Plot Per-Document Per-Topic Probabilities — plot_topic

This function generates a bar plot showing the prevalence of each topic across all documents.

Usage

plot_topic_probability(
  stm_model = NULL,
  gamma_data = NULL,
  top_n = 10,
  height = 800,
  width = 1000,
  topic_labels = NULL,
  colors = NULL,
  verbose = TRUE,
  ...
)

Arguments

stm_model: A fitted STM model object. where stm_model is a fitted Structural Topic Model created using stm::stm().
gamma_data: Optional pre-computed gamma data frame (default: NULL). If provided, used instead of stm_model.
top_n: The number of topics to display, ordered by their mean prevalence.
height: The height of the resulting Plotly plot, in pixels (default: 800).
width: The width of the resulting Plotly plot, in pixels (default: 1000).
topic_labels: Optional topic labels (default: NULL).
colors: Optional color palette for topics (default: NULL).
verbose: Logical, if TRUE, prints progress messages.
...: Further arguments passed to tidytext::tidy.

Value

A ggplot object showing a bar plot of topic prevalence. Topics are ordered by their mean gamma value (average prevalence across documents).

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)

 out <- quanteda::convert(dfm_object, to = "stm")

stm_15 <- stm::stm(
  data = out$meta,
  documents = out$documents,
  vocab = out$vocab,
  max.em.its = 75,
  init.type = "Spectral",
  K = 15,
  prevalence = ~ reference_type + s(year),
  verbose = TRUE)

topic_probability_plot <- TextAnalysisR::plot_topic_probability(
 stm_model = stm_15,
 top_n = 10,
 height = 800,
 width = 1000,
 verbose = TRUE)

print(topic_probability_plot)
}