소장자료
LDR | 02191nam a2200325 a 4500 | ||
001 | 0092273032▲ | ||
005 | 20180520233622▲ | ||
007 | ta▲ | ||
008 | 131115t20132014cc a b 001 0 eng d▲ | ||
015 | ▼aGBB371961▼2bnb▲ | ||
020 | ▼a9781449367619 (pbk.)▲ | ||
020 | ▼a1449367615 (pbk.)▲ | ||
020 | ▼a9781449370459 (ebook)▲ | ||
020 | ▼a1449370454 (ebook)▲ | ||
035 | ▼a(KERIS)REQ000030049107▲ | ||
040 | ▼a247017▲ | ||
050 | 4 | ▼aQA76.9.D343▼bR87 2013▲ | |
082 | 0 | 4 | ▼a006.312▼221▲ |
090 | ▼a006.312▼bR965m2▲ | ||
100 | 1 | ▼aRussell, Matthew A.,▼c(Computer scientist),▼eauthor.▲ | |
245 | 1 | 0 | ▼aMining the social web /▼cMatthew A. Russell.▲ |
250 | ▼a2nd ed.▲ | ||
260 | ▼aBeijing ; ▼aCambridge [Mass.] :▼bO'Reilly,▼c2013.▲ | ||
300 | ▼axxiv, 421 p. :▼bill. ;▼c23 cm.▲ | ||
500 | ▼aPrevious edition: 2011.▲ | ||
504 | ▼aIncludes bibliographical references and index.▲ | ||
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 | 0 | ▼aData mining.▲ | |
650 | 0 | ▼aOnline social networks.▲ | |
999 | ▼c장화옥▲ |
Mining the social web
자료유형
국외단행본
서명/책임사항
Mining the social web / Matthew A. Russell.
개인저자
Russell, Matthew A. , (Computer scientist)
판사항
2nd ed.
발행사항
Beijing ; Cambridge [Mass.] : O'Reilly , 2013.
형태사항
xxiv, 421 p. : ill. ; 23 cm.
일반주기
Previous edition: 2011.
서지주기
Includes bibliographical references and index.
요약주기
"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."
ISBN
9781449367619 (pbk.) 1449367615 (pbk.) 9781449370459 (ebook) 1449370454 (ebook)
청구기호
006.312 R965m2
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