textSimilarityNorm: Compute the semantic similarity between a text variable and a...

In text: Analyses of Text using Transformers Models from HuggingFace, Natural Language Processing and Machine Learning

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).

Description

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).

Usage

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 Also

see textSimilarity

Examples

## Not run: 
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
)

## End(Not run)

text documentation built on Aug. 9, 2023, 5:08 p.m.