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

In Chapter 5, Sentiment Lexicons and Vector-Space Models, we investigated the use of simple lexicon-based classifiers, using both a hand-coded sentiment lexicon and extracting a lexicon from a corpus of marked-up texts. The results from this investigation were that such models can produce reasonable scores, with a variety of tweaks (using a stemmer or changing the way that weights are calculated, such as by using TF-IDF scores) that produce improvements in some cases but not in others. We will now turn to a range of machine learning algorithms to see whether they will lead to better results.

For most of the algorithms that we will be looking at, we will use the Python scikit-learn (sklearn) implementations. A wide range of implementations for all these algorithms are available. The sklearn versions have two substantial advantages: they are freely available with a fairly consistent interface to the training and testing data and they can be easily installed and run...