background function to load
dictWrap(
text,
dict = NULL,
binary = FALSE,
num_mc_cores = parallel::detectCores(),
...
)a matrix with six columns (one for each feature) and a row for every text entered into the function.
a character vector of texts.
a dictionary class object (see dictionary) containing dictionaries for six of the politeness features
return the prevalence (percent of words) or the presence (yes/no) of a feature in each text?
arguments passes onto the quanteda:dfm function