Text Mining with R

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&&&8217;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&&&8217;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&&&8217;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&&&8217;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&&&8217;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