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 "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 How can you tap into the wealth of social web data to discover who’s making connections with whom, what they’re talking about, and where they’re located? With this expanded and thoroughly revised edition, you’ll 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 sitesApply advanced text-mining techniques, such as clustering and TF-IDF, to extract meaning from human language dataBootstrap interest graphs from GitHub by discovering affinities among people, programming languages, and coding projectsBuild interactive visualizations with D3.js, an extraordinarily flexible HTML5 and JavaScript toolkitTake advantage of more than two-dozen Twitter recipes, presented in O’Reilly’s popular 'problem/solution/discussion' cookbook formatThe example code for this unique data science book is maintained in a public GitHub repository. It’s designed to be easily accessible through a turnkey virtual machine that facilitates interactive learning with an easy-to-use collection of IPython Notebooks. " How can you tap into the wealth of social web data to discover who's making connections with whom, what they're talking about, and where they're located? With this expanded and thoroughly revised edition, you'll 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 O'Reilly's popular "problem/solution/discussion" cookbook format The example code for this unique data science book is maintained in a public GitHub repository. It's designed to be easily accessible through a turnkey virtual machine that facilitates interactive learning with an easy-to-use collection of IPython Notebooks."--Amazon.com 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 who’s 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鈥檝e been looking for—as well as many you probably didn鈥檛 even know existed. COMPUTERS / Web / Social Networking
