• Erschienen: 08.02.2011
  • Herausgeber: O'Reilly
  • Seiten: 354,

  • Dieses Buch ist nicht direkt auf Amazon verfügbar.

Mining the Social Web

Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites

von Matthew A. Russell

Beschreibung vom Herausgeber

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

Aus dem Inhaltsverzeichnis

  1. Introduction: Hacking on Twitter Data
  2. Microformats: Semantic Markup and Common Sense Collide
  3. Mailboxes: Oldies but Goodies
  4. Twitter: Friends, Followers, and Setwise Operations
  5. Twitter: The Tweet, the Whole Tweet, and Nothing but the Tweet
  6. LinkedIn: Clustering Your Professional Network for Fun (and Profit?)
  7. Google Buzz: TF-IDF, Cosine Similarity, and Collocations
  8. Blogs et al.: Natural Language Processing (and Beyond)
  9. Facebook: The All-in-One Wonder
  10. The Semantic Web: A Cocktail Discussion