Plot a Semantic Topic Model

Draws one of five deterministic base-graphics views of a fitted topic model.

Usage

# S3 method for class 'sbert_topic_model'
plot(
  x,
  type = c("sizes", "terms", "representatives", "fit", "map"),
  by = "score",
  topics = NULL,
  per_topic = FALSE,
  n_terms = 8L,
  n_representatives = 5L,
  colors = topic_palette(nrow(x$topics)),
  max_points = 1500L,
  main = NULL,
  ...
)

Arguments

x

An sbert_topic_model returned by topics().

type

One of "sizes" (document count per topic), "terms" (top terms per topic), "representatives" (a ranked text list of the centroid-nearest documents per topic), "fit" (a per-topic report with the representative documents followed by all three term views – count, TF-IDF, beta – one row per topic), or "map" (a two-dimensional classical-MDS projection of the document embeddings, colored by topic). The "map" view requires a model fitted with keep_embeddings = TRUE.

by

For type = "terms", one or more of "score" (class-based TF-IDF, the default and most distinctive terms), "beta" (the generative within-topic word probability), and "frequency" (the raw within-topic count). A single value draws one panel per topic; several values draw one row per topic with a column for each metric.

topics

For type "terms", "representatives", or "fit", an optional vector of topic numbers to restrict the plot to. Defaults to all topics.

per_topic

For type "terms", "representatives", or "fit", draw a separate figure for each topic instead of arranging all topics into one gridded figure. Most useful with a device or knitr chunk that keeps every figure (each topic then becomes its own image).

n_terms

Number of top terms shown per panel when type is "terms" or "fit".

n_representatives

Number of representative documents shown per panel when type is "representatives" or "fit". When more are requested than the model stored at fit time, they are recomputed from the retained embeddings; if the model was fitted with keep_embeddings = FALSE, the stored set is used and a warning is issued.

colors

Optional vector of topic colors; defaults to topic_palette().

max_points

Maximum documents drawn when type = "map". Larger corpora are thinned to a deterministic stratified subsample so the classical-MDS projection stays tractable.

main

Optional figure title for type = "fit" drawn as one figure (it is not used with per_topic = TRUE). The default names the layout (term columns and representative documents).

...

Unused; present for S3 compatibility.

Value

Invisibly, x.

Examples

text <- c(
  "Cats chase mice", "Dogs chase balls",
  "Stocks and bonds trade", "Markets price shares",
  "Neural nets learn patterns", "Models train on data"
)
embeddings <- rbind(
  c(1, 0, 0), c(0.9, 0.1, 0),
  c(0, 1, 0), c(0.1, 0.9, 0),
  c(0, 0, 1), c(0.05, 0, 0.95)
)
topic_model <- topics(text, 3, embeddings = embeddings, keep_embeddings = TRUE)
plot(topic_model, type = "sizes")

plot(topic_model, type = "terms")

plot(topic_model, type = "terms", by = "frequency")

plot(topic_model, type = "terms", by = c("frequency", "score", "beta"), topics = 1)

plot(topic_model, type = "representatives")

plot(topic_model, type = "fit")

plot(topic_model, type = "fit", per_topic = TRUE)



plot(topic_model, type = "map")