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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

Summary

Transformers have proved to be very successful in a range of natural language tasks, with numerous recently released chatbots outperforming existing models in their ability to understand and manipulate human language. In this chapter, we looked at how transformers can be used for the task of assigning emotions to informal texts and investigated how well they perform on this task with a range of datasets. We started by taking a brief look at transformers, focusing on the individual components of a transformer, and how data flows through them. Transformers need a lot of data to be effective and produce good results, and a huge amount of computing power and time is also needed. Then, we introduced Hugging Face, discussed why it was useful, and introduced some of the more common pretrained models that are available on the Hugging Face platform, before moving on to discussing how transformers are used for classification. Finally, we showed how to code classifiers using transformers...

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Machine Learning for Emotion Analysis in Python
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