Mining the Social Web: Analyzing Data From Facebook, Twitter, Linkedin, And Other Social Media Sites by Matthew A. RussellMining the Social Web: Analyzing Data From Facebook, Twitter, Linkedin, And Other Social Media Sites by Matthew A. Russell

Mining the Social Web: Analyzing Data From Facebook, Twitter, Linkedin, And Other Social Media Sites

byMatthew A. Russell

Paperback | February 11, 2011

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Want to tap the tremendous amount of valuable social data in Facebook, Twitter, LinkedIn, and Google+? This refreshed edition helps you discover who’s making connections with social media, what they’re talking about, and where they’re located. You’ll learn how to combine social web data, analysis techniques, and visualization to 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, LinkedIn, and Google+
  • 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

"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

Matthew Russell, Chief Technology Officer at Digital Reasoning, Principal at Zaffra, and author of several books on technology including Mining the Social Web (O'Reilly, 2013), now in its second edition. He is passionate about open source software development, data mining, and creating technology to amplify human intelligence. Matthew ...
Title:Mining the Social Web: Analyzing Data From Facebook, Twitter, Linkedin, And Other Social Media SitesFormat:PaperbackDimensions:356 pages, 9.19 × 7 × 0.9 inPublished:February 11, 2011Publisher:O'Reilly MediaLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:1449388345

ISBN - 13:9781449388348


Rated 5 out of 5 by from Easy to read. I tore though it Some basic programming ability is a must for this book, as the first page starts with installing the Python development tools. If you don't know Python, that is okay since all the code is easy to follow. Everything you need to develop and run the examples is described step by step with clear instructions at every point. Once you get comfortable with the basics, the author quickly moves from topic to topic, giving a good introduction into many aspects of how to mine data and generate useful conclusions. Some of the examples include accessing your feed with OAuth, processing feeds to determine influence, using set-wise opeations with redis to determine which of your friends are also followers, storing data in CouchDB, using map-reduce to determine the most popular mentions and topics, natural language processing, and seeing data with various visualization tools. And that was just for Twitter. The book continues on with examples of processing mailboxes, LinkedIn, Google Buzz, blogs, Facebook, and the Semantic Web. The examples show how easy it is to gather and analyze data from all these social web sites. With a good breadth of coverage, I recommend this book for anyone wanting to learn to process and visualize large amounts of data, either from the social web or any other data source.
Date published: 2011-03-08

Table of Contents

Preface; Content Updates; To Read This Book?; Or Not to Read This Book?; Tools and Prerequisites; Conventions Used in This Book; Using Code Examples; Safari™ Books Online; How to Contact Us; Acknowledgments; Chapter 1: Introduction: Hacking on Twitter Data; 1.1 Installing Python Development Tools; 1.2 Collecting and Manipulating Twitter Data; 1.3 Closing Remarks; Chapter 2: Microformats: Semantic Markup and Common Sense Collide; 2.1 XFN and Friends; 2.2 Exploring Social Connections with XFN; 2.3 Geocoordinates: A Common Thread for Just About Anything; 2.4 Slicing and Dicing Recipes (for the Health of It); 2.5 Collecting Restaurant Reviews; 2.6 Summary; Chapter 3: Mailboxes: Oldies but Goodies; 3.1 mbox: The Quick and Dirty on Unix Mailboxes; 3.2 mbox + CouchDB = Relaxed Email Analysis; 3.3 Threading Together Conversations; 3.4 Visualizing Mail "Events" with SIMILE Timeline; 3.5 Analyzing Your Own Mail Data; 3.6 Closing Remarks; Chapter 4: Twitter: Friends, Followers, and Setwise Operations; 4.1 RESTful and OAuth-Cladded APIs; 4.2 A Lean, Mean Data-Collecting Machine; 4.3 Constructing Friendship Graphs; 4.4 Summary; Chapter 5: Twitter: The Tweet, the Whole Tweet, and Nothing but the Tweet; 5.1 Pen : Sword :: Tweet : Machine Gun (?!?); 5.2 Analyzing Tweets (One Entity at a Time); 5.3 Juxtaposing Latent Social Networks (or #JustinBieber Versus #TeaParty); 5.4 Visualizing Tons of Tweets; 5.5 Closing Remarks; Chapter 6: LinkedIn: Clustering Your Professional Network for Fun (and Profit?); 6.1 Motivation for Clustering; 6.2 Clustering Contacts by Job Title; 6.3 Fetching Extended Profile Information; 6.4 Geographically Clustering Your Network; 6.5 Closing Remarks; Chapter 7: Google+: TF-IDF, Cosine Similarity, and Collocations; 7.1 Harvesting Google+ Data; 7.2 Data Hacking with NLTK; 7.3 Text Mining Fundamentals; 7.4 Finding Similar Documents; 7.5 Bigram Analysis; 7.6 Tapping into Your Gmail; 7.7 Before You Go Off and Try to Build a Search Engine...; 7.8 Closing Remarks; Chapter 8: Blogs et al.: Natural Language Processing (and Beyond); 8.1 NLP: A Pareto-Like Introduction; 8.2 A Typical NLP Pipeline with NLTK; 8.3 Sentence Detection in Blogs with NLTK; 8.4 Summarizing Documents; 8.5 Entity-Centric Analysis: A Deeper Understanding of the Data; 8.6 Closing Remarks; Chapter 9: Facebook: The All-in-One Wonder; 9.1 Tapping into Your Social Network Data; 9.2 Visualizing Facebook Data; 9.3 Closing Remarks; Chapter 10: The Semantic Web: A Cocktail Discussion; 10.1 An Evolutionary Revolution?; 10.2 Man Cannot Live on Facts Alone; 10.3 Hope; Colophon;