- Paperback: 448 pages
- Publisher: O'Reilly Media; 2 edition (October 20, 2013)
- Language: English
- ISBN-10: 1449367615
- ISBN-13: 978-1449367619
- Product Dimensions: 7 x 0.9 x 9.2 inches
- Shipping Weight: 2 pounds (View shipping rates and policies)
- Average Customer Review: 4.4 out of 5 stars See all reviews (61 customer reviews)
- Amazon Best Sellers Rank: #279,322 in Books (See Top 100 in Books)
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Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More 2nd Edition
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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 is as simple as installing Virtual Box, installing Vagrant, cloning the 2nd edition’s Github archive, and typing “vagrant up.” You can execute the examples for yourself in the virtual machine; modify them; and use the virtual machine for your own projects, since it’s a fully functional Linux system with Python, Java, MongoDB, and other necessities pre-installed. You can view this as a book with accompanying examples in a particularly nice package, or you can view the book as “premium support” for an open source project that consists of the examples and the VM.
If you want to engage with the data that’s surrounding you, Mining the Social Web is the best place to start. Use it to learn, to experiment, and to build your own data projects.
-- Mike Loukides
Vice President of Content Strategy for O'Reilly Media, Inc.
Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More
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Top Customer Reviews
Mining the Social Web is exceptionally well written covering all major social media platforms. Mr. Russell is also very approachable and answers questions very quickly.
I really can't say enough good things about this book and how it sets the bar high for future technical books!
The intro alone is more than worth the cost of admission - the easy installation of a VM saved me hours of dev ops headache I would have had to endure in my Python explorations. And it just gets better from there - everything is set up for the reader's convenience - just hit Ctrl-Enter and you're revealing the secrets of LinkedIn or Twitter or whatever.
The author clearly poured a lot of effort into his project, and it shows: this book sets a new standard for technical books (at least any technical books I've seen). If you have the slightest interest in the topic, check it out.
I also had the pleasure of interviewing Matthew Russell about this book on The Data Skeptic Podcast where we had the chance to have an interesting discussion about the book.
i think this book is quite useful for those try to conduct the text mining for commercial or marketing survey on cyber data from company facebook or twitter. Highly recommend it.
An addendum - SUPER fast support.
PRO: The extensive links to online references and resources are helpful. Plus, the API introductions and sample code snippets provided are done nicely with minimal errors.
CON: If you are a knowledgeable user of interpretive languages and current development tools, you'll find the book useful. If it's been a while since you generated code, you'll spend more than a few cycles bridging what's in the book versus what the author assumes you already know.