quanteda (version 1.5.2)

docfreq: Compute the (weighted) document frequency of a feature

Description

For a dfm object, returns a (weighted) document frequency for each term. The default is a simple count of the number of documents in which a feature occurs more than a given frequency threshold. (The default threshold is zero, meaning that any feature occurring at least once in a document will be counted.)

Usage

docfreq(
  x,
  scheme = c("count", "inverse", "inversemax", "inverseprob", "unary"),
  smoothing = 0,
  k = 0,
  base = 10,
  threshold = 0,
  use.names = TRUE
)

Arguments

x

a dfm

scheme

type of document frequency weighting, computed as follows, where \(N\) is defined as the number of documents in the dfm and \(s\) is the smoothing constant:

count

\(df_j\), the number of documents for which \(n_{ij} > threshold\)

inverse

$$\textrm{log}_{base}\left(s + \frac{N}{k + df_j}\right)$$

inversemax

$$\textrm{log}_{base}\left(s + \frac{\textrm{max}(df_j)}{k + df_j}\right)$$

inverseprob

$$\textrm{log}_{base}\left(\frac{N - df_j}{k + df_j}\right)$$

unary

1 for each feature

smoothing

added to the quotient before taking the logarithm

k

added to the denominator in the "inverse" weighting types, to prevent a zero document count for a term

base

the base with respect to which logarithms in the inverse document frequency weightings are computed; default is 10 (see Manning, Raghavan, and Sch<U+00FC>tze 2008, p123).

threshold

numeric value of the threshold above which a feature will considered in the computation of document frequency. The default is 0, meaning that a feature's document frequency will be the number of documents in which it occurs greater than zero times.

use.names

logical; if TRUE attach feature labels as names of the resulting numeric vector

...

not used

Value

a numeric vector of document frequencies for each feature

References

Manning, C. D., Raghavan, P., & Sch<U+00FC>tze, H. (2008). Introduction to Information Retrieval. Cambridge: Cambridge University Press. https://nlp.stanford.edu/IR-book/pdf/irbookonlinereading.pdf

Examples

# NOT RUN {
dfmat1 <- dfm(data_corpus_inaugural[1:2])
docfreq(dfmat1[, 1:20])

# replication of worked example from
# https://en.wikipedia.org/wiki/Tf-idf#Example_of_tf.E2.80.93idf
dfmat2 <- 
    matrix(c(1,1,2,1,0,0, 1,1,0,0,2,3),
           byrow = TRUE, nrow = 2,
           dimnames = list(docs = c("document1", "document2"),
                           features = c("this", "is", "a", "sample",
                                        "another", "example"))) %>%
    as.dfm()
dfmat2
docfreq(dfmat2)
docfreq(dfmat2, scheme = "inverse")
docfreq(dfmat2, scheme = "inverse", k = 1, smoothing = 1)
docfreq(dfmat2, scheme = "unary")
docfreq(dfmat2, scheme = "inversemax")
docfreq(dfmat2, scheme = "inverseprob")
# }