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. Note that
the "exact" test is very computationally intensive and therefore much
slower than the other methods.
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.
not used
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, M. & Scott, M. (eds) (2010). Keyness in Texts. Amsterdam, Philadelphia: John Benjamins.
Stubbs, M. (2010). Three Concepts of Keywords. In Keyness in Texts, Bondi, M. & Scott, M. (eds): 1--42. Amsterdam, Philadelphia: John Benjamins.
Scott, M. & Tribble, C. (2006). Textual Patterns: Keyword and Corpus Analysis in Language Education. Amsterdam: Benjamins: 55.
Dunning, T. (1993). Accurate Methods for the Statistics of Surprise and Coincidence. Computational Linguistics, 19(1): 61--74.
# NOT RUN {
# compare pre- v. post-war terms using grouping
period <- ifelse(docvars(data_corpus_inaugural, "Year") < 1945, "pre-war", "post-war")
dfmat1 <- dfm(data_corpus_inaugural, groups = period)
head(dfmat1) # make sure 'post-war' is in the first row
head(tstat1 <- textstat_keyness(dfmat1), 10)
tail(tstat1, 10)
# compare pre- v. post-war terms using logical vector
dfmat2 <- dfm(data_corpus_inaugural)
head(textstat_keyness(dfmat2, docvars(data_corpus_inaugural, "Year") >= 1945), 10)
# compare Trump 2017 to other post-war preseidents
dfmat3 <- dfm(corpus_subset(data_corpus_inaugural, period == "post-war"))
head(textstat_keyness(dfmat3, target = "2017-Trump"), 10)
# using the likelihood ratio method
head(textstat_keyness(dfm_smooth(dfmat3), measure = "lr", target = "2017-Trump"), 10)
# }
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