stminsights (version 0.4.3)

get_network: extract topic correlation network

Description

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

Value

Returns tidygraph network of topic correlations.

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.

Examples

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

# prepare data
data <- corpus(gadarian, text_field = 'open.ended.response')
docvars(data)$text <- as.character(data)

data <- tokens(data, remove_punct = TRUE) |>
  tokens_wordstem() |>
  tokens_remove(stopwords('english')) |> dfm() |>
  dfm_trim(min_termfreq = 2)

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)

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


# plot network
ggraph(stm_corrs, layout = 'auto') +
  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()
}