Returns the size of every topic as fitted (distinct units) 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, by = c("segment", "document"))Arguments
- object
A fitted [topics()] model.
- weights
Optional numeric vector with one non-negative weight per unit on the chosen scale — per fitted document (in document order), typically the `n` column of [dedupe()], or per segment when `by = "segment"` on a segmented model.
- by
`"segment"` (default; every fitted unit) or `"document"` (the distinct source documents).
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.
Details
For a model fitted with `segment = "sentence"`, `"clause"`, or `"phrase"`, `by` picks the scale: `"segment"` counts the fitted segments, `"document"` counts the source documents that have at least one segment in the topic. A document that spans several topics is counted in each, so document proportions need not sum to one. On a document-level model the two scales coincide.
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