DICE {DICEM} | R Documentation |
Detects linguistic markers of politeness in natural language. Takes an N-length vector of text documents and returns an N-row data.frame of scores on the two DICE dimensions.
DICE(text, parser = c("none", "spacy"), uk_english = FALSE, num_mc_cores = 1)
text |
character A vector of texts, each of which will be tallied for DICE features. |
parser |
character Name of dependency parser to use (see details). Without a dependency parser, some features will be approximated, while others cannot be calculated at all. |
uk_english |
logical Does the text contain any British English spelling? Including variants (e.g. Canadian). Default is FALSE |
num_mc_cores |
integer Number of cores for parallelization. Default is 1, but we encourage users to try parallel::detectCores() if possible. |
The best intensity model uses politeness features, which depend on part-of-speech tagged sentences (e.g. "bare commands" are a particular verb class). To include these features in the analysis, a POS tagger must be initialized beforehand - we currently support SpaCy which must be installed separately in Python (see example for implementation). If not, a simpler model can be used, though it is somewhat less accruate.
a data.frame of scores on directness and intensity.
Weingart et al., 2015 Yeomans et al., 2024
data("phone_offers")
DICE(phone_offers$message[1:10], parser="none")
## Not run:
# Detect multiple cores automatically for parallel processing
DICE(phone_offers$message, num_mc_cores=parallel::detectCores())
# Connect to SpaCy installation for part-of-speech features
# THIS REQUIRES SPACY INSTALLATION OUTSIDE OF R
# For some machines, spacyr::spacy_install() will work, but please confirm before running
spacyr::spacy_initialize(python_executable = PYTHON_PATH)
DICE(phone_offers$message, parser="spacy")
## End(Not run)