Welcome to our new series, 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.
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:
In our next post, Josh Montague (@jrmontag) will show you will take us through an analysis of people who Tweeted about the 2017 Cannes film festival. He will walk you through:
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.
People commonly use Twitter data to identify various trends. In this next example in our series, Aaron Gonzales (@binary_aaron) will introduce an overview of methods for working with Twitter data as a time series. We’ll begin by looking at the volume of Tweets that discuss Taylor Swift in 2017, and discuss the following:
As in our previous posts, 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.