`R/3_1_textSimilarity.R`

`textDistanceNorm.Rd`

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).

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

- 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 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.

A vector comprising semantic distance scores.

see `textDistance`

```
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
)
}
```