politeness: Politeness Features

In politeness: Detecting Politeness Features in Text

Politeness Features

Description

Detects linguistic markers of politeness in natural language. This function is the workhorse of the politeness package, taking an N-length vector of text documents and returning an N-row data.frame of feature counts.

Usage

politeness(
  text,
  parser = c("none", "spacy"),
  metric = c("count", "binary", "average"),
  drop_blank = FALSE,
  uk_english = FALSE,
  num_mc_cores = 1
)

Arguments

text	character A vector of texts, each of which will be tallied for politeness 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.
metric	character What metric to return? Raw feature count totals, Binary presence/absence of features, or feature counts per 100 words. Default is "count".
drop_blank	logical Should features that were not found in any text be removed from the data.frame? Default is FALSE
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.

Details

Some politeness features 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).

Value

a data.frame of politeness features, with one row for every item in 'text'. Possible politeness features are listed in feature_table

References

Brown, P., & Levinson, S. C. (1987). Politeness: Some universals in language usage (Vol. 4). Cambridge university press.

Danescu-Niculescu-Mizil, C., Sudhof, M., Jurafsky, D., Leskovec, J., & Potts, C. (2013). A computational approach to politeness with application to social factors. arXiv preprint arXiv:1306.6078.

Voigt, R., Camp, N. P., Prabhakaran, V., Hamilton, W. L., ... & Eberhardt, J. L. (2017). Language from police body camera footage shows racial disparities in officer respect. Proceedings of the National Academy of Sciences, 201702413.

Examples

data("phone_offers")

politeness(phone_offers$message, parser="none",drop_blank=FALSE)

colMeans(politeness(phone_offers$message, parser="none", metric="binary", drop_blank=FALSE))
colMeans(politeness(phone_offers$message, parser="none", metric="count", drop_blank=FALSE))

dim(politeness(phone_offers$message, parser="none",drop_blank=FALSE))
dim(politeness(phone_offers$message, parser="none",drop_blank=TRUE))

## Not run: 
# Detect multiple cores automatically for parallel processing
politeness(phone_offers$message, num_mc_cores=parallel::detectCores())

# Connect to SpaCy installation for part-of-speech features
install.packages("spacyr")
spacyr::spacy_initialize(python_executable = PYTHON_PATH)
politeness(phone_offers$message, parser="spacy",drop_blank=FALSE)


## End(Not run)

politeness documentation built on Nov. 12, 2023, 5:06 p.m.