textSimilarityMatrix

Character string describing type of measure to be computed. Default is "cosine" (see also
measures from textDistance() (which here is computed as 1 - textDistance) including "euclidean", "maximum",
"manhattan", "canberra", "binary" and "minkowski").

method

Compute semantic similarity scores between all combinations in a word embedding

Transforms text variables to word embeddings; where the word embeddings are used to statistically test the mean difference between set of texts, compute semantic similarity scores between texts, predict numerical variables, and visual statistically significant words according to various dimensions etc. For more information see <https://www.r-text.org>.

Oscar Kjell

text

Analyses of Text using Natural Language Processing and Machine
Learning

Salvatore Giorgi

Andrew Schwartz

textSimilarityMatrix function

<dl><dt>x</dt>
<dd>Word embeddings from textEmbed.</dd>
<dt>method</dt>
<dd>Character string describing type of measure to be computed. Default is "cosine" (see also
measures from textDistance() (which here is computed as 1 - textDistance) including "euclidean", "maximum",
"manhattan", "canberra", "binary" and "minkowski").</dd></dl>

Arguments

Compute semantic similarity scores between all combinations in a word embedding — textSimilarityMatrix

<dl>

<dt>x</dt>
<dd>Word embeddings from textEmbed.</dd>


<dt>method</dt>
<dd>Character string describing type of measure to be computed. Default is "cosine" (see also
measures from textDistance() (which here is computed as 1 - textDistance) including "euclidean", "maximum",
"manhattan", "canberra", "binary" and "minkowski").</dd>

</dl>

textSimilarityMatrix: Compute semantic similarity scores between all combinations in a word embedding

Description

Usage

Value

Arguments

See Also

Examples