Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More, 3/e (Paperback)

Matthew A. Russell, Mikhail Klassen



Mine the rich data tucked away in popular social websites such as Twitter, Facebook, LinkedIn, Google+, and Instagram. With the third edition of this popular guide, data scientists, analysts, and programmers will learn how to glean insights from social media—including who’s connecting with whom, what they’re talking about, and where they’re located—using Python code examples, Jupyter notebooks, or Docker containers.

In part one, each standalone chapter focuses on one aspect of the social landscape, including each of the major social sites, as well as web pages, blogs and feeds, mailboxes, GitHub, and a newly added chapter covering Instagram. Part two provides a cookbook with two dozen bite-size recipes for solving particular issues with Twitter.

  • Get a straightforward synopsis of the social web landscape
  • Use Docker to easily run each chapter’s example code, packaged as a Jupyter notebook
  • Adapt and contribute to the code’s open source GitHub repository
  • Learn how to employ best-in-class Python 3 tools to slice and dice the data you collect
  • Apply advanced mining techniques such as TFIDF, cosine similarity, collocation analysis, clique detection, and image recognition
  • Build beautiful data visualizations with Python and JavaScript toolkits




- 獲取社交網絡景觀的簡明概述
- 使用Docker輕鬆運行每個章節的示例代碼,以Jupyter筆記本的形式打包
- 適應並貢獻代碼的開源GitHub存儲庫
- 學習如何使用最佳Python 3工具來切割和分析收集到的數據
- 應用高級挖掘技術,如TFIDF、餘弦相似度、共現分析、圈子檢測和圖像識別
- 使用Python和JavaScript工具包建立美麗的數據可視化