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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

Blogs and NLP


Blogs (short for weblogs) are nowadays an important part of the Web, and an incredibly attractive social media platform. Blogs are used by companies, professionals, and hobbyists to reach out to an audience, promote products and services, or simply discuss an interesting topic. Thanks to the abundance of web-publishing tools and services that makes it easy for non-technical users to post their content, setting up a personal blog is a matter of minutes.

From the point of view of a data miner, blogs are the perfect platform to practice text mining. This chapter focuses on two main topics: how to get textual data from blogs and how to apply NLP on such data.

While NLP is a complex field that deserves more than a book just to get started, we're taking a pragmatic approach and introducing the basic theory with practical examples.

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