Book Image

Machine Learning for Emotion Analysis in Python

By : Allan Ramsay, Tariq Ahmad
5 (1)
Book Image

Machine Learning for Emotion Analysis in Python

5 (1)
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)
1
Part 1:Essentials
3
Part 2:Building and Using a Dataset
7
Part 3:Approaches
14
Part 4:Case Study

Summary

There is no doubt that finding suitable data can be a challenge, but there are ways and means to mitigate that. For example, there are plenty of repositories with comprehensive search features that allow you to find relevant datasets.

In this chapter, we started by looking at public data sources and went through some of the most popular ones. We saw that many datasets are free, but access to some required a subscription to the repository. Even with the existence of these repositories, there is still sometimes a need to “roll your own” dataset, so we looked at the benefit of doing that and some ways in which we might collect our own data and create our own datasets. We then discussed some niche places to find datasets specific to the emotion analysis problem—for example, from competition websites. Datasets often contain sensitive information about individuals, such as their personal beliefs, behaviors, and mental health status, hence we noted that it...