Discover text mining in R and learn how to extract exciting insights from tweets, product reviews, and books through sentiment analysis in R.
You’ll discover a range of approaches to organizing and analyzing text data from books, articles, documents, and more. You’ll get a primer into regular expressions and look at ways to search for common patterns in text effectively.
As you progress, you’ll cover a range of tidyverse packages that can help with text analysis in R, including stringr and tidytext. As well as covering string manipulation and the bag of words technique for text mining in R, you’ll also look at how sentiment analysis works.
By the time you finish, you’ll have a firm understanding of text analysis in R and will have the confidence to carry out your own text mining and sentiment analysis.
Overview
Syllabus
- Introduction to Text Analysis in R
- Analyze text data in R using the tidy framework.
- String Manipulation with stringr in R
- Learn how to pull character strings apart, put them back together and use the stringr package.
- Text Mining with Bag-of-Words in R
- Learn the bag of words technique for text mining with R.
- Sentiment Analysis in R
- Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.
Taught by
Ted Kwartler, Charlotte Wickham, and Maham Khan
