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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
3
Part 2:Building and Using a Dataset
7
Part 3:Approaches
14
Part 4:Case Study

Building and Using a Dataset

The data collection and curation process is one of the most important stages in model building. It is also one of the most time-consuming. Typically, data can come from many sources; for example, customer records, transaction data, or stock lists. Nowadays, with the timely conjunction of big data, fast, high-capacity SSDs (to store big data), and GPUs (to process big data), it is easier for individuals to collect, store, and process data.

In this chapter, you will learn about finding and accessing pre-existing, ready-made data sources that can be used to train your model. We will also look at ways to create your own datasets, transforming datasets so that they are useful for your problem, and we will also see how non-English datasets can be utilized.

In the remainder of this book, we will be using a selection of the datasets listed in this chapter to train and test a range of classifiers. When we do this, we will want to assess how well the classifiers...

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Machine Learning for Emotion Analysis in Python
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