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    Mining the Social Web (Paperback) By (author) Matthew Russell

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    DescriptionHow can you tap into the wealth of social web data to discover who's making connections with whom, what they're talking about, and where they're located? With this expanded and thoroughly revised edition, you'll learn how to acquire, analyze, and summarize data from all corners of the social web, including Facebook, Twitter, LinkedIn, Google+, GitHub, email, websites, and blogs. Employ IPython Notebook, the Natural Language Toolkit, NetworkX, and other scientific computing tools to mine popular social web sites Apply advanced text-mining techniques, such as clustering and TF-IDF, to extract meaning from human language data Bootstrap interest graphs from GitHub by discovering affinities among people, programming languages, and coding projects Build interactive visualizations with D3.js, an extraordinarily flexible HTML5 and JavaScript toolkit Take advantage of more than two-dozen Twitter recipes, presented in O'Reilly's popular "problem/solution/discussion" cookbook format The example code for this unique data science book is maintained in a public GitHub repository. It's designed to be easily accessible through a turnkey virtual machine that facilitates interactive learning with an easy-to-use collection of IPython Notebooks.


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    Title
    Mining the Social Web
    Authors and contributors
    By (author) Matthew Russell
    Physical properties
    Format: Paperback
    Number of pages: 448
    Width: 178 mm
    Height: 231 mm
    Thickness: 25 mm
    Weight: 748 g
    Language
    English
    ISBN
    ISBN 13: 9781449367619
    ISBN 10: 1449367615
    Classifications

    BIC E4L: COM
    Nielsen BookScan Product Class 3: S10.2
    B&T Book Type: NF
    B&T Modifier: Region of Publication: 01
    B&T Modifier: Subject Development: 20
    DC22: 006.312
    B&T Modifier: Academic Level: 03
    B&T Modifier: Text Format: 01
    B&T Merchandise Category: COM
    Ingram Subject Code: XB
    LC subject heading:
    BISAC V2.8: COM051230, COM079000
    Warengruppen-Systematik des deutschen Buchhandels: 16360
    LC subject heading:
    BISAC V2.8: COM060140, COM051000
    LC subject heading:
    B&T General Subject: 233
    LC subject heading:
    BISAC V2.8: COM060160
    LC subject heading:
    BISAC V2.8: COM021030
    DC22: 006.3/12
    BIC subject category V2: UNF
    Libri: DATA5000
    BISAC V2.8: COM051360
    DC23: 006.312
    LC classification: QA76.9.D343 R87 2013
    Thema V1.0: UBJ, UMX, UNF, UDBS
    Edition
    2, Revised
    Edition statement
    2nd Revised edition
    Publisher
    O'Reilly Media, Inc, USA
    Imprint name
    O'Reilly Media, Inc, USA
    Publication date
    20 October 2013
    Publication City/Country
    Sebastopol
    Author Information
    Matthew Russell, Chief Technology Officer at Digital Reasoning Systems (http://www.digitalreasoning.com/) and Principal at Zaffra (http://zaffra.com), 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)
    Review quote
    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 isr