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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

Text classification for question tags


This section is about supervised learning. We define the problem of assigning tags to a question as a text classification problem and we apply it to a dataset of questions from Stack Exchange.

Before introducing the details of text classification, let's consider the following question from the Movies & TV Stack Exchange website (title and body of the question have been merged):

"What's the House MD episode where he hired a woman to fake dead to fool the team? I remember a (supposedly dead) woman waking up and giving a high-five to House. Which episode was this from?"

The preceding question asks for details about a particular episode of the popular TV series House, M.D. As described earlier, questions on Stack Exchange are labeled with tags with the purpose of quickly identifying the topic of the question. The tags assigned by the user to this question are house and identify-this-episode, the first one being a reference to the TV series itself, while...

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