L-BAM Tutorial

# Install the text package (only needed the first time)
# install.packages("text")
library(text)
# textrpp_install()
# textrpp_initialize()

# Get the LBAM as a data frame and filter for models starting with “Dep”
lbam <- text::textLBAM()

subset(
  lbam,
  substr(Construct_Concept_Behaviours, 1, 3) == "dep",
  select = c(Construct_Concept_Behaviours, Name)
)

# Example text to access
text_to_assess = c(
  "I feel down and blue all the time.",
  "I feel great and have no worries that bothers me.")

# Produce depression severity scores using a text-trained model
# This command downloads the model, creates word embeddings, and applies the model to the embeddings.
depression_scores <- text::textPredict(
  model_info = "depression_text_phq9_roberta23_gu2024",
  texts = text_to_assess,
  dim_name = FALSE)

# You can find information about a text-trained model in R.
model_Gu2024 <- readRDS("depressiontext_robertaL23_phq9_Gu2024.rds")
model_Gu2024

# Assess the harmony in life of the same text as above
# The function now uses the same word embeddings as above (i.e., it does not produce new ones).
harmony_in_life_scores <- textAssess(
  model_info = "harmony_text_roberta23_kjell2022",
  texts = text_to_assess,
  dim_name = FALSE)

# Assign implicit motives labels using fine-tuned models
implicit_motive <- text::textClassify(
  model_info = "implicitpower_roberta_ft_nilsson2024",
  texts = text_to_assess)