Foundations of Natural Language Processing in R

Vast amounts of valuable information are locked away in unstructured text, from customer reviews to social media posts. Learning how to programmatically process, analyze, and model this text using R opens up powerful capabilities for data-driven decision-making. This course guides you through the essential concepts of natural language processing (NLP) using modern R packages. You will transition from handling raw, messy text to building structured models that uncover hidden themes, extract key entities, and analyze sentiment. What you'll learn: - Understand the fundamental terminology of NLP and text preprocessing workflows - Apply regular expressions to search, match, and manipulate complex text patterns - Analyze text sentiment using modern tidy data principles and lexicons - Implement topic modeling to discover underlying themes across large document collections - Extract named entities and key phrases to identify critical subjects in text - Explore modern text representations, including basic word embeddings and vector concepts You will start with core linguistic concepts and text cleaning techniques before moving on to hands-on R implementations. The material progresses logically from basic tokenization to advanced modeling workflows, ensuring a solid conceptual and practical foundation. This course is designed for beginners to text mining and NLP, requiring only a basic familiarity with R programming. Start reading today to unlock the power of text analytics in R.