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Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More

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Mine the rich data tucked away in popular social websites such as Twitter, Facebook, LinkedIn, 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
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At the height of its power in the summer of 1942, Hitlerite Germany had enslaved four hundred million people; its empire stretched from the Mediterranean to the Arctic, from the English Channel to the Black Sea and almost to the Caspian. Between the Ukrainian steppes and the Pyrenees only Switzerland remained free. Even Mussolini, whom Hitler once...
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