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Mastering Spark for Data Science

You're reading from   Mastering Spark for Data Science Lightning fast and scalable data science solutions

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781785882142
Length 560 pages
Edition 1st Edition
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Authors (5):
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David George David George
Author Profile Icon David George
David George
Matthew Hallett Matthew Hallett
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Matthew Hallett
Antoine Amend Antoine Amend
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Antoine Amend
Andrew Morgan Andrew Morgan
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Andrew Morgan
Albert Bifet Albert Bifet
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Albert Bifet
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Table of Contents (15) Chapters Close

Preface 1. The Big Data Science Ecosystem FREE CHAPTER 2. Data Acquisition 3. Input Formats and Schema 4. Exploratory Data Analysis 5. Spark for Geographic Analysis 6. Scraping Link-Based External Data 7. Building Communities 8. Building a Recommendation System 9. News Dictionary and Real-Time Tagging System 10. Story De-duplication and Mutation 11. Anomaly Detection on Sentiment Analysis 12. TrendCalculus 13. Secure Data 14. Scalable Algorithms

A Small Step into sarcasm detection

Detecting sarcasm is an active area of research (http://homes.cs.washington.edu/~nasmith/papers/bamman+smith.icwsm15.pdf). In fact, detecting sarcasm is often not easy for humans, so how can it be easy for computers? If I say "We will make America great again"; without knowing me, observing me, or hearing the tone I'm using, how could you know if I really meant what I said? Now, if you were to read a tweet from me that says "We will make America great again :(:(:(", does it help in a sense?

Building features

We believe that sarcasm cannot be detected using plain English text only, especially not when the plain text fits into less than 140 characters. However, we showed in this chapter that emojis can play a major role in the definition of emotion. A naive assumption is that a tweet with both positive sentiment and negative emojis can potentially lead to sarcasm. In addition to the tone, we also found that some words were closer...

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