textSimilarity: Compute the semantic similarity between two text variables.

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

Compute the semantic similarity between two text variables.

Description

Compute the semantic similarity between two text variables.

Usage

textSimilarity(x, y, method = "cosine", center = TRUE, scale = FALSE)

Arguments

x	Word embeddings from textEmbed.
y	Word embeddings from textEmbed.
method	(character) Character string describing type of measure to be computed. Default is "cosine" (see also "spearmen", "pearson" as well as measures from textDistance() (which here is computed as 1 - textDistance) including "euclidean", "maximum", "manhattan", "canberra", "binary" and "minkowski").
center	(boolean; from base::scale) If center is TRUE then centering is done by subtracting the column means (omitting NAs) of x from their corresponding columns, and if center is FALSE, no centering is done.
scale	(boolean; from base::scale) If scale is TRUE then scaling is done by dividing the (centered) columns of x by their standard deviations if center is TRUE, and the root mean square otherwise.

Value

A vector comprising semantic similarity scores. The closer the value is to 1 when using the default method, "cosine", the higher the semantic similarity.

See Also

See textDistance and textSimilarityNorm.

Examples

# Compute the semantic similarity between the embeddings from "harmonytext" and "satisfactiontext".
## Not run: 
similarity_scores <- textSimilarity(
  x = word_embeddings_4$texts$harmonytext,
  y = word_embeddings_4$texts$satisfactiontext
)

# Show information about how similarity_scores were constructed.
comment(similarity_scores)

## End(Not run)

text documentation built on Sept. 11, 2024, 7:22 p.m.