000			02513nam a22002057a 4500
020			_a9781449367619
020			_a9789351103110
082			_a004.69 _bRUS
100			_aRussell, Matthew A.
245			_aMining the social web
250			_aSecond edition
260			_aMumbai : _bShroff Publishers, _c2014
300			_axxiv, 421 pages : _billustrations ;
500			_aIncludes index
505			_a1. Mining twitter: exploring trending topics, discovering what people are talking about, and more -- 2. Mining facebook: analyzing fan pages, examining friendships, and more -- 3. Mining linkdln: faceting job titles, clustering colleagues, and more -- 4. Mining google+: computing documents similarity, extracting collocations, and more -- 5. Mining web pages: Using natural language processing to understand human language, summarize blog posts, and more -- 6. Mining mailboxes: analyzing who's talking to whom about what, how often, and more -- 7. Mining github: inspecting software collaboration habits, building interest graphs, and more -- 8. Mining the semantically marked-up web: extracting microformats, inferencing over RDF, and more -- 9. Twitter cookbook.
520			_a"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."
650			_aData mining.
650			_aOnline social networks.
942			_cBK
999			_c44312 _d44312