# 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)