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).