% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/topic_modeling.R
\name{generate_semantic_topic_keywords}
\alias{generate_semantic_topic_keywords}
\title{Generate Semantic Topic Keywords (c-TF-IDF)}
\usage{
generate_semantic_topic_keywords(
  texts,
  topic_assignments,
  n_keywords = 10,
  method = "c-tfidf",
  diversity = 0.5
)
}
\arguments{
\item{texts}{A character vector of texts.}

\item{topic_assignments}{A vector of topic assignments.}

\item{n_keywords}{The number of keywords to extract per topic (default: 10).}

\item{method}{The representation method: "c-tfidf" (default), "tfidf", "mmr", or "frequency".}

\item{diversity}{Diversity weight for "mmr" between 0 and 1 (default: 0.5).
MMR selects terms by (1 - diversity) * relevance - diversity * redundancy.}
}
\value{
A list of keywords for each topic.
}
\description{
Generate keywords for topics using c-TF-IDF (class-based TF-IDF), similar to BERTopic.
This method treats all documents in a topic as a single document and calculates TF-IDF
scores relative to other topics.
}
\keyword{internal}
