Weight a dfm by term frequency-inverse document frequency (tf-idf) using fully sparse methods.

tfidf(x, scheme_tf = "prop", scheme_df = "inverse", base = 10, ...)

Arguments

x

object for which idf or tf-idf will be computed (a document-feature matrix)

scheme_tf

scheme for tf; defaults to "count"

scheme_df

scheme for link{docfreq}; defaults to "inverse"

base

for the logarithms in the tf and docfreq calls

...

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 scheme_tf = "prop".

References

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

See also

tf, docfreq

Examples

head(data_dfm_lbgexample[, 5:10])
#> Document-feature matrix of: 6 documents, 6 features (61.1% sparse). #> (showing first 6 documents and first 6 features) #> features #> docs E F G H I J #> R1 45 78 115 146 158 146 #> R2 0 2 3 10 22 45 #> R3 0 0 0 0 0 0 #> R4 0 0 0 0 0 0 #> R5 0 0 0 0 0 0 #> V1 0 0 0 2 3 10
head(tfidf(data_dfm_lbgexample)[, 5:10])
#> Document-feature matrix of: 6 documents, 6 features (61.1% sparse). #> (showing first 6 documents and first 6 features) #> features #> docs E F G H I J #> R1 35.01681 37.2154579 54.868944 43.95038 47.56274 43.95038 #> R2 0.00000 0.9542425 1.431364 3.01030 6.62266 13.54635 #> R3 0.00000 0.0000000 0.000000 0.00000 0.00000 0.00000 #> R4 0.00000 0.0000000 0.000000 0.00000 0.00000 0.00000 #> R5 0.00000 0.0000000 0.000000 0.00000 0.00000 0.00000 #> V1 0.00000 0.0000000 0.000000 0.60206 0.90309 3.01030
docfreq(data_dfm_lbgexample)[5:15]
#> E F G H I J K L M N O #> 1 2 2 3 3 3 4 4 4 4 4
head(tf(data_dfm_lbgexample)[, 5:10])
#> Document-feature matrix of: 6 documents, 6 features (61.1% sparse). #> (showing first 6 documents and first 6 features) #> features #> docs E F G H I J #> R1 45 78 115 146 158 146 #> R2 0 2 3 10 22 45 #> R3 0 0 0 0 0 0 #> R4 0 0 0 0 0 0 #> R5 0 0 0 0 0 0 #> V1 0 0 0 2 3 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)))
#> Document-feature matrix of: 2 documents, 6 features (33.3% sparse). #> 2 x 6 sparse Matrix of class "dfmSparse" #> features #> docs this is a sample another example #> document1 1 1 2 1 0 0 #> document2 1 1 0 0 2 3
docfreq(wikiDfm)
#> this is a sample another example #> 2 2 1 1 1 1
tfidf(wikiDfm)
#> Document-feature matrix of: 2 documents, 6 features (33.3% sparse). #> 2 x 6 sparse Matrix of class "dfmSparse" #> features #> docs this is a sample another example #> document1 0 0 0.60206 0.30103 0 0 #> document2 0 0 0 0 0.60206 0.90309