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Machine Learning for Emotion Analysis in Python

Machine Learning for Emotion Analysis in Python

By : Allan Ramsay, Tariq Ahmad
4.6 (5)
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Machine Learning for Emotion Analysis in Python

Machine Learning for Emotion Analysis in Python

4.6 (5)
By: Allan Ramsay, Tariq Ahmad

Overview of this book

Artificial intelligence and machine learning are the technologies of the future, and this is the perfect time to tap into their potential and add value to your business. Machine Learning for Emotion Analysis in Python helps you employ these cutting-edge technologies in your customer feedback system and in turn grow your business exponentially. With this book, you’ll take your foundational data science skills and grow them in the exciting realm of emotion analysis. By following a practical approach, you’ll turn customer feedback into meaningful insights assisting you in making smart and data-driven business decisions. The book will help you understand how to preprocess data, build a serviceable dataset, and ensure top-notch data quality. Once you’re set up for success, you’ll explore complex ML techniques, uncovering the concepts of deep neural networks, support vector machines, conditional probabilities, and more. Finally, you’ll acquire practical knowledge using in-depth use cases showing how the experimental results can be transformed into real-life examples and how emotion mining can help track short- and long-term changes in public opinion. By the end of this book, you’ll be well-equipped to use emotion mining and analysis to drive business decisions.
Table of Contents (18 chapters)
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1
Part 1:Essentials
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3
Part 2:Building and Using a Dataset
7
Part 3:Approaches
14
Part 4:Case Study

Part 1:Essentials

The part introduces natural language processing (NLP), sentiment analysis (SA), and emotion analysis (EA). You will learn about the basic concepts behind SA and EA, why and how they are different, and why EA is so challenging. There is also an introduction to some of the tools that will be used. This part will also look at other approaches to multi-emotion classification and discuss emotions from a psychological point of view. Finally, there will be a discussion on why EA is important, its benefits, and usages.

This part has the following chapter:

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Tech Concepts
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Programming languages
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Machine Learning for Emotion Analysis in Python
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