Mining the social web
By: Russell, Matthew A
Title By: Klassen, Mikhail
Material type: BookPublisher: Beijing : O'Reilly, c2019.Edition: 3rd ed.Description: xxiv, 400 p. : ill. ; 24 cm.ISBN: 9781491985045Other title: Data mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and more.Subject(s): Data mining | Online social networksDDC classification: 006.312 RU MI Online resources: Location MapItem type | Home library | Call number | Status | Notes | Date due | Barcode | Item holds |
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REGULAR | University of Wollongong in Dubai Main Collection | 006.312 RU MI (Browse shelf) | Available | March2019 | T0061917 |
, Shelving location: Main Collection Close shelf browser
006.312 RE CO Recommendation and search in social networks | 006.312 RO DA Data mining with decision trees : | 006.312 RO DA Data science and analytics with Python | 006.312 RU MI Mining the social web | 006.312 TA IN Introduction to data mining / | 006.312 TA IN Introduction to data mining / | 006.312 TA IN Introduction to data mining / |
"Revision history for the third edition"--Title page verso.
Includes bibliographical references and index.
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 -- Twitter cookbook -- Appendixes.
"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.