挖掘社交网络-第2版-(影印版)_PDF下载[32MB-百度云]罗塞尔

本书特色

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你应该如何利用丰富的社交网络数据来发现任意两个人之间的连接, 他们所交流的话题, 以及他们在哪儿？通过本次扩展和彻底的修订, 你将学习到如何获取、分析和总结来自于社交网络每个角落的数据, 包括facebook、twitter、linkedin、google+、github、电子邮件、网站和博客。

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preface xiipart i. a guided tour of the social webprelude !　1. mining twitter: exploring trending topics, discovering what people are talking about,　　and more !　　1.1. overview　　1.2. why is twitter all the rage?　　1.3. exploring twitter’s api　　1.3.1. fundamental twitter terminology　　1.3.2. creating a twitter api connection　　1.3.3. exploring trending topics　　1.3.4. searching for tweets　　1.4. analyzing the 140 characters　　1.4.1. extracting tweet entities　　1.4.2. analyzing tweets and tweet entities with frequency analysis　　1.4.3. computing the lexical diversity of tweets　　1.4.4. examining patterns in retweets　　1.4.5. visualizing frequency data with histograms　　1.5. closing remarks　　1.6. recommended exercises　　1.7. online resources　2. mining facebook: analyzing fan pages, examining friendships, and more　　2.1. overview　　2.2. exploring facebook’s social graph api　　2.2.1. understanding the social graph api　　2.2.2. understanding the open graph protocol　　2.3. analyzing social graph connections　　2.3.1. analyzing facebook pages　　2.3.2. examining friendships　　2.4. closing remarks　　2.5. recommended exercises　　2.6. online resources　3. mining tinkedln: faceting job litles, clustering colleagues, and more　　3.1. overview　　3.2. exploring the linkedln api　　3.2.1. making linkedln api requests　　3.2.2. downloading linkedln connections as a csv file　　3.3. crash course on clustering data　　3.3.1. clustering enhances user experiences　　3.3.2. normalizing data to enable analysis　　3.3.3. measuring similarity　　3.3.4. clustering algorithms　　3.4. closing remarks　　3.5. recommended exercises　　3.6. online resources　4. mining google+: computing document similarity, extracting collocations, and　　4.1. overview　　4.2. exploring the google+ api　　4.2.1. making google+ api requests　　4.3. a whiz-bang introduction to tf-idf　　4.3.1. term frequency　　4.3.2. inverse document frequency　　4.3.3. tf-idf　　4.4. querying human language data with tf-idf　　4.4.1. introducing the natural language toolkit　　4.4.2. applying tf-idf to human language　　4.4.3. finding similar documents　　4.4.4. analyzing bigrams in human language　　4.4.5. reflections on analyzing human language data　　4.5. closing remarks　　4.6. recommended exercises　　4.7. online resources　5. mining web pages: using natural language processing to understand humad　　language, summarize blog posts, and more……　　5.1. overview partll twitter cookbookpartll appendixesa informarion about book’s virtual machine experienceb oauth primerc python and ipython notebook tips @ tricksindex