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R Programming By Example

You're reading from   R Programming By Example Practical, hands-on projects to help you get started with R

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Product type Paperback
Published in Dec 2017
Publisher Packt
ISBN-13 9781788292542
Length 470 pages
Edition 1st Edition
Languages
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Authors (2):
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Omar Trejo Navarro Omar Trejo Navarro
Author Profile Icon Omar Trejo Navarro
Omar Trejo Navarro
Omar Trejo Navarro Omar Trejo Navarro
Author Profile Icon Omar Trejo Navarro
Omar Trejo Navarro
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Toc

Table of Contents (12) Chapters Close

Preface 1. Introduction to R 2. Understanding Votes with Descriptive Statistics FREE CHAPTER 3. Predicting Votes with Linear Models 4. Simulating Sales Data and Working with Databases 5. Communicating Sales with Visualizations 6. Understanding Reviews with Text Analysis 7. Developing Automatic Presentations 8. Object-Oriented System to Track Cryptocurrencies 9. Implementing an Efficient Simple Moving Average 10. Adding Interactivity with Dashboards 11. Required Packages

Digging deeper with sentiment analysis

We have now seen that vector space operations did not work too well regarding the predictive accuracy of our model. In this section, we will attempt a technique which is very different and is closer to the semantic parsing model we mentioned at the beginning of this chapter. We will try sentiment analysis.

We will not only take into account the words in a text, but we will also take into account shifters (that is, negators, amplifiers, de-amplifiers, and adversative conjunctions). A negator flips the sign of a polarized word (for example, I do not like it). An amplifier increases the impact of a polarized word (for example, I really like it.). A de-amplifier reduces the impact of a polarized word (for example, I hardly like it). An adversative conjunction overrules the previous clause containing a polarized word (for example, I like it but...

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