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)
)
topic_model <- topics(text, 3, embeddings = embeddings, n_terms = 3)
topic_hierarchy(topic_model)
#> <sbert_topic_hierarchy> 3 topics, 2 merges (cosine distance)
#> 
#>  step      height                           left
#>     1 0.003556675     topic 2 (Dogs chase balls)
#>     2 0.877531228 topic 1 (Markets price shares)
#>                              right
#>  topic 3 (Kittens nap in sunshine)
#>                            merge 1