Calculate Topic Probabilities

Extracts and summarizes topic probabilities (gamma values) from an STM model, returning a formatted data table of mean topic prevalence.

Usage

calculate_topic_probability(stm_model, top_n = 10, verbose = TRUE, ...)

Arguments

stm_model

A fitted STM model object from stm::stm().

top_n

Number of top topics to display by prevalence (default: 10).

verbose

Logical, if TRUE prints progress messages (default: TRUE).

...

Additional arguments passed to tidytext::tidy().

Value

A DT::datatable showing topics and their mean gamma (prevalence) values, rounded to 3 decimal places.

See also

Other topic-modeling: analyze_semantic_evolution(), assess_embedding_stability(), assess_hybrid_stability(), auto_tune_embedding_topics(), calculate_assignment_consistency(), calculate_eval_metrics_internal(), calculate_keyword_stability(), calculate_semantic_drift(), calculate_topic_stability(), find_optimal_k(), find_topic_matches(), fit_embedding_model(), fit_hybrid_model(), fit_temporal_model(), generate_topic_labels(), get_topic_prevalence(), get_topic_terms(), get_topic_texts(), identify_topic_trends(), plot_model_comparison(), plot_quality_metrics(), run_contrastive_topics_internal(), run_neural_topics_internal(), run_temporal_topics_internal(), validate_semantic_coherence()

Examples

if (interactive()) {
  data <- TextAnalysisR::SpecialEduTech
  united <- unite_cols(data, c("title", "keyword", "abstract"))
  tokens <- prep_texts(united, text_field = "united_texts")
  dfm_obj <- quanteda::dfm(tokens)
  stm_data <- quanteda::convert(dfm_obj, to = "stm")

  topic_model <- stm::stm(
    documents = stm_data$documents,
    vocab = stm_data$vocab,
    K = 10,
    verbose = FALSE
  )

  prob_table <- calculate_topic_probability(topic_model, top_n = 10)
  print(prob_table)
}