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Fit Embedding-based Topic Model

Source: R/topic_modeling.R
fit_embedding_model.Rd

This function performs embedding-based topic modeling using transformer embeddings and specialized clustering techniques. The primary method uses the BERTopic library, which combines transformer embeddings with UMAP dimensionality reduction and HDBSCAN clustering for optimal topic discovery. This approach creates more semantically coherent topics compared to traditional methods by leveraging deep learning embeddings.

Usage

fit_embedding_model(
  texts,
  method = "umap_hdbscan",
  n_topics = 10,
  embedding_model = "all-MiniLM-L6-v2",
  clustering_method = "kmeans",
  similarity_threshold = 0.7,
  min_topic_size = 3,
  cluster_selection_method = "eom",
  umap_neighbors = 15,
  umap_min_dist = 0,
  umap_n_components = 5,
  representation_method = "c-tfidf",
  diversity = 0.5,
  reduce_outliers = TRUE,
  seed = 123,
  verbose = TRUE,
  precomputed_embeddings = NULL
)

Arguments

texts

A character vector of texts to analyze.

method

The topic modeling method: "umap_hdbscan" (uses BERTopic), "embedding_clustering", "hierarchical_semantic".

n_topics

The number of topics to identify. For UMAP+HDBSCAN, use NULL or "auto" for automatic determination, or specify an integer.

embedding_model

The embedding model to use (default: "all-MiniLM-L6-v2").

clustering_method

The clustering method for embedding-based approach: "kmeans", "hierarchical", "dbscan", "hdbscan".

similarity_threshold

The similarity threshold for topic assignment (default: 0.7).

min_topic_size

The minimum number of documents per topic (default: 3).

cluster_selection_method

HDBSCAN cluster selection method: "eom" (Excess of Mass, default) or "leaf" (finer-grained topics).

umap_neighbors

The number of neighbors for UMAP dimensionality reduction (default: 15).

umap_min_dist

The minimum distance for UMAP (default: 0.0). Use 0.0 for tight, well-separated clusters. Use 0.1+ for visualization purposes. Range: 0.0-0.99.

umap_n_components

The number of UMAP components (default: 5).

representation_method

The method for topic representation: "c-tfidf", "tfidf", "mmr", "frequency" (default: "c-tfidf").

diversity

Topic diversity parameter between 0 and 1 (default: 0.5).

reduce_outliers

Logical, if TRUE, reduces outliers in HDBSCAN clustering (default: TRUE).

seed

Random seed for reproducibility (default: 123).

verbose

Logical, if TRUE, prints progress messages.

precomputed_embeddings

Optional matrix of pre-computed document embeddings. If provided, skips embedding generation for improved performance. Must have the same number of rows as the length of texts.

Value

A list containing topic assignments, topic keywords, and quality metrics.

See also

Other topic-modeling: analyze_semantic_evolution(), assess_embedding_stability(), assess_hybrid_stability(), auto_tune_embedding_topics(), calculate_assignment_consistency(), calculate_eval_metrics_internal(), calculate_keyword_stability(), calculate_semantic_drift(), calculate_topic_probability(), calculate_topic_stability(), find_optimal_k(), find_topic_matches(), fit_hybrid_model(), fit_temporal_model(), generate_topic_labels(), get_topic_prevalence(), get_topic_terms(), get_topic_texts(), identify_topic_trends(), plot_model_comparison(), plot_quality_metrics(), run_contrastive_topics_internal(), run_neural_topics_internal(), run_temporal_topics_internal(), validate_semantic_coherence()

Examples

if (interactive()) {
  mydata <- TextAnalysisR::SpecialEduTech
  united_tbl <- TextAnalysisR::unite_cols(
    mydata,
    listed_vars = c("title", "keyword", "abstract")
  )
  texts <- united_tbl$united_texts

  # Embedding-based topic modeling (powered by BERTopic)
  result <- TextAnalysisR::fit_embedding_model(
    texts = texts,
    method = "umap_hdbscan",
    n_topics = 8,
    min_topic_size = 3
  )

  print(result$topic_assignments)
  print(result$topic_keywords)
}