Extract representative keywords from grouped nodes — sniff_groups

This function processes nodes grouped in a network (typically by community detection), and extracts the most frequent and the most distinctive keywords (using TF-IDF) from a descriptor field such as keywords or subject terms.

Usage

sniff_groups_keywords(net_groups, n_terms = 15, min_freq = 1, sep = ";")

Arguments

net_groups: A list containing a network component of class tbl_graph, where each node has at least two attributes: group and DE.
n_terms: Integer. The number of top terms to return per group, both by frequency and by TF-IDF. Default is 15.
min_freq: Integer. Minimum frequency a term must have in a group to be considered. Default is 2.
sep: Character. Separator used in the DE field to split multiple terms. Default is ";".

Value

A tibble with one row per group, containing two columns:

term_freq: the most frequent terms (with raw frequency).
term_tfidf: the most distinctive terms (with TF-IDF scores).

Examples

if (FALSE) { # \dontrun{
# Assuming 'groups' is output from sniff_groups()
groups_keywords <- sniff_groups_keywords(groups)
} # }