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Filtering of Stop Words for Tokens Variables

Source: R/stopwords.R
step_stopwords.Rd

step_stopwords() creates a specification of a recipe step that will filter a token variable for stop words.

Usage

step_stopwords(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  columns = NULL,
  language = "en",
  keep = FALSE,
  stopword_source = "snowball",
  custom_stopword_source = NULL,
  skip = FALSE,
  id = rand_id("stopwords")
)

Arguments

recipe

A recipes::recipe() object. The step will be added to the sequence of operations for this recipe.

...

One or more selector functions to choose which variables are affected by the step. See recipes::selections() for more details.

role

Not used by this step since no new variables are created.

trained

A logical to indicate if the quantities for preprocessing have been estimated.

columns

A character vector of the selected variable names. This is NULL until the step is trained by recipes::prep.recipe(), and should not be set by hand.

language

A single string indicating the language of the stop words by ISO 639-1 coding scheme. Ignored if custom_stopword_source is used. Defaults to "en".

keep

A logical. Should the stop words be kept instead of discarded? Defaults to FALSE.

stopword_source

A single string indicating the stop words source, as listed in stopwords::stopwords_getsources(). Ignored if custom_stopword_source is used. Defaults to "snowball".

custom_stopword_source

A character vector of words to use as a custom list of stop words, which cater to the user's specific problem. If NULL, the default, the list is taken from stopword_source and language. If used, language and stopword_source are ignored.

skip

A logical. Should the step be skipped when the recipe is baked by recipes::bake()? While all operations are baked when recipes::prep() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE, as it may affect the computations for subsequent operations.

id

A character string that is unique to this step to identify it.

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any).

Details

Stop words are words which sometimes are removed before natural language processing tasks. While stop words usually refers to the most common words in the language there is no universal stop word list.

The argument custom_stopword_source allows you to pass a character vector to filter against. With the keep argument one can specify words to keep instead of removing thus allowing you to select words with a combination of these two arguments.

Tidying

When you tidy() this step, a tibble is returned with columns terms, value, keep, and id:

terms

character, the selectors or variables selected

value

character, name of stop word list

keep

logical, whether stop words are removed or kept

id

character, id of this step

Case weights

The underlying operation does not allow for case weights.

See also

step_tokenize() to turn characters into tokens

Other Steps for Token Modification: step_lemma(), step_ngram(), step_pos_filter(), step_stem(), step_token_replace(), step_tokenfilter(), step_tokenmerge()

Examples

library(recipes)
library(modeldata)
data(tate_text)
tate_rec <- recipe(~., data = tate_text) |>
  step_tokenize(medium) |>
  step_stopwords(medium)

tate_obj <- tate_rec |>
  prep()

bake(tate_obj, new_data = NULL, medium) |>
  slice(1:2)
#> # A tibble: 2 × 1
#>       medium
#>    <tknlist>
#> 1 [6 tokens]
#> 2 [2 tokens]

bake(tate_obj, new_data = NULL) |>
  slice(2) |>
  pull(medium)
#> <textrecipes_tokenlist[1]>
#> [1] [2 tokens]
#> # Unique Tokens: 2

tidy(tate_rec, number = 2)
#> # A tibble: 1 × 4
#>   terms  value keep  id             
#>   <chr>  <chr> <lgl> <chr>          
#> 1 medium NA    NA    stopwords_TjhOC
tidy(tate_obj, number = 2)
#> # A tibble: 1 × 4
#>   terms  value    keep  id             
#>   <chr>  <chr>    <lgl> <chr>          
#> 1 medium snowball FALSE stopwords_TjhOC

# With a custom stop words list

tate_rec <- recipe(~., data = tate_text) |>
  step_tokenize(medium) |>
  step_stopwords(medium, custom_stopword_source = c("twice", "upon"))
tate_obj <- tate_rec |>
  prep(traimomg = tate_text)

bake(tate_obj, new_data = NULL) |>
  slice(2) |>
  pull(medium)
#> <textrecipes_tokenlist[1]>
#> [1] [3 tokens]
#> # Unique Tokens: 3