Matthew A. Russell

Mining the Social Web (eBook, PDF)

Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More

• Format: PDF

Produktbeschreibung

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 mediaincluding whos connecting with whom, what theyre talking about, and where theyre locatedusing 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 landscapeUse Docker to easily run each chapters example code, packaged as a Jupyter notebookAdapt and contribute to the codes open source GitHub repositoryLearn how to employ best-in-class Python 3 tools to slice and dice the data you collectApply advanced mining techniques such as TFIDF, cosine similarity, collocation analysis, clique detection, and image recognitionBuild beautiful data visualizations with Python and JavaScript toolkits

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Produktdetails

• Verlag: O'Reilly Media
• Seitenzahl: 432
• Erscheinungstermin: 4. Dezember 2018
• Englisch
• ISBN-13: 9781491973523
• Artikelnr.: 54746441

Autorenporträt

Mikhail Klassen is Chief Data Scientist at Paladin AI, a startup creating adaptive training technologies. He has a PhD in computational astrophysics from McMaster University and a BS in applied physics from Columbia University. Mikhail is passionate about artificial intelligence and how the tools of data science can be used for good. When not working at a startup, he's usually reading or traveling.