R/DICE.R

In DICEM: Directness and Intensity of Conflict Expression

#' 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.
#' @param text character A vector of texts, each of which will be tallied for DICE features.
#' @param 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.
#' @param uk_english logical Does the text contain any British English spelling? Including variants (e.g. Canadian). Default is FALSE
#' @param 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.
#' @return 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$message[1:10], parser="none")
#'
#'\dontrun{
#'
#' # Detect multiple cores automatically for parallel processing
#' DICE(phone_offers$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")
#'}
#'
#'
#'@export

DICE<-function(text,
               parser=c("none","spacy"),
               uk_english=FALSE,
               num_mc_cores=1){
  if(uk_english){
    text<-usWords(text)
  }
  text<-c(text,"filler")
  
  diceX<-featureSet(text,parser=parser)
  
  
  if(parser[1]=="spacy"){
    directModel<-xgboost::xgb.load(system.file("extdata", "directModelxgb.json", package="DICEM") )
    intenseModel<-xgboost::xgb.load(system.file("extdata", "intenseModelxgb.json", package="DICEM") )
  } else{
    directModel<-xgboost::xgb.load(system.file("extdata", "directModel_basicxgb.json", package="DICEM") )
    intenseModel<-xgboost::xgb.load(system.file("extdata", "intenseModel_basicxgb.json", package="DICEM") )
  }
  
  intense<-stats::predict(intenseModel, newdata = diceX)
  direct<-stats::predict(directModel, newdata = diceX)
  
  DICEscores=data.frame(intense,direct)
  
  DICEscores<-DICEscores[1:(nrow(DICEscores)-1),]
  
  return(DICEscores)
}