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

Labeling the data

There are many ways to get data labeled, each with its own pros and cons:

  • Internal (in-house) labeling: This is when experts from within an organization are used to label data. These are usually people who are domain experts and hence are very familiar with the process and requirements. Consequently, this leads to better quality control and high-quality labeling. Furthermore, as the data doesn’t need to leave the building, there are fewer associated security risks. However, internal labeling is not always possible (e.g., the company size is small or there is a lot of data to label). Furthermore, domain experts are expensive people so asking them to spend inordinate amounts of time on menial annotation tasks is probably not the best use of resources!
  • External (outsourced) labeling: As the name suggests, this is when the job is outsourced to companies that specialize in data labeling. These companies are experts at data labeling, and consequently...