Description Usage Arguments Examples
Tfidf re-weighting of dtm
and tdm
matrices
1 2 |
mat |
output of |
vocab |
output of |
norm |
normalization to apply for each document. Either "l1", "l2" or "none" |
sublinear_tf |
when |
extra_df_count |
add this number to the document count; as if all terms in the vocabulary have been seen at least in this many documents. |
1 2 3 4 5 6 7 8 | corpus <- list(a = c("The", "quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog"),
b = c("the", "quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog",
"the", "quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog"))
v <- vocab(corpus, c(1, 2), " ")
dtm <- dtm(corpus, v)
tfidf(dtm, v)
tdm <- tdm(corpus, v)
tfidf(tdm, v)
|
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