Fits a hybrid topic model combining STM with embedding-based methods. This function integrates structural topic modeling (STM) with semantic embeddings for enhanced topic discovery. The STM component provides statistical rigor and covariate modeling capabilities, while the embedding component adds semantic coherence.
Effect Estimation: Covariate effects on topic prevalence can be estimated using
the STM component via stm::estimateEffect(). The embedding component provides
semantically meaningful topic representations but does not support direct covariate
modeling. This hybrid approach combines the best of both worlds: statistical inference
from STM and semantic quality from embeddings.
Usage
fit_hybrid_model(
texts,
metadata = NULL,
n_topics_stm = 10,
embedding_model = "all-MiniLM-L6-v2",
stm_prevalence = NULL,
stm_init_type = "Spectral",
alignment_method = "cosine",
verbose = TRUE,
seed = 123
)Arguments
- texts
A character vector of texts to analyze.
- metadata
Optional data frame with document metadata for STM covariate modeling.
- n_topics_stm
Number of topics for STM (default: 10).
- embedding_model
Embedding model name (default: "all-MiniLM-L6-v2").
- stm_prevalence
Formula for STM prevalence covariates (e.g., ~ category + s(year, df=3)).
- stm_init_type
STM initialization type (default: "Spectral").
- alignment_method
Method for aligning STM and embedding topics (default: "cosine").
- verbose
Logical, if TRUE, prints progress messages.
- seed
Random seed for reproducibility.
Value
A list containing:
stm_result: The STM model output (use this for effect estimation)
embedding_result: The embedding-based topic model output
alignment: Alignment metrics between the two models
combined_topics: Integrated topic representations
metadata: Metadata used in modeling (needed for effect estimation)
Note
For covariate effect estimation, use stm::estimateEffect() on the
stm_result$model component. The metadata must include the covariates
specified in stm_prevalence.
Examples
if (FALSE) { # \dontrun{
texts <- c("Computer-assisted instruction improves math skills for students with disabilities",
"Assistive technology supports reading comprehension for learning disabled students",
"Mobile devices enhance communication for students with autism spectrum disorder")
hybrid_model <- fit_hybrid_model(
texts = texts,
n_topics_stm = 3,
verbose = TRUE
)
} # }