Preserving Phrases with N-Grams in R

Text analysis often falls short when analyzing individual words out of context. To truly understand written data, you must learn to capture multi-word phrases and idioms that hold specific meanings. This course teaches you how to identify, extract, and preserve meaningful word sequences using n-grams in the R programming language. You will transition from basic string processing to advanced phrase-based text mining, enabling you to extract richer insights from unstructured data. By learning to group words that belong together, you will significantly improve the accuracy of your sentiment analysis, topic modeling, and natural language processing workflows. What you'll learn: - Understand the core concepts of tokenization, unigrams, bigrams, and general n-grams. - Clean and preprocess raw text data in R to prepare it for precise phrase extraction. - Extract and analyze phrase frequencies using modern R packages for text analysis. - Filter out noise and meaningless word combinations using customized stop-word lists. - Visualize phrase networks to discover relationships between terms in your corpus. - Apply n-gram techniques to real-world text classification and sentiment analysis tasks. This course begins with foundational definitions of natural language processing concepts and progresses step-by-step through practical R code examples. You will read through clear explanations, analyze structured code blocks, and complete written exercises designed to build your confidence. This course is designed for beginners, data enthusiasts, and aspiring analysts who want to work with text data in R. No previous experience with natural language processing is required, though a basic familiarity with R syntax is helpful. Start reading today to unlock the hidden context in your text data.