extract_topics

This function implements a simple non-negative matrix factorization (NMF)
approach to topic modeling, without requiring additional packages.

A suite of tools for literature-based discovery in biomedical research.
Provides functions for retrieving scientific articles from 'PubMed' and
other NCBI databases, extracting biomedical entities (diseases, drugs, genes, etc.),
building co-occurrence networks, and applying various discovery models
including 'ABC', 'AnC', 'LSI', and 'BITOLA'. The package also includes
visualization tools for exploring discovered connections.

Chao Liu Liu

LBDiscover

Literature-Based Discovery Tools for Biomedical Research

Chao Liu 

extract_topics function

<dl><dt>text_data</dt>
<dd>A data frame containing the text data</dd>
<dt>text_column</dt>
<dd>Name of the column containing the text</dd>
<dt>n_topics</dt>
<dd>Number of topics to extract</dd>
<dt>max_terms</dt>
<dd>Maximum number of terms per topic to return</dd>
<dt>n_iterations</dt>
<dd>Number of iterations for the NMF algorithm</dd>
<dt>seed</dt>
<dd>Optional seed for reproducibility. If NULL, no seed is set.</dd></dl>

Arguments

Apply topic modeling to a corpus — extract_topics

<dl>

<dt>text_data</dt>
<dd>A data frame containing the text data</dd>


<dt>text_column</dt>
<dd>Name of the column containing the text</dd>


<dt>n_topics</dt>
<dd>Number of topics to extract</dd>


<dt>max_terms</dt>
<dd>Maximum number of terms per topic to return</dd>


<dt>n_iterations</dt>
<dd>Number of iterations for the NMF algorithm</dd>


<dt>seed</dt>
<dd>Optional seed for reproducibility. If NULL, no seed is set.</dd>

</dl>

extract_topics: Apply topic modeling to a corpus

Description

Usage

Value

Arguments