Bibliographic Information

Mining the social web

Matthew A. Russell and Mikhail Klassen

O'Reilly, 2018, c2019

3rd ed

Other Title

Mining the social web : data mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and more

Available at  / 6 libraries

Search this Book/Journal

Note

Includes index

Description and Table of Contents

Description

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

by "Nielsen BookData"

Details

  • NCID
    BB27633111
  • ISBN
    • 9781491985045
  • Country Code
    cc
  • Title Language Code
    eng
  • Text Language Code
    eng
  • Place of Publication
    Beijing
  • Pages/Volumes
    xxiv, 400 p.
  • Size
    24 cm
  • Classification
  • Subject Headings
Page Top