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Text Mining with R: A Tidy Approach

Amazon.com Price:  $24.94 (as of 01/05/2019 13:34 PST- Details)

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Description

Much of the data to be had nowadays 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 the use of the tidy principles in the back of R packages like ggraph and dplyr. You’ll learn the way tidytext and other tidy tools in R can make text analysis easier and more effective.

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

  • Learn how you can 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 backward and forward 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

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