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] 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
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