fdm2id (version 1.0.1)

vectorize.docs: Document vectorization

Description

Vectorize a corpus of documents.

Usage

vectorize.docs(
  vectorizer = NULL,
  corpus = NULL,
  lang = "en",
  stopwords = lang,
  excludewords = NULL,
  ngram = 1,
  mincount = 10,
  minphrasecount = NULL,
  transform = c("tfidf", "lsa", "l1", "none"),
  latentdim = 50,
  returndata = TRUE,
  removesinglechars = TRUE,
  sparse = FALSE,
  ...
)

Value

The vectorized documents, as a data.frame or, if sparse is TRUE, as a sparse matrix.

Arguments

vectorizer

The document vectorizer.

corpus

The corpus of documents (a vector of characters).

lang

The language of the documents (NULL if no stemming).

stopwords

The language whose stop words are removed ("en", ...), or NULL to keep them. A list of words of your own goes to excludewords.

excludewords

An optional custom vector of additional words to exclude from the vocabulary (e.g. corpus-specific stop words), on top of (or instead of) the language stopwords given through stopwords.

ngram

maximum size of n-grams.

mincount

Minimum word count to be considered as frequent.

minphrasecount

Minimum collocation of words count to be considered as frequent.

transform

Transformation (TF-IDF, LSA, L1 normanization, or nothing).

latentdim

Number of latent dimensions if LSA transformation is performed.

returndata

If true, the vectorized documents are returned. If false, a "vectorizer" is returned.

removesinglechars

Whether single-character tokens are removed during cleanup.

sparse

Whether the document-term matrix is returned as a sparse matrix (dgCMatrix) rather than as an ordinary data.frame. A document-term matrix is mostly zeros, and storing them all takes about 4.5 GB for 20000 documents and 30000 terms, so anything but a small corpus needs TRUE. Every method of the package accepts either.

...

Other parameters.

See Also

query.docs, stopwords, vectorizers

Examples

# \donttest{
require (text2vec)
# A small subset of movie_review is used here so this example runs quickly.
data ("movie_review")
reviews = movie_review [1:300, ]
# Clustering
docs = vectorize.docs (corpus = reviews$review, transform = "tfidf")
km = KMEANS (docs [sample (nrow (docs), 50), ], k = 10)
# Classification
d = reviews [, 2:3]
d [, 1] = factor (d [, 1])
d = splitdata (d, 1)
vectorizer = vectorize.docs (corpus = d$train.x,
                             returndata = FALSE, mincount = 10)
train = vectorize.docs (corpus = d$train.x, vectorizer = vectorizer)
test = vectorize.docs (corpus = d$test.x, vectorizer = vectorizer)
model = NB (as.matrix (train), d$train.y)
pred = predict (model, as.matrix (test))
evaluation (pred, d$test.y)
# }