挖掘社交网络(第2版,影印版)
Matthew A. Russel
出版时间:2014年12月
页数:448
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。

· 利用IPython Notebook、Natural Language Toolkit、NetworkX和其他一些科学计算工具来挖掘常见的社交网络
· 应用高级文本挖掘技术,比如clustering和TF-IDF,来从人类语言数据中提取有意义的信息
· 通过发现人、编程语言和编码项目之间的紧密联系来从Github中获得兴趣图
· 通过D3.jsp这个非常灵活的HTML5和JavaScript工具箱来搭建互动的可视化结果
· 利用二十多个Twitter方案,它们以O’Reilly的流行“问题/解决方案/讨论”手册格式来呈现

这本独特的数据科学书籍中的代码样例都在一个公共的GitHub资源库中被维护。它们很容易通过一组易于使用的IPython Notebook集合在虚拟机的协助下进行互动学习。
  1. A Guided Tour of the Social Web
  2. Prelude
  3. Chapter 1: Mining Twitter: Exploring Trending Topics, Discovering What People Are Talking About, and More
  4. Overview
  5. Why Is Twitter All the Rage?
  6. Exploring Twitter's API
  7. Analyzing the 140 Characters
  8. Closing Remarks
  9. Recommended Exercises
  10. Online Resources
  11. Chapter 2: Mining Facebook: Analyzing Fan Pages, Examining Friendships, and More
  12. Overview
  13. Exploring Facebook's Social Graph API
  14. Analyzing Social Graph Connections
  15. Closing Remarks
  16. Recommended Exercises
  17. Online Resources
  18. Chapter 3: Mining LinkedIn: Faceting Job Titles, Clustering Colleagues, and More
  19. Overview
  20. Exploring the LinkedIn API
  21. Crash Course on Clustering Data
  22. Closing Remarks
  23. Recommended Exercises
  24. Online Resources
  25. Chapter 4: Mining Google+: Computing Document Similarity, Extracting Collocations, and More
  26. Overview
  27. Exploring the Google+ API
  28. A Whiz-Bang Introduction to TF-IDF
  29. Querying Human Language Data with TF-IDF
  30. Closing Remarks
  31. Recommended Exercises
  32. Online Resources
  33. Chapter 5: Mining Web Pages: Using Natural Language Processing to Understand Human Language, Summarize Blog Posts, and More
  34. Overview
  35. Scraping, Parsing, and Crawling the Web
  36. Discovering Semantics by Decoding Syntax
  37. Entity-Centric Analysis: A Paradigm Shift
  38. Quality of Analytics for Processing Human Language Data
  39. Closing Remarks
  40. Recommended Exercises
  41. Online Resources
  42. Chapter 6: Mining Mailboxes: Analyzing Who's Talking to Whom About What, How Often, and More
  43. Overview
  44. Obtaining and Processing a Mail Corpus
  45. Analyzing the Enron Corpus
  46. Discovering and Visualizing Time-Series Trends
  47. Analyzing Your Own Mail Data
  48. Closing Remarks
  49. Recommended Exercises
  50. Online Resources
  51. Chapter 7: Mining GitHub: Inspecting Software Collaboration Habits, Building Interest Graphs, and More
  52. Overview
  53. Exploring GitHub's API
  54. Modeling Data with Property Graphs
  55. Analyzing GitHub Interest Graphs
  56. Closing Remarks
  57. Recommended Exercises
  58. Online Resources
  59. Chapter 8: Mining the Semantically Marked-Up Web: Extracting Microformats, Inferencing over RDF, and More
  60. Overview
  61. Microformats: Easy-to-Implement Metadata
  62. From Semantic Markup to Semantic Web: A Brief Interlude
  63. The Semantic Web: An Evolutionary Revolution
  64. Closing Remarks
  65. Recommended Exercises
  66. Online Resources
  67. Twitter Cookbook
  68. Chapter 9: Twitter Cookbook
  69. Accessing Twitter's API for Development Purposes
  70. Doing the OAuth Dance to Access Twitter’s API for Production Purposes
  71. Discovering the Trending Topics
  72. Searching for Tweets
  73. Constructing Convenient Function Calls
  74. Saving and Restoring JSON Data with Text Files
  75. Saving and Accessing JSON Data with MongoDB
  76. Sampling the Twitter Firehose with the Streaming API
  77. Collecting Time-Series Data
  78. Extracting Tweet Entities
  79. Finding the Most Popular Tweets in a Collection of Tweets
  80. Finding the Most Popular Tweet Entities in a Collection of Tweets
  81. Tabulating Frequency Analysis
  82. Finding Users Who Have Retweeted a Status
  83. Extracting a Retweet’s Attribution
  84. Making Robust Twitter Requests
  85. Resolving User Profile Information
  86. Extracting Tweet Entities from Arbitrary Text
  87. Getting All Friends or Followers for a User
  88. Analyzing a User’s Friends and Followers
  89. Harvesting a User’s Tweets
  90. Crawling a Friendship Graph
  91. Analyzing Tweet Content
  92. Summarizing Link Targets
  93. Analyzing a User’s Favorite Tweets
  94. Closing Remarks
  95. Recommended Exercises
  96. Online Resources
  97. Appendixes
  98. Appendix: Information About This Book's Virtual Machine Experience
  99. Appendix: OAuth Primer
  100. Overview
  101. Appendix: Python and IPython Notebook Tips & Tricks
  102. Colophon
书名:挖掘社交网络(第2版,影印版)
作者:Matthew A. Russel
国内出版社:东南大学出版社
出版时间:2014年12月
页数:448
书号:978-7-5641-5005-1
原版书书名:Mining the Social Web, 2nd Edition
原版书出版商:O'Reilly Media
Matthew A. Russel
 
