Text Mining and Sentiment Analysis with R

Text data is generated at an incredible rate, yet most of it remains unstructured and underutilized. This course provides a structured path for beginners to transform raw text from social media and web sources into meaningful insights using the R programming language. You will progress from understanding basic text structures to performing sentiment analysis and creating informative visualizations. By applying modern R workflows, you will learn how to handle the unique challenges of social media data, including noise, formatting issues, and high-volume streams. * Understand the core concepts of text mining and natural language processing * Extract data from social media platforms and web pages using R scripts * Clean and structure unstructured text using modern string manipulation techniques * Perform sentiment analysis to identify emotional trends in large datasets * Build text visualizations including wordclouds and frequency distributions * Practice modern text analysis workflows using the tidytext framework The course begins with essential terminology and foundational R packages before moving into data acquisition and cleaning techniques. Each section includes written exercises designed to reinforce your understanding through practical application of code and logic. This course is designed for beginners interested in data science, social media analytics, or linguistics who want a text-based, hands-on introduction to R. No prior experience with text mining is required. Start building your text analytics toolkit and uncover the stories hidden in social media data.