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).

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

## Arguments

x

Word embeddings from textEmbed.

y

Word embedding from textEmbed (from only one text).

method

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.

see textSimilarity

## Examples

if (FALSE) {
library(dplyr)
library(tibble)
harmonynorm <- c("harmony peace ")
satisfactionnorm <- c("satisfaction achievement")

norms <- tibble::tibble(harmonynorm, satisfactionnorm)
word_embeddings <- word_embeddings_4$texts word_embeddings_wordnorm <- textEmbed(norms) similarity_scores <- textSimilarityNorm( word_embeddings$harmonytext,
word_embeddings_wordnorm\$harmonynorm
)
}