corpustools (version 0.5.1)

get_dtm: Create a document term matrix.

Description

Create a document term matrix. The default output is a sparse matrix (Matrix, TsparseMatrix). Alternatively, the dtm style from the tm and quanteda package can be used.

The dfm function is shorthand for using quanteda's dfm (document feature matrix) class. The meta data in the tcorpus is then automatically added as docvars in the dfm.

Usage

get_dtm(
  tc,
  feature,
  context_level = c("document", "sentence"),
  weight = c("termfreq", "docfreq", "tfidf", "norm_tfidf"),
  drop_empty_terms = T,
  form = c("Matrix", "tm_dtm", "quanteda_dfm"),
  subset_tokens = NULL,
  subset_meta = NULL,
  context = NULL,
  context_labels = T,
  feature_labels = T,
  ngrams = NA,
  ngram_before_subset = F
)
get_dfm(
  tc,
  feature,
  context_level = c("document", "sentence"),
  weight = c("termfreq", "docfreq", "tfidf", "norm_tfidf"),
  drop_empty_terms = T,
  subset_tokens = NULL,
  subset_meta = NULL,
  context = NULL,
  context_labels = T,
  feature_labels = T,
  ngrams = NA,
  ngram_before_subset = F
)

Value

A document term matrix, in the format specified in the form argument

Arguments

tc

a tCorpus

feature

The name of the feature column

context_level

Select whether the rows of the dtm should represent "documents" or "sentences".

weight

Select the weighting scheme for the DTM. Currently supports term frequency (termfreq), document frequency (docfreq), term frequency inverse document frequency (tfidf) and tfidf with normalized document vectors.

drop_empty_terms

If True, tokens that do not occur (i.e. column where sum is 0) are ignored.

form

The output format. Default is a sparse matrix in the dgTMatrix class from the Matrix package. Alternatives are tm_dtm for a DocumentTermMatrix in the tm package format or quanteda_dfm for the document feature matrix from the quanteda package.

subset_tokens

A subset call to select which rows to use in the DTM

subset_meta

A subset call for the meta data, to select which documents to use in the DTM

context

Instead of using the document or sentence context, an custom context can be specified. Has to be a vector of the same length as the number of tokens, that serves as the index column. Each unique value will be a row in the DTM.

context_labels

If False, the DTM will not be given rownames

feature_labels

If False, the DTM will not be given column names

ngrams

Optionally, use ngrams instead of individual tokens. This is more memory efficient than first creating an ngram feature in the tCorpus.

ngram_before_subset

If a subset is used, ngrams can be made before the subset, in which case an ngram can contain tokens that have been filtered out after the subset. Alternatively, if ngrams are made after the subset, ngrams will span over the gaps of tokens that are filtered out.

Examples

tc = create_tcorpus(c("First text first sentence. First text first sentence.",
                   "Second text first sentence"), doc_column = 'id', split_sentences = TRUE)

## Perform additional preprocessing on the 'token' column, and save as the 'feature' column
tc$preprocess('token', 'feature', remove_stopwords = TRUE, use_stemming = TRUE)
tc$tokens

## default: regular sparse matrix, using the Matrix package
m = get_dtm(tc, 'feature')
class(m)
m

## alternatively, create quanteda ('quanteda_dfm') or tm ('tm_dtm') class for DTM
# \donttest{
m = get_dtm(tc, 'feature', form = 'quanteda_dfm')
class(m)
m
# }

## create DTM with sentences as rows (instead of documents)
m = get_dtm(tc, 'feature', context_level = 'sentence')
nrow(m)

## use weighting
m = get_dtm(tc, 'feature', weight = 'norm_tfidf')