textTopicsTree

textTopicsTest (EXPERIMENTAL) to get the hierarchical topic tree

Link R with Transformers from Hugging Face to transform 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 Transformers Models from HuggingFace,
Natural Language Processing and Machine Learning

Salvatore Giorgi

Andrew Schwartz

textTopicsTree function

<dl><dt>topic_model</dt>
<dd>(list) The output from textTopics.</dd>
<dt>data</dt>
<dd>(tibble/data.frame) A tibble with the data</dd>
<dt>data_var</dt>
<dd>(string) The name of the text variable that the topic model was trained on</dd></dl>

Arguments

textTopicsTest (EXPERIMENTAL) to get the hierarchical topic tree — textTopicsTree

<dl>

<dt>topic_model</dt>
<dd>(list) The output from textTopics.</dd>


<dt>data</dt>
<dd>(tibble/data.frame) A tibble with the data</dd>


<dt>data_var</dt>
<dd>(string) The name of the text variable that the topic model was trained on</dd>

</dl>

textTopicsTree: textTopicsTest (EXPERIMENTAL) to get the hierarchical topic tree

Description

Usage

Value

Arguments