Filtering of Stop Words for Tokens Variables

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 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 string of variable names that will be populated (eventually) by the terms argument. This is NULL until the step is trained by recipes::prep.recipe().
language: A character to indicate the language of stop words by ISO 639-1 coding scheme.
keep: A logical. Specifies whether to keep the stop words or discard them.
stopword_source: A character to indicate the stop words source as listed in stopwords::stopwords_getsources.
custom_stopword_source: A character vector to indicate a custom list of words that cater to the users specific problem.
skip: A logical. Should the step be skipped when the recipe is baked by recipes::bake.recipe()? While all operations are baked when recipes::prep.recipe() 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 = FALSE.
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 remove 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 to keep the words instead of removing thus allowing you to select words with a combination of these two arguments.

Tidying

When you tidy() this step, a tibble with columns terms (the selectors or variables selected), value (name of stop word list), and keep (whether stop words are removed or kept).

Case weights

The underlying operation does not allow for case weights.

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_GTWJ0
tidy(tate_obj, number = 2)
#> # A tibble: 1 × 4
#>   terms  value    keep  id             
#>   <chr>  <chr>    <lgl> <chr>          
#> 1 medium snowball FALSE stopwords_GTWJ0

# 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