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Mining the Social Web: Data Mining Facebook, Twitter, Linkedin, Google+, Github, and More

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Mining the Social Web: Data Mining Facebook, Twitter, Linkedin, Google+, Github, and More Cover

 

Synopses & Reviews

Publisher Comments:

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:

https://github.com/ptwobrussell/Mining-​the-Social-Web-2nd-Edition

Synopsis:

How can you tap into the wealth of social web data to discover whos making connections with whom, what theyre talking about, and where theyre located? With this expanded and thoroughly revised edition, youll 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 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 OReillys popular "problem/solution/discussion" cookbook format

The example code for this unique data science book is maintained in a public GitHub repository. Its designed to be easily accessible through a turnkey virtual machine that facilitates interactive learning with an easy-to-use collection of IPython Notebooks.

Synopsis:

Facebook, Twitter, LinkedIn, and Google+ generate a tremendous amount of valuable social data, but how can you find out whos connecting with who, what theyre talking about, what friends they have in common, or where theyre located? This book shows you how to answer these questions and more.

Youll learn how to combine social web data, analysis techniques, and visualization to help you find what youve 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.

In this expanded second edition youll learn how to:

  • Create interest graphs for people by using GitHubs rich set of APIs to mine social networks
  • Explore Wikipedia contributions to build a social network of people who are interested in (or have expertise with) certain topics
  • Learn how to employ easy-to-use Python tools to slice and dice the data you collect
  • Get a straightforward synopsis of the social web landscape
  • 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

All you need to get started is a programming background and a willingness to learn basic Python tools.

About the Author

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. Hes also the author of Dojo: The Definitive Guide (OReilly).

Table of Contents

Preface A Guided Tour of the Social Web Prelude Chapter 1: Mining Twitter: Exploring Trending Topics, Discovering What People Are Talking About, and More Chapter 2: Mining Facebook: Analyzing Fan Pages, Examining Friendships, and More Chapter 3: Mining LinkedIn: Faceting Job Titles, Clustering Colleagues, and More Chapter 4: Mining Google+: Computing Document Similarity, Extracting Collocations, and More Chapter 5: Mining Web Pages: Using Natural Language Processing to Understand Human Language, Summarize Blog Posts, and More Chapter 6: Mining Mailboxes: Analyzing Who's Talking to Whom About What, How Often, and More Chapter 7: Mining GitHub: Inspecting Software Collaboration Habits, Building Interest Graphs, and More Chapter 8: Mining the Semantically Marked-Up Web: Extracting Microformats, Inferencing over RDF, and More Twitter Cookbook Chapter 9: Twitter Cookbook Appendixes Information About This Book's Virtual Machine Experience OAuth Primer Python and IPython Notebook Tips and Tricks Colophon

Product Details

ISBN:
9781449367619
Author:
Russell, Matthew A
Publisher:
O'Reilly Media
Author:
Russell, Matthew A.
Subject:
big data,;data analytics;data mining;data science;facebook;github;google+;linkedin;python;social media;twitter
Subject:
big data;data;data analysis;data analytics;data mining;data science;facebook;github;google+;linkedin;python;social media;twitter
Copyright:
Edition Description:
Second Edition
Publication Date:
20131031
Binding:
TRADE PAPER
Language:
English
Pages:
448
Dimensions:
9.19 x 7 in

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Related Subjects

Computers and Internet » Internet » Marketing
Computers and Internet » Internet » Web » Social Networking
Computers and Internet » Internet » Web » Web Programming
Computers and Internet » Software Engineering » General
Computers and Internet » Software Engineering » Programming and Languages
Science and Mathematics » Mathematics » General

Mining the Social Web: Data Mining Facebook, Twitter, Linkedin, Google+, Github, and More New Trade Paper
0 stars - 0 reviews
$44.99 In Stock
Product details 448 pages O'Reilly Media - English 9781449367619 Reviews:
"Synopsis" by ,

How can you tap into the wealth of social web data to discover whos making connections with whom, what theyre talking about, and where theyre located? With this expanded and thoroughly revised edition, youll 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 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 OReillys popular "problem/solution/discussion" cookbook format

The example code for this unique data science book is maintained in a public GitHub repository. Its designed to be easily accessible through a turnkey virtual machine that facilitates interactive learning with an easy-to-use collection of IPython Notebooks.

"Synopsis" by ,

Facebook, Twitter, LinkedIn, and Google+ generate a tremendous amount of valuable social data, but how can you find out whos connecting with who, what theyre talking about, what friends they have in common, or where theyre located? This book shows you how to answer these questions and more.

Youll learn how to combine social web data, analysis techniques, and visualization to help you find what youve 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.

In this expanded second edition youll learn how to:

  • Create interest graphs for people by using GitHubs rich set of APIs to mine social networks
  • Explore Wikipedia contributions to build a social network of people who are interested in (or have expertise with) certain topics
  • Learn how to employ easy-to-use Python tools to slice and dice the data you collect
  • Get a straightforward synopsis of the social web landscape
  • 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

All you need to get started is a programming background and a willingness to learn basic Python tools.

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