Machine Learning for Emotion Analysis in Python: Build AI-powered tools for analyzing emotion using natural language processing and machine learning

by Allan Ramsay, Allan Ramsay

Kickstart your emotion analysis journey with this step-by-step guide to data science success

Key FeaturesDiscover the inner workings of the end-to-end emotional analysis workflowExplore the use of various ML models to derive meaningful insights from dataHone your craft by building and tweaking complex emotion analysis models with practical projectsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionArtificial 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.

What you will learnDistinguish between sentiment analysis and emotion analysisMaster data preprocessing and ensure high-quality inputExpand the use of data sources through data transformationDesign models that employ cutting-edge deep learning techniquesDiscover how to tune your models’ hyperparametersExplore the use of naive Bayes, SVMs, DNNs, and transformers for advanced use casesPractice your newly acquired skills by working on real-world scenariosWho this book is forThis book is for data scientists and Python developers looking to gain insights into the customer feedback for their product, company, brand, governorship, and more. Basic knowledge of machine learning and Python programming is a must.

Table of ContentsFoundationsBuilding and Using a DatasetLabelling DataPreprocessing - Stemming, Tagging, and ParsingSentiment Lexicons and Vector-Space ModelsNaïve BayesSupport Vector MachinesNeural Networks and Deep Neural NetworksExploring TransformersMulticlassifiersCase Study - The Qatar Blockade

334 pages, Kindle Edition

Published September 28, 2023

About the author

Allan Ramsay was a Scottish playwright, publisher, librarian, and impresario who lived in early Enlightenment Edinburgh. His works include the pastoral drama The Gentle Shepherd (1725).

Ramsay probably attended the parish school at Crawfordjohn. He at the age of 16 years in 1700 apprenticed as a wig maker in Edinburgh and later set up his own wig-making business. Always a voracious reader, he began composing verses and in 1712 he was one of the founders of the Easy Club, a group of like-minded men who enjoyed literary discussions over a bottle of claret.

He is best known for his Tea-Table Miscellany (1724 - 1737), a highly regarded and influential collection of Scottish song, The Ever Green (1724), which brought work by the medieval Makars together with that of poets of the seventeenth century, and The Gentle Shepherd (1725), a ballad opera and a hymn to the joys of pastoral life. As a compiler and editor of Scottish lyrics and verse, he played an important part in preserving Scottish work, bridging the ages and inspiring other ballad collectors, such as Sir Walter Scott.