R/receptiveness.R

In politeness: Detecting Politeness Features in Text

utils::globalVariables(c("receptive_train")) # prevent incorrect "no global binding" note

#' Conversational Receptiveness
#'
#' @description Pre-trained model to detect conversational receptiveness
#' @param texts character A vector of texts, each of which will be tallied for politeness features.
#' @param num_mc_cores integer Number of cores for parallelization.
#' @details This is a wrapper around a pre-trained model of "conversational receptiveness".
#' The model trained from Study 1 of that paper can be applied to new text with a single function.
#' This model requires grammar parsing via SpaCy. Please see \code{\link{spacyr}} for details on installation.
#' @return a vector with receptiveness scores
#' @references
#' Yeomans, M., Minson, J., Collins, H., Chen, F. & Gino, F. (2020). Conversational Receptiveness: Improving Engagement with Opposing Views. OBHDP.
#'
#' @examples
#'
#'
#'\dontrun{
#' data("phone_offers")
#'
#' receptiveness(phone_offers$message)
#'
#'}
#'
#'@export
receptiveness<-function(texts, num_mc_cores=1){
  # train_dat=politeness::receptive_train
  #
  # m_polite_train = as.matrix(politeness::receptive_polite)

  m_polite_test = as.matrix(politeness::politeness(texts,
                                                   parser="spacy",drop_blank = F,
                                                   num_mc_cores=num_mc_cores))

  # Remove 1st/2nd person pronouns & rare features
  m_test=m_polite_test[,which(colnames(m_polite_test)%in%politeness::receptive_names)]

  polite_predict<-as.vector(stats::predict(politeness::receptive_model,
                                           newx=m_test,
                                           s="lambda.1se", type="response"))

  return(polite_predict)
  #
  # recept_predict<-as.vector(politeness::politenessProjection(m_train,
  #                                                            train_dat$receptive,
  #                                                            m_test)$test_proj)
  #
  #
  # return(recept_predict)
}