Text Mining with R: From Web Scraping to Sentiment Analysis

Unstructured text data is everywhere, from customer reviews to social media posts, but unlocking its value requires the right analytical tools. R provides a powerful, modern ecosystem specifically designed to turn raw text into actionable insights. This text-only course guides you through the entire text mining pipeline. You will start with the fundamental concepts of text processing, learn to scrape live web data, clean messy text, and perform advanced sentiment analysis to understand the underlying emotions in any dataset. What you'll learn: Understand the foundational concepts of text mining and natural language processing; Scrape raw text data from web pages using modern R packages like rvest; Clean and preprocess unstructured text using tidyverse and stringr; Extract top keywords, term frequencies, and document-term matrices; Perform sentiment analysis to categorize text as positive, negative, or neutral; Apply tidytext principles to structure and analyze text data efficiently. The course begins with key definitions and essential R syntax for text manipulation. You will then progress step-by-step through web scraping, data cleaning, keyword extraction, and sentiment modeling through detailed written explanations and practical code challenges. This course is designed for beginners interested in data science, marketing analysis, or research, with no prior text mining experience required. Start reading today to unlock the hidden insights within written text.