koRpus: docTermMatrix – R documentation

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docTermMatrix

Generate a document-term matrix

Description

Returns a sparse document-term matrix calculated from a given TIF[1] compliant token data frame or object of class kRp.text. You can also calculate the term frequency inverted document frequency value (tf-idf) for each term.

Usage

docTermMatrix(obj, terms = "token", case.sens = FALSE, tfidf = FALSE, ...)

## S4 method for signature 'data.frame'
docTermMatrix(obj, terms = "token", case.sens = FALSE,
      tfidf = FALSE)

## S4 method for signature 'kRp.text'
docTermMatrix(obj, terms = "token", case.sens = FALSE, tfidf = FALSE)

Arguments

`obj`	Either an object of class `kRp.text`, or a TIF[1] compliant token data frame.
`terms`	A character string defining the `tokens` column to be used for calculating the matrix.
`case.sens`	Logical, whether terms should be counted case sensitive.
`tfidf`	Logical, if `TRUE` calculates term frequency–inverse document frequency (tf-idf) values instead of absolute frequency.
`...`	Additional arguments depending on the particular method.

Details

This is usually more interesting if done with more than one single text. If you're interested in full corpus analysis, the tm.plugin.koRpus package should be worth checking out. Alternatively, a data frame with multiple doc_id entries can be used.

See the examples to learn how to limit the analysis to desired word classes.

Value

A sparse matrix of class dgCMatrix.

References

[1] Text Interchange Formats (https://github.com/ropensci/tif) [2] tm.plugin.koRpus: https://CRAN.R-project.org/package=tm.plugin.koRpus

Examples

# code is only run when the english language package can be loaded
if(require("koRpus.lang.en", quietly = TRUE)){
  sample_file <- file.path(
    path.package("koRpus"), "examples", "corpus", "Reality_Winner.txt"
  )
  # of course this makes more sense with a corpus of
  # multiple texts, see the tm.plugin.koRpus[2] package
  # for that
  tokenized.obj <- tokenize(
    txt=sample_file,
    lang="en"
  )
  # get the document-term frequencies in a sparse matrix
  myDTMatrix <- docTermMatrix(tokenized.obj)

  # combine with filterByClass() to, e.g.,  exclude all punctuation
  myDTMatrix <- docTermMatrix(filterByClass(tokenized.obj))

  # instead of absolute frequencies, get the tf-idf values
  myDTMatrix <- docTermMatrix(
    filterByClass(tokenized.obj),
    tfidf=TRUE
  )
} else {}

koRpus

Text Analysis with Emphasis on POS Tagging, Readability, and Lexical Diversity

v0.13-6

GPL (>= 3)

Authors

Meik Michalke [aut, cre], Earl Brown [ctb], Alberto Mirisola [ctb], Alexandre Brulet [ctb], Laura Hauser [ctb]

Initial release

2021-05-08