By Matthew A. Russell
How are you able to faucet into the wealth of social internet information to find who’s making connections with whom, what they’re conversing approximately, and the place they’re situated? With this elevated and punctiliously revised variation, you’ll how to gather, learn, and summarize facts from all corners of the social internet, together with fb, Twitter, LinkedIn, Google+, GitHub, e-mail, web pages, and blogs.
• hire the usual Language Toolkit, NetworkX, and different clinical computing instruments to mine well known social websites
• observe complicated text-mining options, similar to clustering and TF-IDF, to extract that means from human language information
• Bootstrap curiosity graphs from GitHub via getting to know affinities between humans, programming languages, and coding initiatives
• construct interactive visualizations with D3.js, a very versatile HTML5 and JavaScript toolkit
• benefit from greater than two-dozen Twitter recipes, provided in O’Reilly’s renowned "problem/solution/discussion" cookbook structure
the instance code for this targeted facts technological know-how booklet is maintained in a public GitHub repository. It’s designed to be simply available via a turnkey digital computing device that allows interactive studying with an easy-to-use selection of IPython Notebooks.
Read or Download Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More (2nd Edition) PDF
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Additional info for Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More (2nd Edition)
Example text
After the training, at the generalization or test phase, the output from a machine o = fa (x, w) is expected to be ‘a good’ estimate of a system’s true response y. separation function, in a classification. The chosen hypothesis fa (x, w) belongs to a hypothesis space of functions H(fa ∈ H), and it is a function that minimizes some risk functional R(w). It may be practical to remind the reader that under the general name ‘approximating function’ we understand any mathematical structure that maps inputs x into outputs y.
N ]T , H denotes the Hessian matrix (Hij = yi yj K(xi , xj )) of this problem, and p is an (n,1) unit vector p = 1 = [1 1 . . 1]T . Note that the Hessian matrix is a dense n by n matrix. As a result, the amount of the computer memory required to solve the optimization problem is n2 . This is why the next part of the book is focused on solving the problem in an iterative way. ). This fact is also used extensively in next chapter for deriving faster iterative learning algorithm for SVMs. 3.
Note also that the number of unknown variables equals the number of training data n. After learning, the number of free parameters is equal to the number of SVs but it does not depend on the dimensionality of input space. t. 16b) i = 1, . . 16c) αi ≥ 0, where α = [α1 , α2 , . . , αn ]T , H denotes a symmetric Hessian matrix (with elements Hij = yi yj xTi xj ), and p is an n × 1 unit vector p = 1 = [1 1 . . 1]T . 2 Support Vector Machines in Classification and Regression 29 matrix has a size of n by n and it is always a dense matrix.