret = 'YOUR-CONSUMER-SECRET' access_token = 'YOUR-ACCESS-TOKEN' access_secret = 'YOUR-ACCESS-SECRET' auth = OAuthHandler(consumer_key, consumer_secret) auth.set_access_token(access_token, access_secret) api = tweepy.API(auth)
The api variable is now our entry point for most of the operations we can perform with Twitter.
For example, we can read our own timeline (i.e. our Twitter homepage) with:
for status in tweepy.Cursor(api.home_timeline).items(10): # Process a single status print(status.text)
Tweepy provides the convenient Cursor interface to iterate through different types of objects. In the example above we’re using 10 to limit the number of tweets we’re reading, but we can of course access more. The status variable is an instance of the Status() class, a nice wrapper to access the data. The JSON response from the Twitter API is available in the attribute _json (with a leading underscore), which is not the raw JSON string, but a dictionary.
So the code above can be re-written to process/store the JSON:
for status in tweepy.Cursor(api.home_timeline).items(10): # Process a single status process_or_store(status._json)
What if we want to have a list of all our followers? There you go:
for friend in tweepy.Cursor(api.friends).items(): process_or_store(friend._json)
And how about a list of all our tweets? Simple:
for tweet in tweepy.Cursor(api.user_timeline).items(): process_or_store(tweet._json)
In this way we can easily collect tweets (and more) and store them in the original JSON format, fairly easy to convert into different data models depending on our storage (many NoSQL technologies provide some bulk import feature).
The function process_or_store() is a place-holder for your custom implementation. In the simplest form, you could just print out the JSON, one tweet per line:
def process_or_store(tweet): print(json.dumps(tweet))
In case we want to “keep the connection open”, and gather all the upcoming tweets about a particular event, the streaming API is what we need. We need to extend the StreamListener() to customise the way we process the incoming data. A working example that gathers all the new tweets with the #python hashtag:
from tweepy import Stream from tweepy.streaming import StreamListener class MyListener(StreamListener): def on_data(self, data): try: with open('python.json', 'a') as f: f.write(data) return True except BaseException as e: print("Error on_data: %s" % str(e)) return True def on_error(self, status): print(status) return True twitter_stream = Stream(auth, MyListener()) twitter_stream.filter(track=['#python'])
Depending on the search term, we can gather tons of tweets within a few minutes. This is especially true for live events with a world-wide coverage (World Cups, Super Bowls, Academy Awards, you name it), so keep an eye on the JSON file to understand how fast it grows and consider how many tweets you might need for your tests. The above script will save each tweet on a new line, so you can use the command wc -l python.json from a Unix shell to know how many tweets you’ve gathered.
You can see a minimal working example of the Twitter Stream API in the following Gist:
We have introduced tweepy as a tool to access Twitter data in a fairly easy way with Python. There are different types of data we can collect, with the obvious focus on the “tweet” object.
Once we have collected some data, the possibilities in terms of analytics applications are endless. In the next episodes, we’ll discuss some options.
Table of Contents of this tutorial: