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Mastering Machine Learning with R

You're reading from   Mastering Machine Learning with R Advanced machine learning techniques for building smart applications with R 3.5

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Product type Paperback
Published in Jan 2019
Publisher
ISBN-13 9781789618006
Length 354 pages
Edition 3rd Edition
Languages
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Author (1):
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Cory Lesmeister Cory Lesmeister
Author Profile Icon Cory Lesmeister
Cory Lesmeister
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Toc

Table of Contents (16) Chapters Close

Preface 1. Preparing and Understanding Data FREE CHAPTER 2. Linear Regression 3. Logistic Regression 4. Advanced Feature Selection in Linear Models 5. K-Nearest Neighbors and Support Vector Machines 6. Tree-Based Classification 7. Neural Networks and Deep Learning 8. Creating Ensembles and Multiclass Methods 9. Cluster Analysis 10. Principal Component Analysis 11. Association Analysis 12. Time Series and Causality 13. Text Mining 14. Creating a Package 15. Other Books You May Enjoy

Sentiment analysis

"We shall nobly save, or meanly lose, the last, best hope of earth.”
Abraham Lincoln

In this section, we'll take a look at the various sentiment options available in tidytext. Then, we'll apply that to a subset of the data before, during, and after the Civil War. To get started, let's explore the sentiments dataset that comes with tidytext:

> table(sentiments$lexicon)

AFINN bing loughran nrc
2476 6788 4149 13901

The four sentiment options and researchers associated with them are as follows:

  • AFINN: Finn, Arup, and Nielsen
  • bing: Bing, Liu et al.
  • loughran: Loughran and McDonald
  • nrc: Mohammad and Turney

The AFINN sentiment categorizes words on a negative to positive scale from -5 to +5. The bing version has a simple binary negative or positive ranking; loughran provides six different categories including negative...

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