inst/doc/text.R

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

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

## ----setup, eval = evaluate, warning=FALSE, message=FALSE---------------------
#  
#  library(text)
#  
#  # View example data including both text and numerical variables
#  Language_based_assessment_data_8
#  
#  # Transform the text data to BERT word embeddings
#  word_embeddings <- textEmbed(
#    texts = Language_based_assessment_data_8[3],
#    model = "bert-base-uncased",
#    layers = -2,
#    aggregation_from_tokens_to_texts = "mean",
#    aggregation_from_tokens_to_word_types = "mean",
#    keep_token_embeddings = FALSE)
#  
#  # See how word embeddings are structured
#  word_embeddings
#  
#  # Save the word embeddings to avoid having to import the text every time. (i.e., remove the ##)
#  ## saveRDS(word_embeddings, "word_embeddings.rds")
#  
#  # Get the word embeddings again (i.e., remove the ##)
#  ## word_embeddings <- readRDS("_YOURPATH_/word_embeddings.rds")

## ---- eval = evaluate,  warning=FALSE, message=FALSE--------------------------
#  library(text)
#  
#  # Examine the relationship between harmonytext word embeddings and the harmony in life rating scale
#  model_htext_hils <- textTrain(word_embeddings$texts$harmonywords,
#                                Language_based_assessment_data_8$hilstotal)
#  
#  # Examine the correlation between predicted and observed Harmony in life scale scores
#  model_htext_hils$results
#  

## ---- eval = evaluate, warning=FALSE, message=FALSE---------------------------
#  library(text)
#  
#  # Pre-process data
#  projection_results <- textProjection(
#    words = Language_based_assessment_data_8$harmonywords,
#    word_embeddings = word_embeddings$texts,
#    word_types_embeddings = word_embeddings$word_types,
#    x = Language_based_assessment_data_8$hilstotal,
#    y = Language_based_assessment_data_8$age
#  )
#  projection_results$word_data
#  

## ---- eval = evaluate, warning=FALSE, message=FALSE, dpi=300------------------
#  library(text)
#  # Supervised Dimension Projection Plot
#  # To avoid warnings -- and that words do not get plotted, first increase the max.overlaps for the entire session:
#  options(ggrepel.max.overlaps = 1000)
#  
#  # Supervised Dimension Projection Plot
#  plot_projection_2D <- textProjectionPlot(
#    word_data = projection_results,
#   min_freq_words_plot = 1,
#   plot_n_word_extreme = 10,
#   plot_n_word_frequency = 5,
#   plot_n_words_middle = 5,
#   y_axes = TRUE,
#   p_alpha = 0.05,
#   p_adjust_method = "fdr",
#    title_top = "Harmony Words Responses (Supervised Dimension Projection)",
#    x_axes_label = "Low vs. High Harmony in Life Scale Score",
#    y_axes_label = "Low vs.High Age",
#    bivariate_color_codes = c("#E07f6a", "#60A1F7", "#85DB8E",
#                              "#FF0000", "#EAEAEA", "#5dc688",
#                              "#E07f6a", "#60A1F7", "#85DB8E"
#  ))
#  # View plot
#  plot_projection_2D$final_plot
#  
#