Analyses of Text using Natural Language Processing and Machine Learning


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Documentation for package ‘text’ version 0.9.50

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