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written assignment follow the document for the question

  • Chapter 1 outlines the tidy text format and the unnest_tokens() function. It also introduces the gutenbergr and janeaustenr packages, which provide useful literary text datasets that we’ll use throughout this book.
  • Chapter 2 shows how to perform sentiment analysis on a tidy text dataset, using the sentimentsdataset from tidytext and inner_join() from dplyr.
  • Chapter 3 describes the tf-idf statistic (term frequency times inverse document frequency), a quantity used for identifying terms that are especially important to a particular document.
  • Chapter 4 introduces n-grams and how to analyze word networks in text using the widyr and ggraph packages.
  • Chapter 5 introduces methods for tidying document-term matrices and corpus objects from the tm and quanteda packages, as well as for casting tidy text datasets into those formats.
  • Chapter 6 explores the concept of topic modeling, and uses the tidy() method to interpret and visualize the output of the topicmodels package.

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