Tokenize a set of texts and compute a term frequency matrix or data frame.
term_matrix(x, filter = text_filter(x), weights = NULL,
ngrams = NULL, select = NULL, group = NULL,
transpose = FALSE) term_frame(x, filter = text_filter(x), weights = NULL,
ngrams = NULL, select = NULL, group = NULL)
a text vector to tokenize.
a token filter specifying the tokenization rules.
a numeric vector the same length of x assigning
weights to each text, or NULL for unit weights.
an integer vector of n-gram lengths to include, or
NULL to use the select argument to determine the
n-gram lengths.
a character vector of terms to count, or NULL to
count all terms that appear in x.
if non-NULL, a factor, character string, or
integer vector the same length of x specifying the grouping
behavior.
a logical value indicating whether to transpose the result, putting terms as rows instead of columns.
term_matrix with transpose = FALSE returns a sparse matrix
in "dgCMatrix" format with one column for each term and one row for
each input text or (if group is non-NULL) for each grouping
level. If filter$select is non-NULL, then the column names
will be equal to filter$select. Otherwise, the columns are assigned
in arbitrary order.
term_matrix with transpose = TRUE returns the transpose of
the term matrix, in "dgCMatrix" format.
term_frame with group = NULL returns a data frame with one
row for each entry of the term matrix, and columns "text",
"term", and "count" giving the text ID, term, and count.
The "term" column is a character vector. The "text"
column is a factor with levels equal to names(as_text(x));
calling as.integer on the "text" column converts from
the factor values to the integer row index in the term matrix.
term_frame with group non-NULL behaves similarly,
but the result instead has columns named "group", "term",
and "count", with "group" giving the grouping level, as
a factor.
term_matrix tokenizes a set of texts and computes the occurrence
counts for each term. If weights is non-NULL, then each
token in text i increments the count for the corresponding terms
by weights[i]; otherwise, each appearance increments the count
by one.
If ngrams is non-NULL, then multi-type n-grams are
included in the output for all lengths appearing in the ngrams
argument. If ngrams is NULL but select is
non-NULL, then all n-grams appearing in the select set
are included. If both ngrams and select are NULL,
then only unigrams (single type terms) are included.
If group is NULL, then the output has one set of term
counts for each input text. Otherwise, we convert group to
a factor and compute one set of term counts for each level.
Texts with NA values for group get skipped.
# NOT RUN {
text <- c("A rose is a rose is a rose.",
"A Rose is red, a violet is blue!",
"A rose by any other name would smell as sweet.")
term_matrix(text)
# select certain terms
term_matrix(text, select = c("rose", "red", "violet", "sweet"))
# specify a grouping factor
term_matrix(text, group = c("Good", "Bad", "Good"))
# weight the texts
term_matrix(text, weights = c(1, 2, 10),
group = c("Good", "Bad", "Good"))
# include higher-order n-grams
term_matrix(text, ngrams = 1:3)
# select certain multi-type terms
term_matrix(text, select = c("a rose", "a violet", "sweet", "smell"))
# transpose the result
term_matrix(text, ngrams = 1:2, transpose = TRUE)[1:10, ] # first 10 rows
# data frame
head(term_frame(text), n = 10) # first 10 rows
# with grouping
term_frame(text, group = c("Good", "Bad", "Good"))
# taking names from the input
term_frame(c(a = "One sentence.", b = "Another", c = "!!"))
# }
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