Calculate Topic Diversity (Unique-Word Proportion)

Diversity = unique top-K terms across all topics / (n_topics * top_k). Range 0 (all topics identical) to 1 (fully disjoint). Complementary to NPMI coherence: a good model scores high on both.

Usage

calculate_topic_diversity(top_terms_list, top_k = 25)

Arguments

top_terms_list

Named or unnamed list of character vectors, one per topic.

top_k

Number of top terms per topic to include (default 25).

Value

Numeric between 0 and 1, or NA if input empty.

See also

Other topic-modeling: analyze_semantic_evolution(), assess_embedding_stability(), assess_hybrid_stability(), auto_tune_embedding_topics(), calculate_assignment_consistency(), calculate_keyword_stability(), calculate_npmi(), calculate_semantic_drift(), calculate_topic_probability(), calculate_topic_stability(), extract_topic_terms_df(), find_optimal_k(), find_topic_matches(), fit_embedding_model(), fit_hybrid_model(), fit_temporal_model(), fit_topic_prevalence_model(), generate_topic_labels(), get_topic_prevalence(), get_topic_terms(), get_topic_texts(), identify_topic_trends(), plot_model_comparison(), plot_quality_metrics(), plot_topic_effects_categorical(), plot_topic_effects_continuous(), plot_topic_probability(), plot_word_probability(), run_neural_topics_internal(), validate_semantic_coherence()