Do more with Twitter data: Understanding the people behind the Tweet

By Aaron Gonzales

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. You can see the first post in this series here.

I am excited to share the next example in our series, in which Josh Montague (@jrmontag) will take us through an analysis of people who Tweeted about the 2017 Cannes film festival. He will walk you through:

  • Getting Tweets via our Search APIs
  • Working with User-level attributes from Tweet payloads
  • Natural-language processing (NLP) and feature engineering
  • Building and refining clustering models
  • Techniques for model inspection and visualization

As in our previous post, the example is written in Python, but the techniques are language agnostic and can be implemented readily in other languages with good data and machine-learning library support.

Please go here to see the full example, or if you’d like to run it locally, the repo is available on Github.

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