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

Case Study – The Qatar Blockade

In this chapter, we will look at what happens when we apply one of our classifiers to real data that has not been carefully curated, that we don’t have a Gold Standard for, and that was not the data that we trained the classifier on. This is a real-life situation. You’ve trained a classifier; now, you want to use it. How well do the classifiers that we have looked at so far work in this situation? In this chapter, we will compare the output of a classifier on data collected over an extended period with events in an ongoing news story to see whether changes in the pattern of emotions can be linked to developments in the story and whether it is possible to detect long-term changes in public attitudes as well as immediate responses to key events. This analysis will be divided into three parts:

  • We will look at how specific events give rise to short-term changes in the pattern of emotions expressed in tweets
  • We will investigate...