Topic Terms, Retuned Without Refitting — terms.sbert_topic

Recomputes the ranked terms of a fitted topic model under different term settings. Term extraction depends only on the fitted topic assignments and the document text, so every argument here can be changed without repeating the clustering or the encoding.

Usage

# S3 method for class 'sbert_topic_model'
terms(
  x,
  n = NULL,
  stop_words = NULL,
  min_term_frequency = NULL,
  min_token_length = NULL,
  weighting = NULL,
  reduce_frequent_words = NULL,
  stem = NULL,
  numbers = NULL,
  roman_numerals = NULL,
  section_numbers = NULL,
  smoothing = 0,
  sort_by = c("score", "beta"),
  ...
)

Arguments

x: A fitted model from topics().
n: Terms returned per topic, or NULL for the whole vocabulary (useful together with smoothing to obtain a full \(p(term | topic)\) distribution). Defaults to the model's fitted n_terms.
stop_words: Character vector excluded from terms. Defaults to the model's fitted list; see stop_words().
min_term_frequency: Minimum corpus-wide token frequency.
min_token_length: Minimum Unicode character length for a token.
weighting: Class-based term weighting, "ctfidf" or "bm25".
reduce_frequent_words: Square-root the within-topic frequency before weighting, damping very frequent words.
stem: Collapse inflected forms onto a shared Porter stem, displaying the most frequent surface form. Requires SnowballC.
numbers: How to treat purely numeric tokens: "keep" or "remove" (drop digit-only tokens such as years and counts). Defaults to the model's fitted setting.
roman_numerals: How to treat Roman-numeral tokens: "keep" or "remove" (drop chapter/section markers such as ii, iv, up to 100). Defaults to the model's fitted setting.
section_numbers: How to treat section, reference, and list numbering — multi-level indices such as 1.2.3 and enumeration markers such as 1., 2.: "keep" or "remove". Defaults to the model's fitted setting.
smoothing: Additive (Dirichlet) smoothing for the beta column. Only terms a topic actually used are returned, so with smoothing = 0 (default) each topic's beta sums to one, while any positive value reserves mass for the zero-count vocabulary and the returned rows sum to less than one.
sort_by: Order terms within each topic by "score" (default, the class-based weight — distinctive words) or "beta" (raw \(p(term | topic)\) — the words the topic uses most). n is applied after ordering.
...: Unused, present for generic consistency.

Value

A base data frame with one row per topic and term: topic, label, term, rank, score (the class-based weight), frequency (within-topic count), and beta, the multinomial \(p(term | topic)\).

Examples

text <- c(
  "Cats chase mice", "Kittens chase mice too", "Cats nap daily",
  "Stocks and bonds trade", "Markets price shares", "Banks report profit"
)
embeddings <- rbind(
  c(1, 0, 0), c(0.98, 0.02, 0), c(0.96, 0, 0.04),
  c(0, 1, 0), c(0.02, 0.98, 0), c(0, 0.96, 0.04)
)
fitted <- topics(
  text, n_topics = 2, embeddings = embeddings, min_term_frequency = 1
)
terms(fitted, n = 3)
#>   topic                  label    term rank     score frequency      beta
#> 1     1        Cats chase mice    cats    1 0.3788329         2 0.2222222
#> 2     1        Cats chase mice   chase    2 0.3788329         2 0.2222222
#> 3     1        Cats chase mice    mice    3 0.3788329         2 0.2222222
#> 4     2 Stocks and bonds trade   banks    1 0.2558428         1 0.1111111
#> 5     2 Stocks and bonds trade   bonds    2 0.2558428         1 0.1111111
#> 6     2 Stocks and bonds trade markets    3 0.2558428         1 0.1111111
terms(fitted, n = 3, weighting = "bm25")
#>   topic                  label    term rank     score frequency      beta
#> 1     1        Cats chase mice    cats    1 0.3080654         2 0.2222222
#> 2     1        Cats chase mice   chase    2 0.3080654         2 0.2222222
#> 3     1        Cats chase mice    mice    3 0.3080654         2 0.2222222
#> 4     2 Stocks and bonds trade   banks    1 0.2107911         1 0.1111111
#> 5     2 Stocks and bonds trade   bonds    2 0.2107911         1 0.1111111
#> 6     2 Stocks and bonds trade markets    3 0.2107911         1 0.1111111