extract_topics: Apply topic modeling to a corpus

In LBDiscover: Literature-Based Discovery Tools for Biomedical Research

Apply topic modeling to a corpus

Description

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

Usage

extract_topics(
  text_data,
  text_column = "abstract",
  n_topics = 5,
  max_terms = 10,
  n_iterations = 50,
  seed = NULL
)

Arguments

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

Value

A list containing topic-term and document-topic matrices

LBDiscover documentation built on June 16, 2025, 5:09 p.m.