# Analyses of Text using Natural Language Processing and Machine Learning

## Documentation for package ‘text’ version 0.9.50

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

centrality_data_harmony | Example data for plotting a Semantic Centrality Plot.
DP_projections_HILS_SWLS_100 | Data for plotting a Dot Product Projection Plot.
embeddings_from_huggingface2 | Word embeddings from textEmbedLayersOutput function
Language_based_assessment_data_3_100 | Example text and numeric data.
Language_based_assessment_data_8 | Text and numeric data for 10 participants.
PC_projections_satisfactionwords_40 | Example data for plotting a Principle Component Projection Plot.
textCentrality | Compute cosine semantic similarity score between single words' word embeddings and the aggregated word embedding of all words.
textCentralityPlot | Plot words according to cosine semantic similarity to the aggregated word embedding.
textEmbed | Extract layers and aggregate them to word embeddings, for all character variables in a given dataframe.
textEmbedLayerAggregation | Select and aggregate layers of hidden states to form a word embeddings.
textEmbedLayersOutput | Extract layers of hidden states (word embeddings) for all character variables in a given dataframe.
textEmbedStatic | Applies word embeddings from a given decontextualized static space (such as from Latent Semantic Analyses) to all character variables
textPCA | Compute 2 PCA dimensions of the word embeddings for individual words.
textPCAPlot | Plot words according to 2-D plot from 2 PCA components.
textPlot | Plot words from textProjection() or textWordPrediction().
textPredict | Predict scores or classification from, e.g., textTrain.
textPredictTest | Significance testing correlations If only y1 is provided a t-test is computed, between the absolute error from yhat1-y1 and yhat2-y1.
textProjection | Compute Supervised Dimension Projection and related variables for plotting words.
textProjectionPlot | Plot words according to Supervised Dimension Projection.
textrpp_initialize | Initialize text required python packages
textrpp_install | Install text required python packages in conda or virtualenv environment
textrpp_install_virtualenv | Install text required python packages in conda or virtualenv environment
textrpp_uninstall | Uninstall textrpp conda environment
textSimilarity | Compute the cosine semantic similarity between two text variables.
textSimilarityNorm | Compute the semantic similarity between a text variable and a word norm (i.e., a text represented by one word embedding that represent a construct).
textSimilarityTest | Test whether there is a significant difference in meaning between two sets of texts (i.e., between their word embeddings).
textTrain | Train word embeddings to a numeric (ridge regression) or categorical (random forest) variable.
textTrainLists | Individually trains word embeddings from several text variables to several numeric or categorical variables. It is possible to have word embeddings from one text variable and several numeric/categprical variables; or vice verse, word embeddings from several text variables to one numeric/categorical variable. It is not possible to mix numeric and categorical variables.
textTrainRandomForest | Train word embeddings to a categorical variable using random forrest.
textTrainRegression | Train word embeddings to a numeric variable.
textWordPrediction | Compute predictions based on single words for plotting words. The word embeddings of single words are trained to predict the mean value associated with that word. P-values does NOT work yet.
word_embeddings_4 | Word embeddings for 4 text variables for 40 participants