Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social... at Dominant Systems Book Store

Facebook, Twitter, and LinkedIn generate a tremendous amount of valuable social data, but how can you find out who's making connections with social media, what they’re talking about, or where they’re located? This concise and practical book shows you how to answer these questions and more. You'll learn how to combine social web data, analysis techniques, and visualization to help you find what you've been looking for in the social haystack, as well as useful information you didn't know existed.

Each standalone chapter introduces techniques for mining data in different areas of the social Web, including blogs and email. All you need to get started is a programming background and a willingness to learn basic Python tools.

Get a straightforward synopsis of the social web landscape
Use adaptable scripts on GitHub to harvest data from social network APIs such as Twitter, Facebook, and LinkedIn
Learn how to employ easy-to-use Python tools to slice and dice the data you collect
Explore social connections in microformats with the XHTML Friends Network
Apply advanced mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detection
Build interactive visualizations with web technologies based upon HTML5 and JavaScript toolkits

"Let Matthew Russell serve as your guide to working with social data sets old (email, blogs) and new (Twitter, LinkedIn, Facebook). Mining the Social Web is a natural successor to Programming Collective Intelligence: a practical, hands-on approach to hacking on data from the social Web with Python."

--Jeff Hammerbacher, Chief Scientist, Cloudera

"A rich, compact, useful, practical introduction to a galaxy of tools, techniques, and theories for exploring structured and unstructured data."

--Alex Martelli, Senior Staff Engineer, Google

Customer Reviews & Comments
This book covers a lot of ground. It's, at times, a bit vertiginous in the amount of subjects and technologies it touches per chapter, and is not always easy to follow. It can also introduce so many interesting things that, by the time you finished becoming familiar with all of them, after wandering for hours on the web, jumping from interesting technology to interesting technology, you may have forgotten what took you to these places and wonder where you were in the book. Time spent reading it is, however, time very well spent. When you finish it, you will have at least a cursory familiarity with tools like OAuth, CouchDB, Redis, MapReduce, NumPy (and the Python programming language, albeit it will help you a lot if you know your way around Python before you start the book), Graphviz, SIMILE widgets, NLTK, various service APIs and data formats, and will be well equipped to explore those rich datasets on your own. The chapters are well compartmentalized and it's easy to pick chapters to read according to your needs. I know that, when I face the problems they tackle, I will do exactly that. If you do any kind of analysis and visualization of social-generated data that's on the web, this book is a good pick. Even if your datasets are not from the web, you may find the parts on analysis and visualization very interesting.

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