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Creates TfIdf(Latent semantic analysis) model.
The IDF is defined as follows: idf = log(# documents in the corpus) /
(# documents where the term appears + 1)
TfIdfR6Class object.
For usage details see Methods, Arguments and Examples sections.
tfidf = TfIdf$new(smooth_idf = TRUE, norm = c('l1', 'l2', 'none'), sublinear_tf = FALSE)
tfidf$fit_transform(x)
tfidf$transform(x)
$new(smooth_idf = TRUE, norm = c("l1", "l2", "none"), sublinear_tf = FALSE)Creates tf-idf model
$fit_transform(x)fit model to an input sparse matrix (preferably in "dgCMatrix" format) and then transforms it.
$transform(x)transform new data x using tf-idf from train data
A TfIdf object
An input term-co-occurence matrix. Preferably in dgCMatrix format
TRUE smooth IDF weights by adding one to document
frequencies, as if an extra document was seen containing every term in the
collection exactly once. This prevents division by zero.
c("l1", "l2", "none") Type of normalization to apply to term vectors.
"l1" by default, i.e., scale by the number of words in the document.
FALSE Apply sublinear term-frequency scaling, i.e.,
replace the term frequency with 1 + log(TF)
Term Frequency Inverse Document Frequency
# NOT RUN {
data("movie_review")
N = 100
tokens = word_tokenizer(tolower(movie_review$review[1:N]))
dtm = create_dtm(itoken(tokens), hash_vectorizer())
model_tfidf = TfIdf$new()
dtm_tfidf = model_tfidf$fit_transform(dtm)
# }
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