Mining the Social Web: Unlocking the Data within Facebook, Twitter and Other Sites

Description

Facebook, Twitter, and LinkedIn generate a tremendous amount of valuable social data, but how can you find out who's making connections with social media, what they’re talking about, or where they’re located? This concise and practical book shows you how to answer these questions and more. You'll learn how to combine social web data, analysis techniques, and visualization to help you 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, and LinkedIn
* 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

"Let Matthew Russell serve as your guide to working with social data sets old (email, blogs) and new (Twitter, LinkedIn, Facebook). Mining the Social Web is a natural successor to Programming Collective Intelligence: a practical, hands-on approach to hacking on data from the social Web with Python."

 --Jeff Hammerbacher, Chief Scientist, Cloudera

 "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

CONTENTS:

Preface; To Read This Book?; Or Not to Read This Book?; Tools and Prerequisites; Conventions Used in This Book; Using Code Examples; Safari(R) 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 Buzz: TF-IDF, Cosine Similarity, and Collocations; 7.1 Buzz = Twitter + Blogs (???); 7.2 Data Hacking with NLTK; 7.3 Text Mining Fundamentals; 7.4 Finding Similar Documents; 7.5 Buzzing on Bigrams; 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;