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

Naïve Bayes as a machine learning algorithm

The key idea behind the Naïve Bayes algorithm is that you can estimate the likelihood of some outcome given a set of observations by using conditional probabilities and linking the individual observations to the outcome. Defining what conditional probability is turns out to be surprisingly slippery because the notion of probability itself is very slippery. Probabilities are often defined as something similar to proportions, but this view becomes difficult to maintain when you are looking at unique or unbounded sets, which is usually the case when you want to make use of them.

Suppose, for instance, that I am trying to work out how likely it is that France will win the FIFA 2022 World Cup (this is being written 2 days before the final, between France and Argentina, is to be played). In some sense, it is reasonable to ask about this probability – if the bookmakers are offering 3 to 1 against France and the probability that...