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Mining the Social Web : Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More by Mikhail Klassen and Matthew A. Russell (2019, Trade Paperback)

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About this product

Product Identifiers

PublisherO'reilly Media, Incorporated
ISBN-101491985046
ISBN-139781491985045
eBay Product ID (ePID)237690225

Product Key Features

Number of Pages432 Pages
Publication NameMining the Social Web : Data Mining Facebook, Twitter, Linkedin, Instagram, Github, and more
LanguageEnglish
Publication Year2019
SubjectProgramming / General, Systems Architecture / Distributed Systems & Computing, Web / Social Media, Databases / Data Mining
TypeTextbook
AuthorMikhail Klassen, Matthew A. Russell
Subject AreaComputers
FormatTrade Paperback

Dimensions

Item Height0.9 in
Item Weight25.4 Oz
Item Length9.1 in
Item Width7.3 in

Additional Product Features

Edition Number3
Intended AudienceScholarly & Professional
LCCN2020-277838
IllustratedYes
SynopsisMine 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
LC Classification NumberQA76.9.D343

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