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Beginning R pp 303-320 | Cite as

Chapter 19: Text Mining

  • Joshua F. Wiley
  • Larry A. Pace

Abstract

Our final topic is text mining or text data mining. It is really only something that can be done with access to comparatively decent computing power (at least historically speaking). The concept is simple enough. Read text data into R that can then be quantitatively analyzed. The benefits are easier to imagine than the mechanics. Put simply, imagine if one could determine the most common words in a chapter, or a textbook. What if the common words in one text could be compared to the common words in other texts? What might those comparisons teach us? Perhaps different authors have a set of go-to words they more frequently use. Perhaps there is a way to discover who wrote a historical text (or at least provide some likely suspects). Perhaps a model may be trained to sort "good" essays from "bad" essays (or to sort spam and ham in e-mail files). Full disclaimer: this is a beginning R text. There are many more things that would be brilliant to do to text data than what we will do in this chapter.

Keywords

Text File Text Mining Topic Model Function Call Latent Dirichlet Allocation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Dr. Joshua F. Wiley and the estate of Larry A. Pace 2015

Authors and Affiliations

  • Joshua F. Wiley
    • 1
  • Larry A. Pace
    • 1
  1. 1.IndianaUSA

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