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

Debrief

It is a good idea to follow up with your annotators after the task to ascertain their views about the overall task and whether they experienced any particular difficulties with any emotions. For example, tweets may contain several different emotions and hence it may be difficult to pinpoint the prevalent ones. There may also have been occasions where emotive words were seen but the annotator did not feel that this led them to strongly lean toward any particular emotion. It is sensible to obtain an understanding of these types of situations as early as possible as changes to the procedures for collecting data are likely to change the nature of what is collected. Typically, annotators refer to tweets being short and informal as a primary reason for being unable to determine a definitive emotion for a tweet. It is, therefore, ironic that when tweets are overly lengthy, annotators mention that it is hard to restrict themselves to selecting a sensible set of emotions, as in this...