Mining the Social Web

Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More

Nonfiction, Computers, Database Management, Programming, Internet
Cover of the book Mining the Social Web by Matthew A. Russell, Mikhail Klassen, O'Reilly Media
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Author: Matthew A. Russell, Mikhail Klassen ISBN: 9781491973509
Publisher: O'Reilly Media Publication: December 4, 2018
Imprint: O'Reilly Media Language: English
Author: Matthew A. Russell, Mikhail Klassen
ISBN: 9781491973509
Publisher: O'Reilly Media
Publication: December 4, 2018
Imprint: O'Reilly Media
Language: English

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
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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