This function performs embedding-based topic modeling using transformer embeddings and specialized clustering techniques. The primary method uses the BERTopic library, which combines transformer embeddings with UMAP dimensionality reduction and HDBSCAN clustering for optimal topic discovery. This approach creates more semantically coherent topics compared to traditional methods by leveraging deep learning embeddings.
Usage
fit_embedding_topics(
texts,
method = "umap_hdbscan",
n_topics = 10,
embedding_model = "all-MiniLM-L6-v2",
clustering_method = "kmeans",
similarity_threshold = 0.7,
min_topic_size = 3,
umap_neighbors = 15,
umap_min_dist = 0,
umap_n_components = 5,
representation_method = "c-tfidf",
diversity = 0.5,
reduce_outliers = TRUE,
seed = 123,
verbose = TRUE
)Arguments
- texts
A character vector of texts to analyze.
- method
The topic modeling method: "umap_hdbscan" (uses BERTopic), "embedding_clustering", "hierarchical_semantic".
- n_topics
The number of topics to identify. For UMAP+HDBSCAN, use NULL or "auto" for automatic determination, or specify an integer.
- embedding_model
The embedding model to use (default: "all-MiniLM-L6-v2").
- clustering_method
The clustering method for embedding-based approach: "kmeans", "hierarchical", "dbscan", "hdbscan".
- similarity_threshold
The similarity threshold for topic assignment (default: 0.7).
- min_topic_size
The minimum number of documents per topic (default: 3).
- umap_neighbors
The number of neighbors for UMAP dimensionality reduction (default: 15).
- umap_min_dist
The minimum distance for UMAP (default: 0.0). Use 0.0 for tight, well-separated clusters. Use 0.1+ for visualization purposes. Range: 0.0-0.99.
- umap_n_components
The number of UMAP components (default: 5).
- representation_method
The method for topic representation: "c-tfidf", "tfidf", "mmr", "frequency" (default: "c-tfidf").
- diversity
Topic diversity parameter between 0 and 1 (default: 0.5).
- reduce_outliers
Logical, if TRUE, reduces outliers in HDBSCAN clustering (default: TRUE).
- seed
Random seed for reproducibility (default: 123).
- verbose
Logical, if TRUE, prints progress messages.
Examples
if (interactive()) {
mydata <- TextAnalysisR::SpecialEduTech
united_tbl <- TextAnalysisR::unite_cols(
mydata,
listed_vars = c("title", "keyword", "abstract")
)
texts <- united_tbl$united_texts
# Embedding-based topic modeling (powered by BERTopic)
result <- TextAnalysisR::fit_embedding_topics(
texts = texts,
method = "umap_hdbscan",
n_topics = 8,
min_topic_size = 3
)
print(result$topic_assignments)
print(result$topic_keywords)
}