Matthew Russell, Chief Technology Officer at Digital Reasoning, Principal at Zaffra, and author of several books on technology including Mining the Social Web (O'Reilly, 2013), now in its second edition. He is passionate about open source software development, data mining, and creating technology to amplify human intelligence. Matthew studied computer science and jumped out of airplanes at the United States Air Force Academy. When not solving hard problems, he enjoys practicing Bikram Hot Yoga, CrossFitting and participating in triathlons.
 
 
The animal on the cover of Mining the Social Webis a groundhog (Marmota monax), also known as awoodchuck (a name derived from the Algonquin namewuchak). Groundhogs are famously associated with theUS/Canadian holiday Groundhog Day, held every February 2nd. Folklore holdsthat if the groundhog emerges from its burrow that day and sees its shadow,winter will continue for six more weeks. Proponents say that the rodentsforecast accurately 75 to 90 percent of the time. Many cities host famousgroundhog weather prognosticators, including Punxsutawney Phil (ofPunxsutawney, Pennsylvania, and the 1993 Bill Murray filmGroundhog Day). This legend perhaps originates from the fact that the groundhog is oneof the few species that enters true hibernation during the winter. Primarilyherbivorous, groundhogs will fatten up in the summer on vegetation, berries,nuts, insects, and the crops in human gardens, causing many people toconsider them pests. They then dig a winter burrow, and remain there fromOctober to March (although they may emerge earlier in temperate areas, or,presumably, if they will be the center of attention on their eponymousholiday). The groundhog is the largest member of the squirrel family, around16–26 inches long and weighing 4–9 pounds. It is equipped with curved, thickclaws ideal for digging, and two coats of fur: a dense grey undercoat and alighter-colored topcoat of longer hairs, which provides protection againstthe elements. Groundhogs range throughout most of Canada and northern regions of theUnited States, in places where open space and woodlands meet. They arecapable of climbing trees and swimming but are usually found on the ground,not far from the burrows they dig for sleeping, rearing their young, andseeking protection from predators. These burrows typically have two to fiveentrances, and up to 46 feet of tunnels.