Summarize a Semantic Topic Model

Prints a compact scientific report – corpus size, cluster separation, coherence, and topic_diversity – and returns a tidy per-topic quality table.

Usage

# S3 method for class 'sbert_topic_model'
summary(object, measure = c("npmi", "umass"), n_terms = 10L, ...)

Arguments

object

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

measure

Coherence measure passed to [coherence()].

n_terms

Number of top terms per topic used for coherence and topic_diversity.

...

Unused; present for S3 compatibility.

Value

Invisibly, a data frame with one row per topic containing `topic`, `label`, `n_documents`, `proportion`, and `coherence`.

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))
topics <- topics(text, 2, embeddings = embeddings, n_terms = 3)
summary(topics)
#> Semantic topic model summary
#>   documents:            4
#>   topics:               2
#>   model:                precomputed embeddings
#>   between/total SS:      99.3%
#>   mean npmi  coherence:  -0.1667
#>   topic topic_diversity:      1.000 (top 10 terms)
#> 
#>  topic                   label n_documents proportion coherence
#>      1    chase / balls / cats           2        0.5    0.0000
#>      2 bonds / markets / price           2        0.5   -0.3333