# For text-version => 0.9.99
# Install text from CRAN
install.packages("text")
library(text)

# Set-up en environment with text-required python packages
textrpp_install()

# Initialize the environment – and save the settings for next time
textrpp_initialize(save_profile = TRUE)

# # # # # # # # # # # # # # # # # # # # # # # # # # # #

# Example text
texts <- c("I am feeling relatedness with others", "That's great!")

# Defaults
embeddings <- textEmbed(texts)

# Output
embeddings$tokens # Output embeddings$texts
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# Look at example data included in the text- package comprising both text and numerical variables (note that there are only 40 participants in this example).
Language_based_assessment_data_8

# Transform the text/word data to word embeddings (see help(textEmbed) to see the default settings).
word_embeddings <- textEmbed(
Language_based_assessment_data_8,
model = "bert-base-uncased",
aggregation_from_layers_to_tokens = "concatenate",
aggregation_from_tokens_to_texts = "mean",
keep_token_embeddings = FALSE
)

# See how the word embeddings are structured
word_embeddings

# Save the word embeddings to avoid having to embed the text again. It is good practice to save output from analyses that take a lot of time to compute, which is often the case when analyzing text data.
saveRDS(word_embeddings, "word_embeddings.rds")

# Get the saved word embeddings (again)

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# Get hidden states for "I am fine"
imf_embeddings_11_12 <- textEmbedRawLayers(
"I am fine",
layers = 11:12
)
imf_embeddings_11_12

#OUTPUT

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# 1. Concatenate layers(results in 1,536 dimensions).
textEmbedLayerAggregation(
imf_embeddings_11_12$context_tokens, layers = 11:12, aggregation_from_layers_to_tokens = "concatenate", aggregation_from_tokens_to_texts = "mean" ) # OUTPUT # # # # # # # # # # # # # # # # # # # # # # # # # # # # # 2. Aggregate layers using mean (results in 768). textEmbedLayerAggregation( imf_embeddings_11_12$context_tokens,
layers = 11,
aggregation_from_tokens_to_texts = "mean"
)

# OUTPUT

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# Examine the relationship between satisfactiontext and the corresponding rating scale
model_satisfactiontext_swls <- textTrain(
x = word_embeddings$texts$satisfactiontexts, # the predictor variables (i.e., the word embeddings)
y = Language_based_assessment_data_8$swlstotal, # the criterion variable (i.e.,the rating scale score. model_description = "author(s): Kjell, Giorgi, & Schwartz; data: N=40, population = Online, Mechanical Turk; publication: title = Example for demo; description: swls = the satisfaction with life scale" ) # Examine the correlation between predicted and observed Harmony in life scale scores model_satisfactiontext_swls$results

# OUTPUT:

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# Save the mode
saveRDS(
model_satisfactiontext_swls,
"model_satisfactiontext_swls.rds"
)
"model_satisfactiontext_swls.rds"
)

# Examine the names in the object returned from training
names(model_satisfactiontext_swls)

#OUTPUT:

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# Predicting several outcomes from several word embeddings
models_words_ratings <- textTrainLists(
word_embeddings$texts[1:2], Language_based_assessment_data_8[5:6] ) # See results models_words_ratings$results

# OUTPUT

# Save model
saveRDS(models_words_ratings, "models_words_ratings.rds")
"models_words_ratings.rds"
)

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"valence_Warriner_L11.rds"
)

# Examine the model
valence_Warriner_L11

# PART OF THE OUTPUT

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# Apply the model to the satisfaction text
satisfaction_text_valence <- textPredict(
valence_Warriner_L11,
word_embeddings$texts$satisfactiontexts,
dim_names = FALSE
)

# Examine the correlation between the predicted valence and the Satisfaction with life scale score
psych::corr.test(
satisfaction_text_valence$word_embeddings__ypred, Language_based_assessment_data_8$swlstotal
)

# OUTPUT

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# Compute semantic similarity scores between two text columns, using the previously created word_embeddings.
semantic_similarity_scores <- textSimilarity(
word_embeddings$texts$harmonytexts,
word_embeddings$texts$satisfactiontexts
)
# Look at the first scores

# OUTPUT
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# Read word norms text (later we will use these for the semantic centrality plot)
"Word_Norms_Mental_Health_Kjell2018_text.csv"
)

