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Fit Semantic Model

Source: R/semantic_analysis.R
fit_semantic_model.Rd

Performs comprehensive semantic analysis including similarity, dimensionality reduction, and clustering. This is a high-level wrapper function.

Usage

fit_semantic_model(
  texts,
  analysis_types = c("similarity", "dimensionality_reduction", "clustering"),
  document_feature_type = "embeddings",
  similarity_method = "cosine",
  use_embeddings = TRUE,
  embedding_model = "all-MiniLM-L6-v2",
  dimred_method = "UMAP",
  clustering_method = "umap_dbscan",
  n_components = 2,
  n_clusters = 5,
  seed = 123,
  verbose = TRUE
)

Arguments

texts

A character vector of texts to analyze.

analysis_types

Types of analysis to perform: "similarity", "dimensionality_reduction", "clustering".

document_feature_type

Feature extraction type (default: "embeddings").

similarity_method

Similarity calculation method (default: "cosine").

use_embeddings

Logical, use embedding-based approaches (default: TRUE).

embedding_model

Sentence transformer model name (default: "all-MiniLM-L6-v2").

dimred_method

Dimensionality reduction method: "PCA", "t-SNE", "UMAP" (default: "UMAP").

clustering_method

Clustering method: "kmeans", "hierarchical", "umap_dbscan" (default: "umap_dbscan").

n_components

Number of dimensions for reduction (default: 2).

n_clusters

Number of clusters (default: 5).

seed

Random seed for reproducibility (default: 123).

verbose

Logical, if TRUE, prints progress messages.

Value

A list containing results from requested analyses.

See also

Other semantic: analyze_document_clustering(), analyze_similarity_gaps(), calculate_clustering_metrics(), calculate_cross_similarity(), calculate_document_similarity(), calculate_similarity_robust(), cluster_embeddings(), cross_analysis_validation(), export_document_clustering(), extract_cross_category_similarities(), 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 (interactive()) {
  data(SpecialEduTech)
  texts <- SpecialEduTech$abstract[1:10]

  results <- fit_semantic_model(
    texts = texts,
    analysis_types = c("similarity", "clustering")
  )

  print(results)
}