polmineR (version 0.7.9)

features,partition-method: Get features by comparison.

Description

The features of two objects, usually a partition defining a corpus of interest (coi), and a partition defining a reference corpus (ref) are compared. The most important purpose is term extraction.

Usage

# S4 method for partition
features(x, y, included = FALSE, method = "chisquare",
  verbose = FALSE)
# S4 method for count
features(x, y, by = NULL, included = FALSE,
  method = "chisquare", verbose = TRUE)
# S4 method for partition_bundle
features(x, y, included = FALSE,
  method = "chisquare", verbose = TRUE, mc = getOption("polmineR.mc"),
  progress = FALSE)
# S4 method for ngrams
features(x, y, included = FALSE, method = "chisquare",
  verbose = TRUE, ...)

Arguments

x

A partition or partition_bundle object.

y

A partition object, it is assumed that the coi is a subcorpus of ref

included

TRUE if coi is part of ref, defaults to FALSE

method

the statistical test to apply (chisquare or log likelihood)

verbose

A logical value, defaults to TRUE

by

the columns used for merging, if NULL (default), the p-attribute of x will be used

mc

logical, whether to use multicore

progress

logical

...

further parameters

References

Baker, Paul (2006): Using Corpora in Discourse Analysis. London: continuum, p. 121-149 (ch. 6).

Manning, Christopher D.; Schuetze, Hinrich (1999): Foundations of Statistical Natural Language Processing. MIT Press: Cambridge, Mass., pp. 151-189 (ch. 5).

Examples

# NOT RUN {
use("polmineR")

kauder <- partition(
  "GERMAPARLMINI",
  speaker = "Volker Kauder", interjection = "speech",
  p_attribute = "word"
  )
all <- partition("GERMAPARLMINI", interjection = "speech", p_attribute = "word")

terms_kauder <- features(x = kauder, y = all, included = TRUE)
top100 <- subset(terms_kauder, rank_chisquare <= 100)
head(top100)

# a different way is to compare count objects
kauder_count <- as(kauder, "count")
all_count <- as(all, "count")
terms_kauder <- features(kauder_count, all_count, included = TRUE)
top100 <- subset(terms_kauder, rank_chisquare <= 100)
head(top100)

speakers <- partition_bundle("GERMAPARLMINI", s_attribute = "speaker")
speakers <- enrich(speakers, p_attribute = "word")
speaker_terms <- features(speakers[[1:5]], all, included = TRUE, progress = TRUE)
dtm <- as.DocumentTermMatrix(speaker_terms, col = "chisquare")
# }