dfm_trim: Trim a dfm using frequency threshold-based feature selection

In quanteda: Quantitative Analysis of Textual Data

Trim a dfm using frequency threshold-based feature selection

Description

Returns a document by feature matrix reduced in size based on document and term frequency, usually in terms of a minimum frequency, but may also be in terms of maximum frequencies. Setting a combination of minimum and maximum frequencies will select features based on a range.

Feature selection is implemented by considering features across all documents, by summing them for term frequency, or counting the documents in which they occur for document frequency. Rank and quantile versions of these are also implemented, for taking the first n features in terms of descending order of overall global counts or document frequencies, or as a quantile of all frequencies.

Usage

dfm_trim(
  x,
  min_termfreq = NULL,
  max_termfreq = NULL,
  termfreq_type = c("count", "prop", "rank", "quantile"),
  min_docfreq = NULL,
  max_docfreq = NULL,
  docfreq_type = c("count", "prop", "rank", "quantile"),
  sparsity = NULL,
  verbose = quanteda_options("verbose")
)

Arguments

x	a dfm object
min_termfreq, max_termfreq	minimum/maximum values of feature frequencies across all documents, below/above which features will be removed
termfreq_type	how min_termfreq and max_termfreq are interpreted. "count" sums the frequencies; "prop" divides the term frequencies by the total sum; "rank" is matched against the inverted ranking of features in terms of overall frequency, so that 1, 2, ... are the highest and second highest frequency features, and so on; "quantile" sets the cutoffs according to the quantiles (see quantile()) of term frequencies.
min_docfreq, max_docfreq	minimum/maximum values of a feature's document frequency, below/above which features will be removed
docfreq_type	specify how min_docfreq and max_docfreq are interpreted. "count" is the same as ⁠[docfreq](x, scheme = "count")⁠; "prop" divides the document frequencies by the total sum; "rank" is matched against the inverted ranking of document frequency, so that 1, 2, ... are the features with the highest and second highest document frequencies, and so on; "quantile" sets the cutoffs according to the quantiles (see quantile()) of document frequencies.
sparsity	equivalent to 1 - min_docfreq, included for comparison with tm
verbose	print messages

Value

A dfm reduced in features (with the same number of documents)

Note

Trimming a dfm object is an operation based on the values in the document-feature matrix. To select subsets of a dfm based on the features themselves (meaning the feature labels from featnames()) – such as those matching a regular expression, or removing features matching a stopword list, use dfm_select().

See Also

dfm_select(), dfm_sample()

Examples

dfmat <- dfm(tokens(data_corpus_inaugural))

# keep only words occurring >= 10 times and in >= 2 documents
dfm_trim(dfmat, min_termfreq = 10, min_docfreq = 2)

# keep only words occurring >= 10 times and in at least 0.4 of the documents
dfm_trim(dfmat, min_termfreq = 10, min_docfreq = 0.4, docfreq_type = "prop")

# keep only words occurring <= 10 times and in <=2 documents
dfm_trim(dfmat, max_termfreq = 10, max_docfreq = 2)

# keep only words occurring <= 10 times and in at most 3/4 of the documents
dfm_trim(dfmat, max_termfreq = 10, max_docfreq = 0.75, docfreq_type = "prop")

# keep only words occurring 5 times in 1000, and in 2 of 5 of documents
dfm_trim(dfmat, min_docfreq = 0.4, min_termfreq = 0.005, termfreq_type = "prop")

## Not run: 
# compare to removeSparseTerms from the tm package
(dfmattm <- convert(dfmat, "tm"))
tm::removeSparseTerms(dfmattm, 0.7)
dfm_trim(dfmat, min_docfreq = 0.3)
dfm_trim(dfmat, sparsity = 0.7)

## End(Not run)

quanteda documentation built on Sept. 11, 2024, 6:08 p.m.