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

Labeling Data

Artificial Intelligence (AI) models are only as good as the data they are trained with. Hence good, high-quality data is vitally important.

AI algorithms generally start in a basic, simplified, form. In supervised learning, accurately labeling (also known as annotating) data is a vitally important step to train an algorithm, improve its predictions, and ensure that what it learns is right. Numerous studies, reports, and surveys show that data scientists spend anywhere between 50-80% of their time doing data preparation and preprocessing (see Figure 3.1) – and data labeling is usually a huge part of this.

Figure 3.1 – Distribution of time allocated to machine learning tasks

Figure 3.1 – Distribution of time allocated to machine learning tasks

In this chapter, you will learn why it is important to ensure that data is labeled correctly; how this can be achieved; how to assess whether it has indeed been achieved; and in particular, how to identify annotators who have not carried out the task to...