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