Mining the Social Web: Data Mining Facebook, Twitter, Linkedin, Instagram, Github, and More
商品資訊
ISBN13:9781491985045
出版社:Oreilly & Associates Inc
作者:Matthew A. Russell; Mikhail Klassen
出版日:2017/11/25
裝訂/頁數:平裝/500頁
規格:22.9cm*17.8cm*2.5cm (高/寬/厚)
版次:3
商品簡介
Want to tap the tremendous amount of valuable social data in Facebook, Twitter, LinkedIn, GitHub, Instagram, and Google+? This new edition helps you discover who’s making connections with social media, what they’re talking about, and where they’re located. You’ll learn how to combine social web data, analysis techniques, and visualization to find what you’ve been looking for in the social haystack—as well as useful information you didn’t know existed.
- Get a straightforward synopsis of the social web landscape
- Use adaptable scripts on GitHub to harvest data from social network APIs.
- Learn how to employ easy-to-use Python tools to slice and dice the data you collect
- Explore social connections in microformats with the XHTML Friends Network
- Apply advanced mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, clique detection, and image recognition
- Build interactive visualizations with web technologies based upon HTML5 and JavaScript toolkits
作者簡介
Matthew Russell, Chief Technology Officer at Digital Reasoning, Principal at Zaffra, and author of several books on technology including Mining the Social Web (O'Reilly, 2013), now in its second edition. He is passionate about open source software development, data mining, and creating technology to amplify human intelligence. Matthew studied computer science and jumped out of airplanes at the United States Air Force Academy. When not solving hard problems, he enjoys practicing Bikram Hot Yoga, CrossFitting and participating in triathlons.
Mikhail Klassen is co-founder and Chief Data Scientist at Paladin:Paradigm, an aerospace analytics startup based in Montreal.His PhD research work was in large-scale numerical simulations of star formation, where he implemented a novel radiative transfer technique that led to more accurate models of the birth environments of high-mass stars.
At Paladin:Paradigm, he's working on designing the first data-driven adaptive training platform for next-generation pilots using data mining techniques and machine learning.
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