tfidf: compute tf-idf weights from a dfm

In quanteda: Quantitative Analysis of Textual Data

Description

Compute tf-idf, inverse document frequency, and relative term frequency on document-feature matrices. See also weight.

Usage

tfidf(x, normalize = FALSE, scheme = "inverse", ...)

Arguments

x	object for which idf or tf-idf will be computed (a document-feature matrix)
normalize	if TRUE, use relative term frequency
scheme	scheme for docfreq
...	additional arguments passed to docfreq when calling tfidf

Details

tfidf computes term frequency-inverse document frequency weighting. The default is not to normalize term frequency (by computing relative term frequency within document) but this will be performed if normalize = TRUE.

References

Manning, C. D., Raghavan, P., & Schutze, H. (2008). Introduction to Information Retrieval. Cambridge University Press.

Examples

head(data_dfm_LBGexample[, 5:10])
head(tfidf(data_dfm_LBGexample)[, 5:10])
docfreq(data_dfm_LBGexample)[5:15]
head(tf(data_dfm_LBGexample)[, 5:10])

# replication of worked example from
# https://en.wikipedia.org/wiki/Tf-idf#Example_of_tf.E2.80.93idf
(wikiDfm <- new("dfmSparse", 
                Matrix::Matrix(c(1,1,2,1,0,0, 1,1,0,0,2,3),
                   byrow = TRUE, nrow = 2,  
                   dimnames = list(docs = c("document1", "document2"), 
                     features = c("this", "is", "a", "sample", "another",
                                  "example")), sparse = TRUE)))
docfreq(wikiDfm)
tfidf(wikiDfm)

quanteda documentation built on May 26, 2017, 9:02 a.m.

compute (weighted) term frequency from a dfm

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