Computes quantitative dispersion metrics for terms, measuring how
evenly distributed they are across the corpus.
Usage
calculate_dispersion_metrics(tokens_object, terms)
Arguments
- tokens_object
A quanteda tokens object
- terms
Character vector of terms to analyze
Value
Data frame with columns:
term: The search term
frequency: Total occurrences
doc_count: Number of documents containing term
doc_ratio: Proportion of documents containing term
juilland_d: Juilland's D dispersion (0-1, higher = more even)
rosengren_s: Rosengren's S dispersion
