Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Machine Learning for Emotion Analysis in Python
  • Table Of Contents Toc
Machine Learning for Emotion Analysis in Python

Machine Learning for Emotion Analysis in Python

By : Allan Ramsay, Tariq Ahmad
4.6 (5)
close
close
Machine Learning for Emotion Analysis in Python

Machine Learning for Emotion Analysis in Python

4.6 (5)
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)
close
close
1
Part 1:Essentials
3
Part 2:Building and Using a Dataset
7
Part 3:Approaches
14
Part 4:Case Study

Support Vector Machines

In Chapter 6, Naive Bayes, we looked at using Bayes’ Theorem to find the emotions that are associated with individual tweets. The conclusion there was that the standard Naive Bayes algorithm worked well with some datasets and less well with others. In the following chapters, we will look at several other algorithms to see whether we can get any improvements, starting in this chapter with the well-known support vector machine (SVM) (Boser et al., 1992) approach.

We will start this chapter by giving a brief introduction to SVMs. This introduction will take a geometric approach that may be easier for you than the standard presentation. Bennett and Bredensteiner (see the References section) give detailed formal proof that the two approaches are equivalent – the discussion in this chapter is intended simply to provide an intuitive grasp of the issues. We will then show you how to use the sklearn.svm.LinearSVC implementation for our current task....

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Machine Learning for Emotion Analysis in Python
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon