create_dtm: Document-term matrix construction

Description

This is a high-level function for creating a document-term matrix.

Usage

create_dtm(it, vectorizer, type = c("dgCMatrix", "dgTMatrix",
  "RsparseMatrix"), ...)
# S3 method for itoken
create_dtm(it, vectorizer, type = c("dgCMatrix",
  "dgTMatrix", "RsparseMatrix"), ...)
# S3 method for itoken_parallel
create_dtm(it, vectorizer,
  type = c("dgCMatrix", "dgTMatrix", "RsparseMatrix"), ...)

Arguments

itoken iterator or list of itoken iterators.

vectorizer

function vectorizer function; see vectorizers.

type

character, one of c("dgCMatrix", "dgTMatrix").

...

placeholder for additional arguments (not used at the moment). over it.

Value

A document-term matrix

Details

If a parallel backend is registered and first argument is a list of itoken, iterators, function will construct the DTM in multiple threads. User should keep in mind that he or she should split the data itself and provide a list of itoken iterators. Each element of it will be handled in separate thread and combined at the end of processing.

Examples

Run this code

# NOT RUN {
data("movie_review")
N = 1000
it = itoken(movie_review$review[1:N], preprocess_function = tolower,
             tokenizer = word_tokenizer)
v = create_vocabulary(it)
#remove very common and uncommon words
pruned_vocab = prune_vocabulary(v, term_count_min = 10,
 doc_proportion_max = 0.5, doc_proportion_min = 0.001)
vectorizer = vocab_vectorizer(v)
it = itoken(movie_review$review[1:N], preprocess_function = tolower,
             tokenizer = word_tokenizer)
dtm = create_dtm(it, vectorizer)
# get tf-idf matrix from bag-of-words matrix
dtm_tfidf = transformer_tfidf(dtm)

## Example of parallel mode
it = token_parallel(movie_review$review[1:N], tolower, word_tokenizer, movie_review$id[1:N])
vectorizer = hash_vectorizer()
dtm = create_dtm(it, vectorizer, type = 'dgTMatrix')
# }