qdap (version 1.2.0)

formality: Formality Score

Description

Transcript apply formality score by grouping variable(s) and optionally plot the breakdown of the model.

Usage

formality(text.var, grouping.var = NULL, order.by.formality = TRUE,
  digits = 2, ...)

Arguments

text.var

The text variable (or an object from pos, pos_by or formality. Passing the later three ob

grouping.var

The grouping variables. Default NULL generates one word list for all text. Also takes a single grouping variable or a list of 1 or more grouping variables.

order.by.formality

logical. If TRUE orders the results by formality score.

digits

The number of digits displayed.

...

Other arguments passed to pos_by.

Value

• A list containing at the following components:
• text
  The text variable
• POStagged
  Raw part of speech for every word of the text variable
• POSprop
  Part of speech proportion for every word of the text variable
• POSfreq
  Part of speech count for every word of the text variable
• pos.by.freq
  The part of speech count for every word of the text variable by grouping variable(s)
• pos.by.prop
  The part of speech proportion for every word of the text variable by grouping variable(s)
• form.freq.by
  The nine broad part of speech categories count for every word of the text variable by grouping variable(s)
• form.prop.by
  The nine broad part of speech categories proportion for every word of the text variable by grouping variable(s)
• formality
  Formality scores by grouping variable(s)
• pos.reshaped
  An expanded formality scores output (grouping, word.count, pos & form.class) by word

Warning

Heylighen & Dewaele (2002) state, "At present, a sample would probably need to contain a few hundred words for the measure to be minimally reliable. For single sentences, the F-value should only be computed for purposes of illustration" (p. 24).

Details

Heylighen & Dewaele(2002)'s formality score is calculated as: $$F = 50(\frac{n_{f}-n_{c}}{N} + 1)$$ Where: $$f = \left {noun, \;adjective, \;preposition, \;article\right }$$ $$c = \left {pronoun, \;verb, \;adverb, \;interjection\right }$$ $$N = \sum{(f \;+ \;c \;+ \;conjunctions)}$$

References

Heylighen, F., & Dewaele, J.M. (2002). Variation in the contextuality of language: An empirical measure. Context in Context, Special issue of Foundations of Science, 7 (3), 293-340.

Examples

with(DATA, formality(state, person))
(x1 <- with(DATA, formality(state, list(sex, adult))))
plot(x1)
plot(x1, short.names = FALSE)

scores(x1)
counts(x1)
proportions(x1)
preprocessed(x1)

plot(scores(x1))
plot(counts(x1))
plot(proportions(x1), high="darkgreen")
plot(preprocessed(x1))

data(rajPOS) #A data set consisting of a pos list object
x2 <- with(raj, formality(rajPOS, act))
plot(x2)
x3 <- with(raj, formality(rajPOS, person))
plot(x3, bar.colors="Dark2")
plot(x3, bar.colors=c("Dark2", "Set1"))
x4 <- with(raj, formality(rajPOS, list(person, act)))
plot(x4, bar.colors=c("Dark2", "Set1"))

rajDEM <- key_merge(raj, raj.demographics) #merge demographics with transcript.
x5 <- with(rajDEM, formality(rajPOS, sex))
plot(x5, bar.colors="RdBu")
x6 <- with(rajDEM, formality(rajPOS, list(fam.aff, sex)))
plot(x6, bar.colors="RdBu")
x7 <- with(rajDEM, formality(rajPOS, list(died, fam.aff)))
plot(x7, bar.colors="RdBu",  point.cex=2, point.pch = 3)
x8 <- with(rajDEM, formality(rajPOS, list(died, sex)))
plot(x8, bar.colors="RdBu",  point.cex=2, point.pch = "|")

names(x8)
colsplit2df(x8$formality)

#pass an object from pos or pos_by
ltruncdf(with(raj, formality(x8 , list(act, person))), 6, 4)