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R Machine Learning Projects

You're reading from   R Machine Learning Projects Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781789807943
Length 334 pages
Edition 1st Edition
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Author (1):
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Dr. Sunil Kumar Chinnamgari Dr. Sunil Kumar Chinnamgari
Author Profile Icon Dr. Sunil Kumar Chinnamgari
Dr. Sunil Kumar Chinnamgari
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Table of Contents (12) Chapters Close

Preface 1. Exploring the Machine Learning Landscape FREE CHAPTER 2. Predicting Employee Attrition Using Ensemble Models 3. Implementing a Jokes Recommendation Engine 4. Sentiment Analysis of Amazon Reviews with NLP 5. Customer Segmentation Using Wholesale Data 6. Image Recognition Using Deep Neural Networks 7. Credit Card Fraud Detection Using Autoencoders 8. Automatic Prose Generation with Recurrent Neural Networks 9. Winning the Casino Slot Machines with Reinforcement Learning 10. The Road Ahead
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The sentiment analysis problem

Sentiment analysis is one of the most general text classification applications. The purpose of it is to analyze messages such as user reviews, and feedback from employees, in order to identify whether the underlying sentiment is positive, negative, or neutral.

Analyzing and reporting sentiment in texts allows businesses to quickly get a consolidated high-level insight without having to read each one of the comments received.

While it is possible to generate holistic sentiment based on the overall comments received, there is also an extended area called aspect-based sentiment analysis. It is focused on deriving sentiment based on each area of the service. For example, a customer that visited a restaurant when writing a review would generally cover areas such as ambience, food quality, service quality, and price. Though the feedback about each of...

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