“21 Recipes for Mining Twitter” by Matthew Russell provides readers with a problem-oriented crash course in using Python and freely available third party Python packages to mine social data from Twitter. It assumes familiarity with Python and makes quick progress through extracting and using different types of user and streaming data available from the Twitter API via the twitter package in particular.
I like the general approach of calling out problems to be solved, then addressing them one by one with a “recipe” for each. Some might complain that this approach results in disjointedness from one recipe to the next, but in fact that’s a feature, not a bug. “21 Recipes for Mining Twitter” is actually a spin-off of Russell’s more in depth “Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites” (also an O’Reilly book). The latter shows many more examples, and not just for mining Twitter, but also for harvesting and analyzing social data from many other services and APIs as well. So if you need down-and-dirty recipes for Twitter alone, get this book, and if you need more blanks filled in for Twitter mining and/or information on accessing data from other social services, get the other book. Or heck, get them both!
This book is recommended for: Anyone already familiar with programming who is looking to solve specific problems using Twitter data. It’s a slam dunk for Python programmers.
Recommended with reservations for: Non-programmers interested in social data analysis, with the caveat that they will probably need to spend some time working through the Python.org tutorial or getting up to speed with Python elsewhere before they can make great progress with this book.
Disclaimer: I know the author and have worked with him in various capacities on other projects, but not this book. Even if I didn’t know him, however, I’d still love and make use of this book.