Supervised machine learning for text analysis in R - Hvitfeldt, Emil; Silge, Julia (CRC Press, Taylor & Francis Group, 2022)
"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"--
Literaturverzeichnis: Seite 369-378
Keywords: R -- Programm; Textanalyse; Maschinelles Lernen; Computational linguistics / Statistical methods; Natural language processing (Computer science); Supervised learning (Machine learning); Predictive analytics; Regression analysis; Discriminant analysis; R (Computer program language)