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

A geometric introduction to SVMs

Suppose we have two groups of entities, called B and R, where each entity is described by a pair of numerical coordinates. B includes objects with coordinates (6.38, -10.62), (4.29, -8.99), (8.68, -4.54), and so on and R contains objects with coordinates (6.50, -3.82), (7.39, -3.13), (7.64, -10.02), and so on (the example used in this discussion has been taken from https://scikit-learn.org/stable/modules/svm.html#classification). Plotting these points on a graph gives us the following:

Figure 7.1 – Plot of the R and B points

Figure 7.1 – Plot of the R and B points

It looks as though you should be able to draw a straight line to separate the two groups, and if you could, then you could use it to decide whether some new point was an instance of R or B.

There are numerous ways of finding such a line for a simple case like this. One approach would be to find the convex hulls (Graham, 1972) for the two groups – that is, the polygons that include them...