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Mining the social web /
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 th...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, CA :
O'Reilly Media,
2018.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Russell, Matthew A. |c (Computer scientist), |e author. | |
| 245 | 1 | 0 | |a Mining the social web / |c Matthew A. Russell and Mikhail Klassen. |
| 264 | 1 | |a Sebastopol, CA : |b O'Reilly Media, |c 2018. | |
| 300 | |a 1 online resource (xxiv, 400 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file | ||
| 500 | |a Includes index. | ||
| 588 | |a Online resource; title from PDF title page (EBSCO, viewed December 7, 2018). | ||
| 520 | |a 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. | ||
| 542 | |f Copyright © 2019 Matthew Russell, Mikhail Klassen | ||
| 505 | 0 | |a I.A Guided Tour of the Social Web -- Prelude -- 1. Mining Twitter: Exploring Trending Topics, Discovering What People Are Talking About, and More -- 2. Mining Facebook: Analyzing Fan Pages, Examining Friendships, and More -- 3. Mining Instagram: Computer Vision, Neural Networks, Object Recognition, and Face Detection -- 4. Mining LinkedIn: Faceting Job Titles, Clustering Colleagues, and More -- 5. Mining Text Files: Computing Document Similarity, Extracting Collocations, and More -- 6. Mining Web Pages: Using Natural Language Processing to Understand Human Language, Summarize Blog Posts, and More -- 7. Mining Mailboxes: Analyzing Who's Talking to Whom About What, How Often, and More -- 8. Mining GitHub: Inspecting Software Collaboration Habits, Building Interest Graphs, and More -- II. Twitter Cookbook -- 9. Twitter Cookbook -- III. Appendixes -- A. Information About This Book's Virtual Machine Experience -- B. OAuth Primer -- C. Python and Jupyter Notebook Tips and Tricks. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Online social networks. | |
| 650 | 2 | |a Data Mining | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 6 | |a Réseaux sociaux (Internet) | |
| 650 | 7 | |a COMPUTERS |x General. |2 bisacsh | |
| 650 | 7 | |a Data mining |2 fast | |
| 650 | 7 | |a Online social networks |2 fast | |
| 700 | 1 | |a Klassen, Mikhail, |e author. | |
| 776 | 0 | 8 | |i Print version: |a Russell, Matthew A. (Computer scientist). |t Mining the social web. |d Sebastopol, CA : O'Reilly Media, 2018 |z 9781491985045 |z 1491985046 |w (OCoLC)987280491 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781491973547/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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