Analyzes topic evolution over time periods using dynamic modeling approaches to track concept emergence, evolution, and decline.
Usage
run_temporal_topics_internal(
texts,
metadata = NULL,
n_topics = 10,
temporal_unit = "year",
temporal_window = 3,
detect_evolution = TRUE,
embedding_model = "all-MiniLM-L6-v2",
seed = 123
)Arguments
- texts
Character vector of documents
- metadata
Data frame containing temporal information
- n_topics
Number of topics to discover
- temporal_unit
Unit for temporal analysis ("year", "quarter", "month")
- temporal_window
Size of temporal window for analysis
- detect_evolution
Whether to detect topic evolution patterns
- embedding_model
Transformer model for embeddings
- seed
Random seed for reproducibility
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(),
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(),
validate_semantic_coherence()