| Type: | Package |
| Title: | Directness and Intensity of Conflict Expression |
| Version: | 0.1.0 |
| Description: | A Natural Language Processing Model trained to detect directness and intensity during conflict. See https://www.mikeyeomans.info. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| Depends: | R (≥ 3.5.0) |
| Imports: | politeness, stringr, doc2concrete, vader, Matrix, quanteda, xgboost |
| RoxygenNote: | 7.3.1 |
| Suggests: | knitr, spacyr, rmarkdown, testthat |
| NeedsCompilation: | no |
| Packaged: | 2024-08-16 13:30:39 UTC; myeomans |
| Author: | Michael Yeomans [aut, cre] |
| Maintainer: | Michael Yeomans <mk.yeomans@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2024-08-21 12:50:06 UTC |
DICE Model Scores
Description
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.
Usage
DICE(text, parser = c("none", "spacy"), uk_english = FALSE, num_mc_cores = 1)
Arguments
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. |
Details
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.
Value
a data.frame of scores on directness and intensity.
References
Weingart et al., 2015 Yeomans et al., 2024
Examples
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)
Basic Features
Description
Simple features as inputs to the DICE model
Usage
basicSet(text)
Arguments
text |
character A vector of texts, each of which will be tallied for DICE features. |
Details
The DICE models use, as features, linear and quadratic terms for sentiment, emotion, and word count.
Value
a data.frame of feature scores for the pre-trained models.
Pre-trained advice concreteness features
Description
For internal use only. This dataset demonstrates the ngram features that are used for the pre-trained models.
Usage
diceNGrams
Format
A (truncated) matrix of ngram feature counts for alignment to the pre-trained glmnet models.
Source
Yeomans et al., (2024). A Natural Language Processing Model for Conflict Expression in Conversation
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
Purchase offers for phone
Description
A dataset containing the purchase offer message and a label indicating if the writer was assigned to be warm (1) or tough (0)
Usage
phone_offers
Format
A data frame with 355 rows and 2 variables:
- message
character of purchase offer message
- condition
binary label indicating if message is warm or tough
Source
Jeong, M., Minson, J., Yeomans, M. & Gino, F. (2019).
"Communicating Warmth in Distributed Negotiations is Surprisingly Ineffective."
Study 1. https://osf.io/t7sd6/
Polynomial pre-trained fit
Description
This calculates the polynomial projection of the simple features used during model training
Usage
polymodel
Format
A pre-trained polynomial equation
UK to US Conversion dictionary
Description
For internal use only. This dataset contains a quanteda dictionary for converting UK words to US words. The models in this package were all trained on US English.
Usage
uk2us
Format
A quanteda dictionary with named entries. Names are the US version, and entries are the UK version.
Source
Borrowed from the quanteda.dictionaries package on github (from user kbenoit)
UK to US conversion
Description
background function to load.
Usage
usWords(text)
Arguments
text |
character Vector of strings to convert to US spelling. |
Value
character Vector of Americanized strings.