Apply topic modeling to a corpus — extract_topics • LBDiscover

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
)

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

Value

A list containing topic-term and document-topic matrices

Examples

if (FALSE) { # \dontrun{
topics <- extract_topics(article_data, text_column = "abstract", n_topics = 5)
} # }