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Mastering Social Media Mining with Python

You're reading from   Mastering Social Media Mining with Python Unearth deeper insight from your social media data with advanced Python techniques for acquisition and analysis

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Product type Paperback
Published in Jul 2016
Publisher Packt
ISBN-13 9781783552016
Length 338 pages
Edition 1st Edition
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Author (1):
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Marco Bonzanini Marco Bonzanini
Author Profile Icon Marco Bonzanini
Marco Bonzanini
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Table of Contents (10) Chapters Close

Preface 1. Social Media, Social Data, and Python FREE CHAPTER 2. #MiningTwitter – Hashtags, Topics, and Time Series 3. Users, Followers, and Communities on Twitter 4. Posts, Pages, and User Interactions on Facebook 5. Topic Analysis on Google+ 6. Questions and Answers on Stack Exchange 7. Blogs, RSS, Wikipedia, and Natural Language Processing 8. Mining All the Data! 9. Linked Data and the Semantic Web

Mining videos on YouTube


YouTube (http://youtube.com) probably doesn't need much introduction nowadays, being one of the most visited websites in the world (ranked second in March 2016 in Alexa rank). The video-sharing service features a wide spectrum of content material is, in fact, produced and shared by a range of different authors, from amateur video bloggers to big corporations. YouTube's registered users can upload, rate, and comment on videos. Viewing and sharing doesn't require the users to register a profile.

YouTube was acquired by Google in 2006, so today they are part of the larger Google platform. In Chapter 5, Topic Analysis on Google+, and Chapter 7, Blogs, RSS, Wikipedia and Natural Language Processing, we have already introduced other services by Google, especially Google+ and Blogger. YouTube provides three different APIs to integrate your applications with the YouTube platform: YouTube Data, YouTube Analytics, and YouTube Reporting. We'll focus on the first one to retrieve...

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