Select Top Terms for Each Topic

This function selects the top terms for each topic based on their word probability distribution (beta).

Usage

get_topic_terms(stm_model, top_term_n = 10, verbose = TRUE, ...)

Arguments

stm_model

An STM model object.

top_term_n

The number of top terms to display for each topic (default: 10).

verbose

Logical, if TRUE, prints progress messages.

...

Further arguments passed to tidytext::tidy.

Value

A data frame containing the top terms for each topic.

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_probability(), 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_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()) {
  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)

  stm_15 <- TextAnalysisR::create_stm_model(
  dfm_object,
  topic_n = 15,
  max.em.its = 75,
  categorical_var = "reference_type",
  continuous_var = "year",
  verbose = TRUE
  )

  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)

top_topic_terms <- TextAnalysisR::get_topic_terms(
  stm_model = stm_15,
  top_term_n = 10,
  verbose = TRUE
  )
print(top_topic_terms)
}