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Tips

Do more with Twitter data: Finding the right data

By Aaron Gonzales
Tuesday, 20 February 2018

Welcome to our new series, Do More with Twitter Data, where our data scientists work through examples and share their learnings and tips for getting the most out of Twitter data using Twitter APIs. Each post in the series will center around a real-life project and provides MIT-licensed code that you can use to bootstrap your projects with our enterprise and premium APIs.

Twitter is what's happening and what people are talking about right now. Hundreds of millions of Tweets are sent on the platform each day. I represent a group of data scientists on the Twitter Data team who want to help people and businesses do more with this vast amount of data in less time. In this spirit, we are starting a series of tutorials that aim to help people work more effectively with Twitter data. In our first post, Fiona Pigott (@notFromShrek) will show you how to get the Tweets most related to the question, “What do people talk about when they fly?” She will walk you through:

  • Getting Tweets using our search APIs
  • Filtering and refining rules to improve the quality of your Tweet sample
  • Working with Tweet payload elements (parsing Tweets, tokenization of text, etc.), and
  • Basic natural-language processing with Twitter data.

The example has code in Python and uses Python tools that our group has developed, but the concepts and workflows are language agnostic and can be implemented in other languages.

Please go here to see the full example, and if you want to run it yourself, the repo is on Github.

Would you like to be notified when we launch the next post in this series? Sign up to receive an alert.

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