tidytext Basics

Learn about the basic structure of a tidytext program.

We'll cover the following

Key concepts of tidytext

tidytext is designed to streamline specific text analysis tasks, making it a valuable tool for text mining and natural language processing. It is focused on a limited but important set of tasks, such as:

  • Tokenization: tidytext helps us break down text documents into individual words or tokens. The unnest_tokens() function is commonly used for this purpose, allowing us to specify how we want to tokenize our text (such as by word or by sentence).

  • Sentiment analysis: tidytext includes functions for performing sentiment analysis on text data. We can use prebuilt sentiment lexicons, such as the Bing or AFINN lexicons, or create custom lexicons. The get_sentiments() function retrieves sentiment lexicons, and the inner_join() function can be used to join sentiment scores with our text data.

  • Term frequency-inverse document frequency: Tf-idf is a numerical statistic that reflects the importance of a word within a document and across a collection of documents. The bind_tf_idf() function in tidytext calculates these values, allowing us to compare the importance of words across different documents.

  • Visualization: tidytext integrates with ggplot2, a popular visualization package in R, allowing us to create insightful visualizations of our text data.

Here’s some basic code illustrating how tidytext works:

Get hands-on with 1200+ tech skills courses.