`R/3_1_textSimilarity.R`

`textSimilarityNorm.Rd`

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)`

- 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.

A vector comprising semantic similarity scores.

see `textSimilarity`

```
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
)
}
```