Includes bibliographical references and index.

Language and modeling -- Tokenization -- Stop words -- Stemming -- Word Embeddings -- Regression -- Classification -- Dense neural networks -- Long short-term memory (LSTM) networks -- Convolutional neural networks -- Text models in the real world.

"Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing"-- Provided by publisher.