tokens: Construct a tokens object
In koheiw/quanteda.core: Quantitative Analysis of Textual Data

Description Usage Arguments Details Value Details quanteda Tokenizers See Also Examples

Construct a tokens object, either by importing a named list of characters from an external tokenizer, or by calling the internal quanteda tokenizer.

tokens(
  x,
  what = "word",
  remove_punct = FALSE,
  remove_symbols = FALSE,
  remove_numbers = FALSE,
  remove_url = FALSE,
  remove_separators = TRUE,
  split_hyphens = FALSE,
  include_docvars = TRUE,
  padding = FALSE,
  verbose = quanteda_options("verbose"),
  ...
)

`x`	the input object to the tokens constructor, one of: a (uniquely) named list of characters; a tokens object; or a corpus or character object that will be tokenized
`what`	character; which tokenizer to use. The default `what = "word"` is the version 2 quanteda tokenizer. Legacy tokenizers (version < 2) are also supported, including the default `what = "word1"`. See the Details and quanteda Tokenizers below.
`remove_punct`	logical; if `TRUE` remove all characters in the Unicode "Punctuation" `[P]` class, with exceptions for those used as prefixes for valid social media tags if `preserve_tags = TRUE`
`remove_symbols`	logical; if `TRUE` remove all characters in the Unicode "Symbol" `[S]` class
`remove_numbers`	logical; if `TRUE` remove tokens that consist only of numbers, but not words that start with digits, e.g. `2day`
`remove_url`	logical; if `TRUE` find and eliminate URLs beginning with http(s)
`remove_separators`	logical; if `TRUE` remove separators and separator characters (Unicode "Separator" `[Z]` and "Control" `[C]` categories)
`split_hyphens`	logical; if `TRUE`, split words that are connected by hyphenation and hyphenation-like characters in between words, e.g. `"self-aware"` becomes `c("self", "-", "aware")`
`include_docvars`	if `TRUE`, pass docvars through to the tokens object. Does not apply when the input is a character data or a list of characters.
`padding`	if `TRUE`, leave an empty string where the removed tokens previously existed. This is useful if a positional match is needed between the pre- and post-selected tokens, for instance if a window of adjacency needs to be computed.
`verbose`	if `TRUE`, print timing messages to the console
`...`	used to pass arguments among the functions

tokens() works on tokens class objects, which means that the removal rules can be applied post-tokenization, although it should be noted that it will not be possible to remove things that are not present. For instance, if the tokens object has already had punctuation removed, then tokens(x, remove_punct = TRUE) will have no additional effect.

quanteda tokens class object, by default a serialized list of integers corresponding to a vector of types.

As of version 2, the choice of tokenizer is left more to the user, and tokens() is treated more as a constructor (from a named list) than a tokenizer. This allows users to use any other tokenizer that returns a named list, and to use this as an input to tokens(), with removal and splitting rules applied after this has been constructed (passed as arguments). These removal and splitting rules are conservative and will not remove or split anything, however, unless the user requests it.

Using external tokenizers is best done by piping the output from these other tokenizers into the tokens() constructor, with additional removal and splitting options applied at the construction stage. These will only have an effect, however, if the tokens exist for which removal is specified at in the tokens() call. For instance, it is impossible to remove punctuation if the input list to tokens() already had its punctuation tokens removed at the external tokenization stage.

To construct a tokens object from a list with no additional processing, call as.tokens() instead of tokens().

Recommended tokenizers are those from the tokenizers package, which are generally faster than the default (built-in) tokenizer but always splits infix hyphens, or spacyr.

The default word tokenizer what = "word" splits tokens using stri_split_boundaries(x, type = "word") but by default preserves infix hyphens (e.g. "self-funding"), URLs, and social media "tag" characters (#hashtags and @usernames), and email addresses. The rules defining a valid "tag" can be found here for hashtags and here for usernames.

In versions < 2, the argument remove_twitter controlled whether social media tags were preserved or removed, even when remove_punct = TRUE. This argument is not longer functional in versions >= 2. If greater control over social media tags is desired, you should user an alternative tokenizer, including non-quanteda options.

For backward compatibility, the following older tokenizers are also supported through what:

"word1": (legacy) implements similar behaviour to the version of what = "word" found in pre-version 2. (It preserves social media tags and infix hyphens, but splits URLs.) "word1" is also slower than "word".
"fasterword": (legacy) splits on whitespace and control characters, using stringi::stri_split_charclass(x, "[\\p{Z}\\p{C}]+")
"fastestword": (legacy) splits on the space character, using stringi::stri_split_fixed(x, " ")
"character": tokenization into individual characters
"sentence": sentence segmenter based on stri_split_boundaries, but with additional rules to avoid splits on words like "Mr." that would otherwise incorrectly be detected as sentence boundaries. For better sentence tokenization, consider using spacyr.

tokens_ngrams(), tokens_skipgrams(), as.list.tokens(), as.tokens()

txt <- c(doc1 = "A sentence, showing how tokens() works.",
         doc2 = "@quantedainit and #textanalysis https://example.com?p=123.",
         doc3 = "Self-documenting code??",
         doc4 = "£1,000,000 for 50¢ is gr8 4ever \U0001f600")
tokens(txt)
tokens(txt, what = "word1")

# removing punctuation marks but keeping tags and URLs
tokens(txt[1:2], remove_punct = TRUE)

# splitting hyphenated words
tokens(txt[3])
tokens(txt[3], split_hyphens = TRUE)

# symbols and numbers
tokens(txt[4])
tokens(txt[4], remove_numbers = TRUE)
tokens(txt[4], remove_numbers = TRUE, remove_symbols = TRUE)

## Not run: # using other tokenizers
tokens(tokenizers::tokenize_words(txt[4]), remove_symbols = TRUE)
tokenizers::tokenize_words(txt, lowercase = FALSE, strip_punct = FALSE) %>%
    tokens(remove_symbols = TRUE)
tokenizers::tokenize_characters(txt[3], strip_non_alphanum = FALSE) %>%
    tokens(remove_punct = TRUE)
tokenizers::tokenize_sentences(
    "The quick brown fox.  It jumped over the lazy dog.") %>%
    tokens()

## End(Not run)