politeness (version 0.2.2)

politeness: 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"), binary = FALSE,
  drop_blank = TRUE, 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.

binary

logical Return a binary indicator for the presence of a feature instead of total counts? Default is TRUE

drop_blank

logical Should features that were not found in any text be removed from the data.frame? Default is TRUE

num_mc_cores

integer Number of cores for parallelization. Default is parallel::detectCores().

Value

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

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).

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

# NOT RUN {
data("phone_offers")

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

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

# }
# NOT RUN {



# }