Sentiment Analysis in R

Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.

Add sentiment analysis to your text mining toolkit! Sentiment analysis is used by text miners in marketing, politics, customer service, and elsewhere. In this course you will learn to identify positive and negative language, specific emotional intent, and make compelling visualizations. You’ll start with an introduction to polarity scoring using qdap’s sentiment function, and will build your understanding of Zipf’s law and subjectivity lexicons along the way.



Sentiment, and the language used to express it, is complicated and nuanced. It’s based on linguistics, sociology, and psychology, as well as culture and slang. The second chapter in this course helps you navigate those difficulties using Plutchik’s wheel of emotion, and organizes your work using Tidytext from the Tidyverse.



Turning your sentiment analysis into clear data visualizations will help you create a clearer narrative and share your insights with the rest of the business. The third chapter of this course shows you how to visualize your sentiment analysis, and takes you beyond word clouds to create simple and impactful graphics that tell the full story of your data.



You’ll finish off the course by putting all of your knowledge to the test with a case study. Using Airbnb reviews, you’ll explore what people really look for in a good rental.

• Fast & Dirty: Polarity scoring
• In the first chapter, you will learn how to apply qdap's sentiment function called polarity() .
• Sentiment Analysis the tidytext Way
• In the second chapter you will explore 3 subjectivity lexicons from tidytext. Then you will do an inner join to score some text.
• Visualizing Sentiment
• Make compelling visuals with your sentiment output.
• Case study: Airbnb reviews
• Is your property a good rental? What do people look for in a good rental?