Calculate "keyness", a score for features that occur differentially across different categories. Here, the categories are defined by reference to a "target" document index in the dfm, with the reference group consisting of all other documents.
textstat_keyness(x, target = 1L, measure = c("chi2", "exact", "lr",
"pmi"), sort = TRUE, correction = c("default", "yates", "williams",
"none"))a dfm containing the features to be examined for keyness
the document index (numeric, character or logical) identifying the document forming the "target" for computing keyness; all other documents' feature frequencies will be combined for use as a reference
(signed) association measure to be used for computing keyness.
Currently available: "chi2"; "exact" (Fisher's exact test);
"lr" for the likelihood ratio; "pmi" for pointwise mutual
information.
logical; if TRUE sort features scored in descending order
of the measure, otherwise leave in original feature order
if "default", Yates correction is applied to
"chi2"; William's correction is applied to "lr"; and no
correction is applied for the "exact" and "pmi" measures.
Specifying a value other than the default can be used to override the
defaults, for instance to apply the Williams correction to the chi2
measure. Specifying a correction for the "exact" and "pmi"
measures has no effect and produces a warning.
a data.frame of computed statistics and associated p-values, where
the features scored name each row, and the number of occurrences for both
the target and reference groups. For measure = "chi2" this is the
chi-squared value, signed positively if the observed value in the target
exceeds its expected value; for measure = "exact" this is the
estimate of the odds ratio; for measure = "lr" this is the
likelihood ratio "pmi" this is the pointwise
mutual information statistics.
textstat_keyness returns a data.frame of features and
their keyness scores and frequency counts.
Bondi, Marina, and Mike Scott, eds. 2010. Keyness in Texts. Amsterdam, Philadelphia: John Benjamins, 2010.
Stubbs, Michael. 2010. "Three Concepts of Keywords". In Keyness in Texts, Marina Bondi and Mike Scott, eds. pp21<U+2013>42. Amsterdam, Philadelphia: John Benjamins.
Scott, M. & Tribble, C. 2006. Textual Patterns: keyword and corpus analysis in language education. Amsterdam: Benjamins, p. 55.
Dunning, Ted. 1993. "Accurate Methods for the Statistics of Surprise and Coincidence", Computational Linguistics, Vol 19, No. 1, pp. 61-74.
# NOT RUN {
# compare pre- v. post-war terms using grouping
period <- ifelse(docvars(data_corpus_inaugural, "Year") < 1945, "pre-war", "post-war")
mydfm <- dfm(data_corpus_inaugural, groups = period)
head(mydfm) # make sure 'post-war' is in the first row
head(result <- textstat_keyness(mydfm), 10)
tail(result, 10)
# compare pre- v. post-war terms using logical vector
mydfm2 <- dfm(data_corpus_inaugural)
textstat_keyness(mydfm2, docvars(data_corpus_inaugural, "Year") >= 1945)
# compare Trump 2017 to other post-war preseidents
pwdfm <- dfm(corpus_subset(data_corpus_inaugural, period == "post-war"))
head(textstat_keyness(pwdfm, target = "2017-Trump"), 10)
# using the likelihood ratio method
head(textstat_keyness(dfm_smooth(pwdfm), measure = "lr", target = "2017-Trump"), 10)
# }
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