Obtain and Adjust the Topic Stop-Word List

Returns the stop words excluded from term extraction and keyword candidates. `add` extends the list — the standard way to remove a corpus-wide vocabulary from topic labels (words every document shares carry no discriminative information). `remove` un-lists built-in entries whose surface form is meaningful in a specific corpus.

Usage

stop_words(language = "en", add = NULL, remove = NULL)

Arguments

language

Currently only `"en"` is supported.

add

Character vector of extra words to exclude (matched case-insensitively).

remove

Character vector of words to drop from the list.

Value

A sorted character vector of lowercase stop words.

Details

For a fully custom list, pass any character vector straight to the `stop_words` argument of [topics()], [select_topics()], [topic_corpus()], or [keywords()] — for example `stop_words(add = c("covid", "patient"))` to keep the defaults plus domain terms, a bare `c("covid", "patient")` to replace the list entirely, or `character()` to disable stop-word filtering.

Examples

head(stop_words())
#> [1] "a"       "about"   "after"   "again"   "against" "all"    
# keep the defaults and add domain terms
custom <- stop_words(add = c("students", "learning"), remove = "against")

# use a custom list when modeling
text <- c("students learning math", "markets and trading stocks")
embeddings <- rbind(c(1, 0), c(0, 1))
topics(text, n_topics = 2, embeddings = embeddings, stop_words = custom)
#> <sbert_topic_model>
#>   documents: 2
#>   topics: 2
#>   model: precomputed embeddings
#>   algorithm: deterministic k-means (Lloyd)
#>   topic sizes: 1, 1
#>   between/total SS: 100.0%