Calculates similarity between documents using traditional NLP methods or modern embedding-based approaches. Comprehensive metrics are automatically computed unless disabled.
Usage
calculate_document_similarity(
texts,
document_feature_type = "words",
semantic_ngram_range = 2,
similarity_method = "cosine",
use_embeddings = FALSE,
embedding_model = "all-MiniLM-L6-v2",
calculate_metrics = TRUE,
verbose = TRUE
)Arguments
- texts
A character vector of texts to compare.
- document_feature_type
Feature extraction type: "words", "ngrams", "embeddings", or "topics".
- semantic_ngram_range
Integer, n-gram range for ngram features (default: 2).
- similarity_method
Similarity calculation method: "cosine", "jaccard", "euclidean", "manhattan".
- use_embeddings
Logical, use embedding-based similarity (default: FALSE).
- embedding_model
Sentence transformer model name (default: "all-MiniLM-L6-v2").
- calculate_metrics
Logical, compute comprehensive similarity metrics (default: TRUE).
- verbose
Logical, if TRUE, prints progress messages.
Value
A list containing:
- similarity_matrix
N x N similarity matrix
- feature_matrix
Document feature matrix used for calculation
- method_info
Information about the method used
- metrics
Comprehensive similarity metrics (if calculate_metrics = TRUE)
- execution_time
Time taken for analysis
See also
Other semantic:
analyze_document_clustering(),
analyze_similarity_gaps(),
calculate_clustering_metrics(),
calculate_cross_similarity(),
calculate_similarity_robust(),
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 (interactive()) {
data(SpecialEduTech)
texts <- SpecialEduTech$abstract[1:5]
result <- calculate_document_similarity(
texts = texts,
document_feature_type = "words",
similarity_method = "cosine"
)
print(result$similarity_matrix)
print(result$metrics)
}