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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
“Data from the social Web is different: networks and text, not tables and numbers, are the rule, and familiar query languages are replaced with rapidly evolving web service APIs. 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
Matthew Russell, Vice President of Engineering 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).
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