text (version 0.9.90)

textCentrality: Compute semantic similarity score between single words' word embeddings and the aggregated word embedding of all words.

Description

Compute semantic similarity score between single words' word embeddings and the aggregated word embedding of all words.

Usage

textCentrality(
  words,
  word_embeddings,
  single_word_embeddings = single_word_embeddings_df,
  method = "cosine",
  aggregation = "mean",
  min_freq_words_test = 0
)

Value

A dataframe with variables (e.g., including semantic similarity, frequencies) for the individual words that are used for the plotting in the textCentralityPlot function.

Arguments

words

Word or text variable to be plotted.

word_embeddings

Word embeddings from textEmbed for the words to be plotted (i.e., the aggregated word embeddings for the "words" variable).

single_word_embeddings

Word embeddings from textEmbed for individual words (i.e., the decontextualized word embeddings).

method

Character string describing type of measure to be computed. Default is "cosine" (see also measures from textDistance() (which here is computed as 1 - textDistance) including "euclidean", "maximum", "manhattan", "canberra", "binary" and "minkowski").

aggregation

Method to aggregate the word embeddings (default = "mean"; see also "min", "max" or "[CLS]").

min_freq_words_test

Option to select words that have at least occurred a specified number of times (default = 0); when creating the semantic similarity scores.

See Also

see textCentralityPlot textProjection

Examples

if (FALSE) {
df_for_plotting <- textCentrality(
  words = Language_based_assessment_data_8$harmonywords,
  word_embeddings = word_embeddings_4$harmonywords,
  single_word_embeddings = word_embeddings_4$singlewords_we
)
df_for_plotting
}