semnet: Create a semantic network based on the co-occurence of tokens...

In corpustools: Managing, Querying and Analyzing Tokenized Text

Create a semantic network based on the co-occurence of tokens in documents

Description

This function calculates the co-occurence of features and returns a network/graph in the igraph format, where nodes are tokens and edges represent the similarity/adjacency of tokens. Co-occurence is calcuated based on how often two tokens occured within the same document (e.g., news article, chapter, paragraph, sentence). The semnet_window() function can be used to calculate co-occurrence of tokens within a given token distance.

Usage

semnet(
  tc,
  feature = "token",
  measure = c("con_prob", "con_prob_weighted", "cosine", "count_directed",
    "count_undirected", "chi2"),
  context_level = c("document", "sentence"),
  backbone = F,
  n.batches = NA
)

Arguments

tc	a tCorpus or a featureHits object (i.e. the result of search_features)
feature	The name of the feature column
measure	The similarity measure. Currently supports: "con_prob" (conditional probability), "con_prob_weighted", "cosine" similarity, "count_directed" (i.e number of cooccurrences) and "count_undirected" (same as count_directed, but returned as an undirected network, chi2 (chi-square score))
context_level	Determine whether features need to co-occurr within "documents" or "sentences"
backbone	If True, add an edge attribute for the backbone alpha
n.batches	If a number, perform the calculation in batches

Value

an Igraph graph in which nodes are features and edges are similarity scores

Examples

text = c('A B C', 'D E F. G H I', 'A D', 'GGG')
tc = create_tcorpus(text, doc_id = c('a','b','c','d'), split_sentences = TRUE)

g = semnet(tc, 'token')
g
igraph::get.data.frame(g)
plot_semnet(g)

corpustools documentation built on May 31, 2023, 8:45 p.m.