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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

Summary

At the beginning of this chapter, we referenced the vision of Tim Berners-Lee, the creator of the Web. Considering the Web as a social creation rather than a technical one is a point of view that softens the border between social and semantic data.

We also described the bigger picture of the Semantic Web and how the different technologies proposed throughout the years fit together and offer the opportunity to make this vision concrete. Even though the hype and hope about the Semantic Web have been quite high in the past 15-20 years, Tim Berners-Lee's vision is not ubiquitous yet.

Community efforts such as those promoted by the W3C, and by governments and other private companies, such as schema.org, attempt to make a strong push towards this direction. While the skeptics have plenty of material to discuss why the promises of the Semantic Web are not fully implemented yet, we prefer to focus on the opportunities that we have at the moment.

The current trends of the social media...

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