Build a topic-term data frame from any supported topic model

Unified helper that produces the long-format data.frame(topic, term, beta) expected by generate_topic_labels() from an STM model, an embedding result, or a hybrid result. Dispatches on the object's structure:

• STM model (has $beta$logbeta and $vocab) -> top terms via stm::labelTopics() FREX

• Embedding result (has $topic_keywords) -> c-TF-IDF keywords with rank-derived pseudo-beta

• Hybrid result (has $combined_topics) -> weighted combined keywords

Usage

extract_topic_terms_df(model, n = 7)

Arguments

model

A topic model object (STM fit, embedding result, or hybrid result).

n

Number of top terms per topic (default 7).

Value

data.frame(topic, term, beta) in long format.

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_diversity(), calculate_topic_probability(), calculate_topic_stability(), 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()