Politeness is a universal dimension of langauge (Lakoff, 1973; Brown & Levinson, 1987). In practically all communication, a speaker can choose to be more or less polite to their audience. In this package, we provide tools for researchers to measure the markers and effects of politeness in natural language.


You can install politeness directly, like so:


Many of the politeness features containted in this package use dependency parsing. We rely on the popular python library SpaCy, which is simple to install, although the procedure can vary for different machines. Our software depends on a convenient wrapper function, spacyr, that also includes several intallation tools. If you do not have SpaCy installed, a reduced set of features is provided (i.e. those that do not require dependency tags) but this only recommended for initial tests, rather than final analyses.

Please confirm that your machine can run SpaCy and spacyr first! This step can be difficult for some machines, and we defer to the existing documentation for that software as a guide.

If you want to try out this package without having to configure SpaCy on your own machine, I have written a tutorial that will let you analyse the data on Colab. This will install everything you need automatically (though it does take ~ 20 minutes for everything to run). However, for people unfamiliar with python, it can be much easier to use colab to start, than to configure their own computer.


If you find this package useful, please cite us, using the following reference from our R Journal publication.

Yeomans, M., Kantor, A. & Tingley, D. (2018). Detecting Politeness in Natural Language. The R Journal, 10(2), 489-502.

Note that this publication was written using a very early version (0.2.4) of the package. For the most up-to-date description of the functionality, please see the vignette in this repository.


In the package we have included a dataset, phone_offers. It was collected from an experiment in which participants were told to write offers asking to buy a smartphone from a seller on Craigslist. We randomly manipulated their instructions so that their task was to write in a warm or tough style. The dataset includes the text of their messages, as well as the condition assignment.



# install.packages("spacyr")
#spacyr::spacy_initialize(python_executable = "PYTHON_PATH")
#politeness::politeness(phone_offers$message, parser="spacy")

                           split_levels = c("Warm","Tough"),
                           split_name = "Condition")