Text Mining with R: A Tidy Approach

Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective.

The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You’ll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media.

• Learn how to apply the tidy text format to NLP
• Use sentiment analysis to mine the emotional content of text
• Identify a document’s most important terms with frequency measurements
• Explore relationships and connections between words with the ggraph and widyr packages
• Convert back and forth between R’s tidy and non-tidy text formats
• Use topic modeling to classify document collections into natural groups
• Examine case studies that compare Twitter archives, dig into NASA metadata, and analyze thousands of Usenet messages

Table of Contents

Chapter 1 The Tidy Text Format
Chapter 2 Sentiment Analysis with Tidy Data
Chapter 3 Analyzing Word and Document Frequency: tf-idf
Chapter 4 Relationships Between Words: N-grams and Correlations
Chapter 5 Converting to and from Nontidy Formats
Chapter 6 Topic Modeling
Chapter 7 Case Study: Comparing Twitter Archives
Chapter 8 Case Study: Mining NASA Metadata
Chapter 9 Case Study: Analyzing Usenet Text