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

• Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning
• Natural Language Processing with Python
• R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
• Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition
• Deep Learning with R
• Practical Statistics for Data Scientists: 50 Essential Concepts
• Advanced R, Second Edition (Chapman & Hall/CRC The R Series)
• Data Visualization: A Practical Introduction
• R Graphics Cookbook: Practical Recipes for Visualizing Data
• R Cookbook (O'Reilly Cookbooks)