Extract representative quotes for each STM topic — extract

Takes the result of sframe_run_stm_topics() (not a raw stm model object) and, for each topic, pulls the top n_quotes documents by topic-document probability using stm::findThoughts(), then maps each one back to its original respondent row via the mapping sframe_run_stm_topics() stored on result$fit.

Usage

extract_quotes(model, text, n_quotes = 3L)

Arguments

model: A stm_topics result list from sframe_run_stm_topics() (i.e. result, not result$fit and not the raw stm object).
text: The response vector the topic model was fit on: either the raw vector (one entry per original data row, indexed 1:1 by row number) or the clean_text_responses()-cleaned vector, which drops blank/missing rows and is therefore shorter, its positions no longer equal original row numbers once any earlier row was dropped. Both forms work correctly: when text carries the respondent attribute clean_text_responses() sets, that mapping is used to find each quote's real position; otherwise text is assumed to be the raw, 1:1-indexed vector.
n_quotes: Integer. Quotes to return per topic. Default 3L.

Value

A data.frame with columns topic, rank, respondent (the original row index in the data the model's text argument came from, not a document-matrix or corpus row index), and quote.

Examples

# \donttest{
if (requireNamespace("stm", quietly = TRUE) &&
    requireNamespace("tidytext", quietly = TRUE)) {
  demo <- sframe_demo_data()
  sf_plan(demo$instrument) <- list(list(
    id = "RQ1", research_question = "What themes appear in the comments?",
    family = "text_analysis", method = "stm_topics",
    roles = list(item = "comments"), options = list(k = 3, seed = 42)
  ))
  res <- run_analysis_plan(demo$responses, demo$instrument)
  quotes <- extract_quotes(res$RQ1, demo$responses$comments, n_quotes = 2)
  quotes
}
#>   topic rank respondent                                   quote
#> 1     1    1         11                   Clear online content.
#> 2     1    2         12                   Clear online content.
#> 3     2    1          5 Would like more sustainability details.
#> 4     2    2          8 Would like more sustainability details.
#> 5     3    1          2             Useful service information.
#> 6     3    2          3             Useful service information.
# }