000			01918nam a22002297a 4500
003			OSt
005			20140711150603.0
008			140711b xxu||||| |||| 00| 0 eng d
020			_a978-93-5110-311-0
040			_cENGLISH
082			_22nd. _a006.312/RUS
100			_aRUSSELL,MATTHEW A.
245			_aMINING SOCIAL WEB:DATA MINING,FACE BOOK,TWITTER,LINKEDIN,GOOGLE+,GITHUB,AND MORE
260			_aMUMBAI _bSPD _c2013
300			_axxiv,421
365			_2RUPEES _b695.00
366			_2TECHNICAL BOOK SERVICES _f20%
520			_aHow 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.
530			_aWEB/RACK
650			_aDATA MINING,SOCIAL WEB,FACE BOOK,TWITTER,LINKEDIN,GOOGLE+,GITHUB,AND MORE
942			_2ddc _cB
999			_c247155 _d247155