Implicit Motives Tutorial

In text: Analyses of Text using Transformers Models from HuggingFace, Natural Language Processing and Machine Learning

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
evaluate = FALSE

#### Initial setup: Install and open the text package in an R environment (only required the first time).
#install.packages("text")
#textrpp_install()
#textrpp_initialize()

#### Load the package.
library(text)

#### 1: Load data. The following data serves as an example:  
im_example <- dplyr::mutate(
  .data = Language_based_assessment_data_8,
  story = harmonytexts,
  participant_id = dplyr::row_number()) 

#### 2: Retrieve sentence- and person-level predictions. Choose between our three motives: power, achievement, or affiliation.
predictions <- textPredict(
  texts = im_example$story,
  model_info = "implicitpower_roberta23_nilsson2024",
  participant_id = im_example$participant_id,
  dataset_to_merge_predictions = im_example)

#### 3: Examine sentence- and person-level predictions. 
predictions$sentence_assessments
predictions$person_assessments

text documentation built on June 8, 2025, 1:32 p.m.