extract topic correlation network

get_network() is a helper function to extract topic correlation networks as tidygraph objects and add labels and topic proportions.

Arguments

model	The stm model for computing the correlation network.
method	The method for determining edges. Can be either 'simple' or 'huge'.
cutoff	The correlation cutoff criterion for method = 'cutoff'. Defaults to 0.05.
labels	An optional vector of topic labels. Must include a label for each topic of the model.
cutiso	Remove isolated notes without any edges from the network. Defaults to FALSE.

Value

Returns tidygraph network of topic correlations.

Examples

library(stm)
library(ggraph)
library(quanteda)

# prepare data
data <- corpus(gadarian, text_field = 'open.ended.response')
docvars(data)$text <- as.character(data)
data <- dfm(data, stem = TRUE, remove = stopwords('english'),
            remove_punct = TRUE)
out <- convert(data, to = 'stm')

# fit model
gadarian_10 <- stm(documents = out$documents,
                   vocab = out$vocab,
                   data = out$meta,
                   prevalence = ~ treatment + s(pid_rep),
                   K = 10,
                   max.em.its = 1, # reduce computation time for example
                   verbose = FALSE)

# extract network
stm_corrs <- get_network(model = gadarian_10,
                         method = 'simple',
                         labels = paste('Topic', 1:10),
                         cutoff = 0.001,
                         cutiso = TRUE)

# \dontrun{
# plot network
ggraph(stm_corrs, layout = 'fr') +
  geom_edge_link(
    aes(edge_width = weight),
    label_colour = '#fc8d62',
    edge_colour = '#377eb8') +
  geom_node_point(size = 4, colour = 'black')  +
  geom_node_label(
    aes(label = name, size = props),
    colour = 'black',  repel = TRUE, alpha = 0.85) +
  scale_size(range = c(2, 10), labels = scales::percent) +
  labs(size = 'Topic Proportion',  edge_width = 'Topic Correlation') +
  scale_edge_width(range = c(1, 3)) +
  theme_graph()
# }