Replace multi-word phrases in text(s) with a compound version of the phrases concatenated with concatenator (by default, the "_" character) to form a single token. This prevents tokenization of the phrases during subsequent processing by eliminating the whitespace delimiter.

phrasetotoken(object, phrases, ...)

# S4 method for corpus,ANY
phrasetotoken(object, phrases, ...)

# S4 method for textORtokens,dictionary
phrasetotoken(object, phrases, ...)

# S4 method for textORtokens,collocations
phrasetotoken(object, phrases, ...)

# S4 method for character,character
phrasetotoken(object, phrases,
  concatenator = "_", valuetype = c("glob", "regex", "fixed"),
  case_insensitive = TRUE, ...)

# S4 method for tokenizedTexts,character
phrasetotoken(object, phrases,
  concatenator = "_", valuetype = c("glob", "regex", "fixed"),
  case_insensitive = TRUE, ...)

Arguments

object
source texts, a character or character vector
phrases
a dictionary object that contains some phrases, defined as multiple words delimited by whitespace, up to 9 words long; or a quanteda collocation object created by collocations
...
additional arguments passed through to core "character,character" method
concatenator
the concatenation character that will connect the words making up the multi-word phrases. The default _ is highly recommended since it will not be removed during normal cleaning and tokenization (while nearly all other punctuation characters, at least those in the Unicode punctuation class [P] will be removed.
valuetype
how to interpret keyword expressions: "glob" for "glob"-style wildcard expressions; "regex" for regular expressions; or "fixed" for exact matching. See valuetype for details.
case_insensitive
if TRUE, ignore case when matching

Value

character or character vector of texts with phrases replaced by compound "words" joined by the concatenator

Examples

## Not run: ------------------------------------ # mytexts <- c("The new law included a capital gains tax, and an inheritance tax.", # "New York City has raised a taxes: an income tax and a sales tax.") # mydict <- dictionary(list(tax=c("tax", "income tax", "capital gains tax", "inheritance tax"))) # (cw <- phrasetotoken(mytexts, mydict)) # dfm(cw, verbose=FALSE) # # # when used as a dictionary for dfm creation # mydfm2 <- dfm(cw, dictionary = dictionary(lapply(mydict, function(x) gsub(" ", "_", x)))) # mydfm2 # # # to pick up "taxes" in the second text, set valuetype = "regex" # mydfm3 <- dfm(cw, dictionary = dictionary(lapply(mydict, phrasetotoken, mydict)), # valuetype = "regex") # mydfm3 # ## one more token counted for "tax" than before ## --------------------------------------------- # using a dictionary to pre-process multi-word expressions myDict <- dictionary(list(negative = c("bad* word*", "negative", "awful text"), postiive = c("good stuff", "like? th??"))) txt <- c("I liked this, when we can use bad words, in awful text.", "Some damn good stuff, like the text, she likes that too.") phrasetotoken(txt, myDict)
#> [1] "I liked this, when we can use bad words, in awful_text." #> [2] "Some damn good_stuff, like the text, she likes that too."
# on simple text phrasetotoken("This is a simpler version of multi word expressions.", "multi word expression*")
#> [1] "This is a simpler version of multi word expressions."
# on simple text toks <- tokenize("Simon sez the multi word expression plural is multi word expressions, Simon sez.") phrases <- c("multi word expression*", "Simon sez") phrasetotoken(toks, phrases)
#> tokenizedTexts from 1 document. #> Component 1 : #> [1] "Simon_sez" "the" "multi_word_expression" #> [4] "plural" "is" "multi_word_expressions" #> [7] "," "Simon_sez" "." #>