Overview

In the age of information overload, text is everywhere-from tweets and product reviews to scientific publications and policy documents. Text Mining with R is a comprehensive, modern, and hands-on guide for exploring, analyzing, and modeling unstructured textual data using the R programming language. Written in clear, narrative-driven prose, this book demystifies the process of extracting meaning from text through a blend of theory, implementation, and practical case studies. Built on the foundations of the tidyverse, this book introduces readers to the power of the tidytext, textrecipes, quanteda, text2vec, and stm packages, as well as supervised learning techniques using tidymodels. From simple sentiment analysis to topic modeling, word embeddings, and building multilingual NLP pipelines, the book guides both beginners and advanced users through essential concepts in natural language processing (NLP). Going beyond academic theory, Text Mining with R addresses real-world concerns: ethical text mining, bias in lexicons and models, and the interpretability of machine-learned text classifiers. Whether you're a data scientist, social scientist, journalist, or developer, this book equips you with the knowledge and tools to turn raw text into insights.

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