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Mining the social web / Matthew A. Russell and Mikhail Klassen.

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Available copies

  • 1 of 1 copy available at Evergreen Indiana.

Current holds

0 current holds with 1 total copy.

Location Call Number / Copy Notes Barcode Shelving Location Status Due Date
Eckhart PL - Main 006.754 RUS (Text) 840191002697905 Adult Nonfiction - Upper Level Available -

Record details

  • ISBN: 9781491985045
  • ISBN: 1491985046
  • Physical Description: xxiv, 400 pages : illustrations ; 24 cm
  • Edition: Third edition.
  • Publisher: Sebastopol, CA : O'Reilly Media, 2018.

Content descriptions

Bibliography, etc. Note:
Includes bibliographical references and index.
Formatted Contents Note:
Part 1. A guided tour of the social web. Mining Twitter : exploring trending topics, discovering what people are talking about, and more ; Mining Facebook : analyzing fan pages, examining friendships, and more ; Mining Instagram : computer vision, neural networks, object recognition, and face detection ; Mining LinkedIn : faceting job titles, clustering colleagues, and more ; Mining text files : computing document similarity, extracting collocations, and more ; Mining web pages : using natural language processing to understand human language, summarize blog posts, and more ; Mining mailboxes : analyzing who's talking to whom about what, how often, and more ; Mining GitHub : inspecting software collaboration habits, building interest graphs, and more -- Part II. Twitter cookbook -- Part III. Appendixes. A. Information about this book's virtual machine experience ; B. OAuth primer ; C. Python and Jupyter notebook tips and tricks.
Summary, etc.:
"Mine the rich data tucked away in popular social websites like Twitter, Facebook, LinkedIn, Instagram, and GitHub. With the third edition of this popular guide, data scientists, analysts, and programmers will learn how to glean insights from social media--who's connecting with whom, what they're talking about, and where they're located--using Python code examples, Jupyter notebooks, or Docker containers."--Back cover.
Subject: Data mining.
Online social networks.
COMPUTERS > Databases > Data Mining.
COMPUTERS > Programming Languages > Python.
COMPUTERS > Social Aspects.
COMPUTERS > Software Development & Engineering.
COMPUTERS > Web > Social Media.

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