Computes word co-occurrence networks with community detection and network metrics. Supports multiple feature spaces: unigrams, n-grams, and embeddings. Based on proven implementation for intuitive network visualization.
Usage
semantic_cooccurrence_network(
dfm_object,
doc_var = NULL,
co_occur_n = 10,
top_node_n = 30,
node_label_size = 14,
pattern = NULL,
showlegend = TRUE,
seed = NULL,
feature_type = "words",
ngram_range = 2,
texts = NULL,
embeddings = NULL
)Arguments
- dfm_object
A quanteda document-feature matrix (dfm).
- doc_var
A document-level metadata variable for categories (default: NULL).
- co_occur_n
Minimum co-occurrence count (default: 10).
- top_node_n
Number of top nodes to display based on degree centrality (default: 30).
- node_label_size
Font size for node labels (default: 14).
- pattern
Regex pattern to filter specific words (default: NULL).
- showlegend
Whether to show community legend (default: TRUE).
- seed
Random seed for reproducible layout (default: NULL).
- feature_type
Feature space: "words", "ngrams", or "embeddings" (default: "words").
- ngram_range
N-gram size when feature_type = "ngrams" (default: 2).
- texts
Optional character vector of texts for n-gram creation (default: NULL).
- embeddings
Optional embedding matrix for embedding-based networks (default: NULL).