text (version 0.9.99.2)

textDistanceNorm: Compute 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).

Description

Compute 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).

Usage

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

Value

A vector comprising semantic distance scores.

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 "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 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.

See Also

see textDistance and textSimilarityTest

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$texts
word_embeddings_wordnorm <- textEmbed(norms)
similarity_scores <- textDistanceNorm(
  word_embeddings$harmonytext,
  word_embeddings_wordnorm$harmonynorm
)
}