Mining the Social Web: Finding Needles in the Social by Matthew A. Russell

By Matthew A. Russell

Fb, Twitter, and LinkedIn generate an enormous volume of precious social info, yet how are you going to discover who's making connections with social media, what they’re speaking approximately, or the place they’re situated? This concise and functional publication indicates you ways to respond to those questions and extra. You'll tips on how to mix social net info, research options, and visualization that will help you locate what you've been trying to find within the social haystack, in addition to beneficial info you didn't be aware of existed.

every one standalone bankruptcy introduces innovations for mining info in several parts of the social net, together with blogs and e mail. All you must start is a programming historical past and a willingness to profit uncomplicated Python instruments.

* Get an easy synopsis of the social net panorama
* Use adaptable scripts on GitHub to reap information from social community APIs similar to Twitter, fb, and LinkedIn
* how you can hire easy-to-use Python instruments to slice and cube the information you acquire
* discover social connections in microformats with the XHTML buddies community
* observe complicated mining ideas reminiscent of TF-IDF, cosine similarity, collocation research, record summarization, and clique detection
* construct interactive visualizations with internet applied sciences dependent upon HTML5 and JavaScript toolkits

"Data from the social internet is various: networks and textual content, now not tables and numbers, are the rule of thumb, and popular question languages are changed with swiftly evolving net carrier APIs. enable Matthew Russell function your advisor to operating with social information units previous (email, blogs) and new (Twitter, LinkedIn, Facebook). Mining the Social net is a common successor to Programming Collective Intelligence: a realistic, hands-on method of hacking on info from the social net with Python." --Jeff Hammerbacher

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STORE IMAGE AND STORE POSITIONS FOR GROCERY RETAIL CHAINS: 35 To interpret the dimensions of Figure 1 we need additional information. Figure 1 only shows the projections of the attribute points onto the plane but does not indicate which attribute have had the most impact in determining the orientation of the dimensions. Table 3b provides the numerical results of the correspondence analysis for the set of attribute points. e. inertia. The relative contributions to inertia quantify the importance of each attribute point in determining the direction of the principal dimension, and serve as guides to the interpretation of each dimension.

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