Build the Topic Hierarchy of a Fitted Model

Agglomeratively clusters the topic centroids by cosine distance and returns the merge tree: which topics are semantic neighbors, in what order they would fuse, and how far apart they are. Use it to judge whether a topic count is too fine (early merges at small heights are near-duplicate topics) and to choose a target count for [reduce_topics()]. Deterministic — no sampling, no seed.

Usage

topic_hierarchy(object, method = "average")

Arguments

object

An `sbert_topic_model` returned by [topics()].

method

Agglomeration method passed to [stats::hclust()]. Default `"average"`.

Value

An `sbert_topic_hierarchy`: a list with `merges` (a tidy data frame with one row per merge — `step`, `height`, and the human-readable `left` and `right` branch descriptions) and `tree` (the underlying `hclust` object, labeled with topic labels). `print()` shows the merge table; `plot()` draws the labeled dendrogram.

Examples

text <- c(
  "Cats chase mice", "Dogs chase balls", "Kittens nap in sunshine",
  "Stocks and bonds trade", "Markets price shares", "Banks report profit"
)
embeddings <- rbind(
  c(1, 0), c(0.95, 0.05), c(0.9, 0.1),
  c(0, 1), c(0.05, 0.95), c(0.1, 0.9)
)
topics <- topics(text, 3, embeddings = embeddings, n_terms = 3)
topic_hierarchy(topics)
#> <sbert_topic_hierarchy> 3 topics, 2 merges (cosine distance)
#> 
#>  step      height                              left
#>     1 0.003556675    topic 2 (chase / balls / cats)
#>     2 0.877531228 topic 1 (banks / bonds / markets)
#>                               right
#>  topic 3 (kittens / nap / sunshine)
#>                             merge 1