Practical Text Mining and NLP in R

Much of the world's data exists as unstructured text, from social media posts to customer reviews. Learning how to analyze this text is a critical skill for modern data scientists. This course teaches you how to transform raw text into actionable insights using R. You will start with the fundamental concepts of natural language processing (NLP) and text mining, then progress to practical techniques for importing, cleaning, and analyzing unstructured data. By the end of this course, you will be able to perform sentiment analysis, build predictive text models, and extract meaning from online feeds using modern R packages. What you'll learn: - Understand the core concepts of natural language processing and text mining workflows - Clean and preprocess raw text data using modern R packages like tidytext and stringr - Perform exploratory data analysis and sentiment analysis on unstructured text - Extract text data from web pages and online sources using modern scraping techniques - Build and evaluate predictive machine learning models for text classification in R - Apply tokenization, term frequency analysis, and topic modeling to discover hidden patterns The course begins with foundational definitions and key terminology before moving into hands-on text manipulation. You will then progress through real-world scenarios, including web scraping, sentiment analysis, and predictive modeling. This course is designed for beginners in data science and R programming who want to expand their skills into NLP. No prior experience with text mining is required, though a basic familiarity with R syntax is helpful. Start reading today to unlock the power of unstructured text data.