Semantic distance between a text variable and a word norm

textDistanceNorm() computes the semantic distance between a text variable and a word norm (i.e., a text represented by one word embedding that represent a construct/concept).

textDistanceNorm(x, y, method = "euclidean", center = FALSE, scale = FALSE)

Arguments

x

Word embeddings (from textEmbed()).

y

Word embedding from textEmbed (from only one text).

method

(character) Character string describing type of measure to be computed; default is "euclidean" (see also measures from stats:dist() including "maximum", "manhattan", "canberra", "binary" and "minkowski". It is also possible to use "cosine", which computes the cosine distance (i.e., 1 - cosine(x, y)).

center

(boolean; from base::scale) If center is TRUE then centering is done by subtracting the embedding mean (omitting NAs) of x from each of its dimension, 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) embedding dimensions by the standard deviation of the embedding if center is TRUE, and the root mean square otherwise.

Value

A vector comprising semantic distance scores.

See also

see textDistance

Examples

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

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