# Read the word embeddings for the word norms
"Word_Norms_Mental_Health_Kjell2018_text_embedding_L11.rds"
)

# Examine which word norms there are.
names(word_norms_embeddings$texts) # OUTPUT # # # # # # # # # # # # # # # # # # # # # # # # # # # # # Compute semantic similarity score between the harmony answers and the harmony norm # Note that the descriptive word answers are used instead of text answers to correspond with how the word norm was created. norm_similarity_scores_harmony <- textSimilarityNorm( word_embeddings$texts$harmonywords, word_norms_embeddings$texts$harmonynorm ) # Correlating the semantic measure with the corresponding rating scale psych::corr.test( norm_similarity_scores_harmony, Language_based_assessment_data_8$hilstotal
)

# OUTPUT
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# Extract word type embeddings and text embeddings for harmony words
harmony_words_embeddings <- textEmbed(
texts = Language_based_assessment_data_8["harmonywords"],
aggregation_from_layers_to_tokens = "concatenate",
aggregation_from_tokens_to_texts = "mean",
aggregation_from_tokens_to_word_types = "mean",
keep_token_embeddings = FALSE
)

# Pre-processing data for plotting
projection_results <- textProjection(
words = Language_based_assessment_data_8$harmonywords, word_embeddings = harmony_words_embeddings$texts,
word_types_embeddings = harmony_words_embeddings$word_types, x = Language_based_assessment_data_8$hilstotal,
y = Language_based_assessment_data_8$age ) projection_results$word_data

# To avoid warnings -- and that words do not get plotted, first increase the max.overlaps for the entire session:
options(ggrepel.max.overlaps = 1000)

# Plot
plot_projection <- textPlot(
projection_results,
min_freq_words_plot = 1,
plot_n_word_extreme = 10,
plot_n_word_frequency = 5,
plot_n_words_middle = 5,
y_axes = FALSE,
p_alpha = 0.05,
title_top = "Harmony Words Responses (Supervised Dimension Projection)",
x_axes_label = "Low to High Harmony in Life Scale Score",
y_axes_label = "",
bivariate_color_codes = c("#FFFFFF", "#FFFFFF", "#FFFFFF",
"#E07f6a", "#EAEAEA", "#85DB8E",
"#FFFFFF", "#FFFFFF", "#FFFFFF"
)
)
# View plot

plot_projection$final_plot # # # # # # # # # # # # # # # # # # # # # # # # # # # # # Plot plot_projection_2D <- textPlot( projection_results, min_freq_words_plot = 1, plot_n_word_extreme = 10, plot_n_word_frequency = 5, plot_n_words_middle = 5, y_axes = TRUE, # Change to TRUE/FALSE p_alpha = 0.05, p_adjust_method = "fdr", title_top = "Harmony Words Responses (Supervised Dimension Projection)", x_axes_label = "Low vs. High Harmony in Life Scale Score", y_axes_label = "Low vs.High Age", bivariate_color_codes = c("#E07f6b", "#60A1F7", "#85DB8D", "#FF0000", "#EAEAEA", "#5dc688", "#E07f6a", "#60A1F7", "#85DB8E" ) ) # View plot plot_projection_2D$final_plot

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# Computing words' centrality (semantic similarity) score to the aggregated embedding of all words
centrality_results <- textCentrality(
words = word_norms$satisfactionnorm, word_embeddings = word_norms_embeddings$texts$satisfactionnorm, word_types_embeddings = word_norms_embeddings$word_types
)

options(ggrepel.max.overlaps = 1000)
centrality_plot <- textCentralityPlot(
word_data = centrality_results,
min_freq_words_test = 2,
plot_n_word_extreme = 10,
plot_n_word_frequency = 5,
plot_n_words_middle = 5,
title_top = "Satisfaction with life word norm: Semantic Centrality Plot",
x_axes_label = "Satisfaction with Life Semantic Centrality"
)

centrality_plot$final_plot # OUTPUT # # # # # # # # # # # # # # # # # # # # # # # # # # # # # Supplementary # PCA results to be plotted help(textPCA) textPCA_results <- textPCA( words = Language_based_assessment_data_8$satisfactionwords,
word_types_embeddings = harmony_words_embeddings$word_types ) # Plotting the PCA results plot_PCA <- textPCAPlot( word_data = textPCA_results, min_freq_words_test = 2, plot_n_word_extreme = 5, plot_n_word_frequency = 5, plot_n_words_middle = 5 ) plot_PCA$final_plot