koRpus (version 0.13-4)

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

Value

A sparse matrix of class dgCMatrix.

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.

References

[1] Text Interchange Formats (https://github.com/ropensci/tif)

Examples

# NOT RUN {
tokenized.obj <- tokenize(
  file.path(path.package("koRpus"), "tests", "testthat", "sample_text.txt")
)

# 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
)
# }