featureSet {DICEM}R Documentation

DICE Features

Description

Extracts feature sets to match pre-trained models

Usage

featureSet(text, parser = c("none", "spacy"), 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, the politeness features are excluded from the model.

num_mc_cores

integer Number of cores for parallelization. Default is 1, but we encourage users to try parallel::detectCores() if possible.

Details

The 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 features, matching the pre-trained model set


[Package DICEM version 0.1.0 Index]