Topic Sizes on the Distinct and Weighted Scales

Returns the size of every topic as fitted (distinct documents) and, when `weights` are supplied, on the weighted scale — for example the original row frequencies from [dedupe()]. The gap between `proportion` and `weighted_share` measures how template-driven a topic is: a topic whose weighted share far exceeds its distinct share is a small repertoire of heavily repeated texts.

Usage

topic_sizes(object, weights = NULL)

Arguments

object: A fitted [topics()] model.
weights: Optional numeric vector with one non-negative weight per fitted document (in document order), typically the `n` column of [dedupe()].

Value

A base data frame with one row per topic and columns `topic`, `label`, `n_documents`, and `proportion`, plus `n_weighted` and `weighted_share` when `weights` are supplied.

Examples

text <- c(
  "Cats chase mice", "Dogs chase balls",
  "Stocks and bonds trade", "Markets price shares"
)
embeddings <- rbind(c(1, 0), c(0.9, 0.1), c(0, 1), c(0.1, 0.9))
fitted <- topics(text, 2, embeddings = embeddings)
topic_sizes(fitted, weights = c(10, 1, 1, 1))
#>   topic                   label n_documents proportion n_weighted
#> 1     1    chase / balls / cats           2        0.5         11
#> 2     2 bonds / markets / price           2        0.5          2
#>   weighted_share
#> 1      0.8461538
#> 2      0.1538462