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Calculate Similarity Robust

Source: R/semantic_analysis.R
calculate_similarity_robust.Rd

Calculates document similarity with fallback methods and diagnostics. Attempts embeddings first, falls back to Jaccard similarity if needed.

Usage

calculate_similarity_robust(
  texts,
  method = "embeddings",
  embedding_model = "all-MiniLM-L6-v2",
  cache_embeddings = TRUE,
  min_word_length = 3,
  doc_names = NULL
)

Arguments

texts

Character vector of texts

method

Similarity method ("embeddings" or "jaccard")

embedding_model

Model name for embeddings (default: "all-MiniLM-L6-v2")

cache_embeddings

Logical, cache embeddings (default: TRUE)

min_word_length

Minimum word length for Jaccard (default: 3)

doc_names

Optional document names

Value

List containing similarity matrix, method used, embeddings, and diagnostics

See also

Other semantic: analyze_document_clustering(), analyze_similarity_gaps(), calculate_clustering_metrics(), calculate_cross_similarity(), calculate_document_similarity(), cluster_embeddings(), cross_analysis_validation(), export_document_clustering(), extract_cross_category_similarities(), fit_semantic_model(), generate_cluster_labels(), generate_cluster_labels_auto(), generate_embeddings(), reduce_dimensions(), semantic_document_clustering(), semantic_similarity_analysis(), temporal_semantic_analysis(), validate_cross_models(), word_co_occurrence_network(), word_correlation_network()

Examples

if (FALSE) { # \dontrun{
data(SpecialEduTech)
texts <- SpecialEduTech$abstract[1:5]

result <- calculate_similarity_robust(texts)
print(result$similarity_matrix)
print(result$diagnostics)
} # }