Fit Temporal Topic Model

Analyzes how topics evolve over time by fitting topic models to different time periods and tracking semantic changes.

Usage

fit_temporal_model(
  texts,
  dates,
  time_windows = "yearly",
  embeddings = NULL,
  verbose = TRUE
)

Arguments

texts

A character vector of text documents to analyze.

dates

A vector of dates corresponding to each document (will be converted to Date).

time_windows

Time grouping strategy: "yearly", "monthly", or "quarterly" (default: "yearly").

embeddings

Optional pre-computed embeddings matrix. If NULL, embeddings will be generated.

verbose

Logical indicating whether to print progress messages (default: TRUE).

Value

A list containing temporal analysis results with topic evolution patterns.

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_embedding_model(), fit_hybrid_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()