Creates TfIdf(Latent semantic analysis) model.
The IDF is defined as follows:
idf = log(# documents in the corpus) /
(# documents where the term appears + 1)
Term Frequency Inverse Document Frequency
For usage details see Methods, Arguments and Examples sections.
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$new(smooth_idf = TRUE, norm = c("l1", "l2", "none"), sublinear_tf = FALSE)
Creates tf-idf model
fit model to an input sparse matrix (preferably in "dgCMatrix" format) and then transforms it.
transform new data
x using tf-idf from train data
An input term-co-occurence matrix. Preferably in
TRUE smooth IDF weights by adding one to document
frequencies, as if an extra document was seen containing every term in the
collection exactly once. This prevents division by zero.
c("l1", "l2", "none") Type of normalization to apply to term vectors.
"l1" by default, i.e., scale by the number of words in the document.
FALSE Apply sublinear term-frequency scaling, i.e.,
replace the term frequency with
1 + log(TF)
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