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Mining the Social Web: Data Mining Facebook, Twitter, Linkedin, Instagram, Github, and More - Paperback
$55.99
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by Matthew A. Russell (Author), Mikhail Klassen (Author)
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
Author Biography
Matthew Russell (@ptwobrussell) is Chief Technology Officer at Built Technologies, where he leads a team of leaders on a mission to improve the way the world is built. Outside of work, he contemplates ultimate reality, practices rugged individualism, and trains for the possibilities of a zombie or robot apocalypse.
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.
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Standard U.S. returns are accepted for eligible new, unopened items within 30 days of carrier delivery. Returns are by mail. Customers are responsible for standard return shipping, there is no restocking fee, and standard exchanges are not offered. Once an approved return is received and inspected, the refund is issued to the original payment method within 5 business days. Damaged, defective, or incorrect items are handled at no return-shipping cost to the customer.
Return eligibility is subject to the full policy. Opened, read, or unsealed books with broken seals or visible wear are final sale. Digital downloads, gift cards, and personalized products are non-refundable. Original shipping charges are not refunded for standard returns. Contact us within 7 days of delivery about damaged, defective, or incorrect items.
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