question_type(text.var, grouping.var = NULL, neg.cont = FALSE, percent = TRUE, zero.replace = 0, digits = 2, contraction = qdapDictionaries::contractions, bracket = "all", amplifiers = qdapDictionaries::amplification.words, ...)
NULL generates
one word list for all text. Also takes a single grouping variable or a list
of 1 or more grouping variables.TRUE provides separate counts for the
negative contraction forms of the interrogative words.TRUE output given as percent. If
FALSE the output is proportion.contractions data set."curly", "square", "round",
"angle" and "all". These strings correspond
to: {, [, (, < or all four types.amplification.words data
set.bracketX.Returns a list of:
rawA dataframe of the questions used in the transcript and their
type.
countA dataframe of total questions (tot.quest) and counts of
question types (initial interrogative word) by grouping variable(s).
rnpDataframe of the frequency and proportions of question types by
grouping variable.
indsThe indices of the original text variable that contain questions.
missingThe row numbers of the missing data (excluded from analysis).
percentThe value of percent used for plotting purposes.
zero.replaceThe value of zero.replace used for plotting purposes.
Transcript apply question counts.
The algorithm searches for the following interrogative words (and optionally, their negative contraction form as well):
1) whose 2) whom 3) who 4) where 5) what 6) which 7) why 8) when 9) were* 10) was* 11) does* 12) did* 13) do* 14) is 15) are* 16) will* 17) how 18) should 19) could 20) would* 21) shall 22) may 23) might* 24) must* 25) can* 26) has 27) have* 28) had* 29) ok 30) right 31) correct 32) implied do/does/did
The interrogative word that is found first (with the exception of "ok", "right"/"alright", and "correct") in the question determines the sentence type. "ok", "right"/"alright", and "correct" sentence types are determined if the sentence is a question with no other interrogative words found and "ok", "right"/"alright", or "correct" is the last word of the sentence. Those interrogative sentences beginning with the word "you", "wanna", or "want" are categorized as implying do/does/did question type, though the use of do/does/did is not explicit. Those sentence beginning with "you" followed by a select interrogative word (and or their negative counter parts) above (marked with *) or 1-2 amplifier(s) followed by the select interrogative word are categorized by the select word rather than an implied do/does/did question type. A sentence that is marked "ok" over rides an implied do/does/did label. Those with undetermined sentence type are labeled unknown.
## <strong>Not run</strong>: # ## Inspect the algorithm classification # x <- c("Kate's got no appetite doesn't she?", # "Wanna tell Daddy what you did today?", # "You helped getting out a book?", "umm hum?", # "Do you know what it is?", "What do you want?", # "Who's there?", "Whose?", "Why do you want it?", # "Want some?", "Where did it go?", "Was it fun?") # # left_just(preprocessed(question_type(x))[, c(2, 6)]) # # ## Transcript/dialogue examples # (x <- question_type(DATA.SPLIT$state, DATA.SPLIT$person)) # # ## methods # scores(x) # plot(scores(x)) # counts(x) # plot(counts(x)) # proportions(x) # plot(proportions(x)) # truncdf(preprocessed(x), 15) # plot(preprocessed(x)) # # plot(x) # plot(x, label = TRUE) # plot(x, label = TRUE, text.color = "red") # question_type(DATA.SPLIT$state, DATA.SPLIT$person, percent = FALSE) # DATA[8, 4] <- "Won't I distrust you?" # question_type(DATA.SPLIT$state, DATA.SPLIT$person) # DATA <- qdap::DATA # with(DATA.SPLIT, question_type(state, list(sex, adult))) # # out1 <- with(mraja1spl, question_type(dialogue, person)) # ## out1 # out2 <- with(mraja1spl, question_type(dialogue, list(sex, fam.aff))) # ## out2 # out3 <- with(mraja1spl, question_type(dialogue, list(sex, fam.aff), # percent = FALSE)) # plot(out3, label = TRUE, lab.digits = 3) # ## <strong>End(Not run)</strong>
colcomb2class,
bracketX