Abstract
This chapter moves readers from the analysis of one or two texts to a larger corpus. Machine clustering is introduced in the context of an authorship attribution problem.
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Notes
- 1.
If you have been working on other sections of this book or on R projects of your own, it might be a good idea to either restart R or to clear the R workspace. To do the latter, just click on the Session menu of the RStudio GUI and select Clear Workspace. This will remove all R objects and functions that you may have been using, wiping the R slate clean, as it were.
- 2.
Patrick Burns has written a 125 page book documenting many of R’s unusual behavior. The book is informative and entertaining to read. You can find it online at http://www.burns-stat.com.
- 3.
Enter ?regex at the prompt to learn more about regex in R.
- 4.
You can learn more about the useInternalNodes argument in the documentation for the xmlTreeParse function. Basically, setting it to TRUE avoids converting the contents into R objects, which saves a bit of processing time.
- 5.
- 6.
For a brief overview of how this work is conducted, See Jockers, Matthew L. Macroanalysis: Digital Methods and Literary History. University of Illinois Press, 2013. Pages 63–67.
- 7.
seq_along is a simple R function for generating a sequence of numbers. Check the R-help documentation for details. In this example, I could have just as easily used 1:43 or 1:length(book.freqs.l).
- 8.
Factors are explained in a later section.
- 9.
Other options include using reshape and expressions that leverage the apply family of functions.
- 10.
Remember that the getTEIWordTableList function that we built multiplies all the relative frequencies by 100.
- 11.
For details, consult the documentation for the dist and hclust functions.
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© 2014 Springer International Publishing Switzerland
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Jockers, M.L. (2014). Clustering. In: Text Analysis with R for Students of Literature. Quantitative Methods in the Humanities and Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-03164-4_11
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DOI: https://doi.org/10.1007/978-3-319-03164-4_11
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