Analyze and Visualize Word Co-occurrence Networks — plot_cooccurrence

This function creates a word co-occurrence network based on a document-feature matrix (dfm).

Usage

plot_cooccurrence_network(
  dfm_object,
  doc_var = NULL,
  co_occur_n = 50,
  top_node_n = 30,
  nrows = 1,
  height = 800,
  width = 900,
  category = NULL,
  use_category_specific = FALSE,
  category_params = NULL
)

Arguments

dfm_object: A quanteda document-feature matrix (dfm).
doc_var: A document-level metadata variable (default: NULL).
co_occur_n: Minimum number of co-occurrences for filtering terms (default: 50).
top_node_n: Number of top nodes to display (default: 30).
nrows: Number of rows to display in the table (default: 1).
height: The height of the resulting Plotly plot, in pixels (default: 800).
width: The width of the resulting Plotly plot, in pixels (default: 900).
category: An optional category to filter the data (default: NULL).
use_category_specific: Logical; if TRUE, uses category-specific parameters (default: FALSE).
category_params: A named list of parameters for each category level (default: NULL).

Value

A list containing the Plotly plot, a data frame of the network layout, and the igraph graph object.

Examples

if (interactive()) {
  mydata <- TextAnalysisR::SpecialEduTech

  united_tbl <- TextAnalysisR::unite_cols(
    mydata,
    listed_vars = c("title", "keyword", "abstract")
  )

  tokens <- TextAnalysisR::prep_texts(united_tbl, text_field = "united_texts")

  dfm_object <- quanteda::dfm(tokens)

  # Overall
  word_co_occurrence_network_results <- TextAnalysisR::plot_cooccurrence_network(
                                        dfm_object,
                                        doc_var = "reference_type",
                                        co_occur_n = 30,
                                        top_node_n = 0,
                                        nrows = 1,
                                        height = 800,
                                        width = 900)

  print(word_co_occurrence_network_results$plot)
  print(word_co_occurrence_network_results$table)
  print(word_co_occurrence_network_results$summary)

  # Journal article
 category_params <- list(
  "journal_article" = list(co_occur_n = 80, top_node_n = 20),
  "thesis" = list(co_occur_n = 30, top_node_n = 20)
)

 word_co_occurrence_category <- TextAnalysisR::plot_cooccurrence_network(
  dfm_object,
  doc_var = "reference_type",
  use_category_specific = TRUE,
  category_params = category_params)

 print(word_co_occurrence_category$journal_article$plot)
 print(word_co_occurrence_category$journal_article$table)
 print(word_co_occurrence_category$journal_article$summary)

 # Thesis
 print(word_co_occurrence_category$thesis$plot)
 print(word_co_occurrence_category$thesis$table)
 print(word_co_occurrence_category$thesis$summary)

}