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Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More (häftad)
Häftad (Paperback)
Antal sidor
2 Rev ed
black & white illustrations
230 x 180 x 30 mm
736 g
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Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More (häftad)

Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More

Data Mining from Facebook, Twitter, and Linkedin With Python

Häftad Engelska, 2013-10-15
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Facebook, Twitter, LinkedIn, Google+, and other social web properties generate a wealth of valuable social data, but how can you tap into this data and discover whos connecting with whom, which insights are lurking just beneath the surface, and what people are talking about? This book shows you how to answer these questions and many more. Each chapter combines popular and useful social web data with analysis techniques and visualization to help you find the needles in the social haystack that you've been looking for-as well as many you probably didn't even know existed.

In this expanded and thoroughly revised second edition youll learn how to:

  • Navigate the most popular social web APIs to access, collect, analyze, and visualize social web data
  • Employ IPython Notebook and other easy to use Python packages such as the Natural Language Toolkit, NetworkX, and Matplotlib to efficiently sift through social web data as part of an experimentally-driven approach to discovering insights in social web data
  • Apply advanced text-mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detection to human language data that you'll encounter all over the web
  • Bootstrap interest graphs by discovering latent affinities between people, programming languages, and coding projects from GitHub data
  • Visualize social web data with D3, a state-of-the-art HTML5 and JavaScript toolkit
The book's source code is maintained in a GitHub repository maintained by the author and can be deployed as turn-key virtual machine with each chapter's source code presented in an interactive and easy to use IPython Notebook format. No complex third-party installations or advanced Python knowledge is required to get the most out of this book.

All the code and most recent updates to the code can be found at github:
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Recensioner i media

Mining the social web, again When we first published "Mining the Social Web," I thought it was one of the most important books I worked on that year. Now that we're publishing a second edition (which I didn't work on), I find that I agree with myself. With this new edition, "Mining the Social Web" is more important than ever. While we're seeing more and more cynicism about the value of data, and particularly "big data," that cynicism isn't shared by most people who actually work with data. Data has undoubtedly been overhyped and oversold, but the best way to arm yourself against the hype machine is to start working with data yourself, to find out what you can and can't learn. And there's no shortage of data around. Everything we do leaves a cloud of data behind it: Twitter, Facebook, Google+ -- to say nothing of the thousands of other social sites out there, such as Pinterest, Yelp, Foursquare, you name it. Google is doing a great job of mining your data for value. Why shouldn't you? There are few better ways to learn about mining social data than by starting with Twitter; Twitter is really a ready-made laboratory for the new data scientist. And this book is without a doubt the best and most thorough approach to mining Twitter data out there. But that's only a starting point. We hear a lot in the press about sentiment analysis and mining unstructured text data; this book shows you how to do it. If you need to mine the data in web pages or email archives, this book shows you how. And if you want to understand how to people collaborate on projects, "Mining the Social Web" is the only place I've seen that analyzes GitHub data. All of the examples in the book are available on Github. In addition to the example code, which is bundled into IPython notebooks, Matthew has provided a VirtualBox VM that installs Python, all the libraries you need to run the examples, the examples themselves, and an IPython server. Checking out the examples isa

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Övrig information

Matthew Russell, Chief Technology Officer at Digital Reasoning Systems ( and Principal at Zaffra (, is a computer scientist who is passionate about data mining, open source, and web application technologies. He's also the author of Dojo: The Definitive Guide (O'Reilly)