dictionary | R Documentation |
Create a quanteda dictionary class object, either from a list or by importing from a foreign format. Currently supported input file formats are the WordStat, LIWC, Lexicoder v2 and v3, and Yoshikoder formats. The import using the LIWC format works with all currently available dictionary files supplied as part of the LIWC 2001, 2007, and 2015 software (see References).
dictionary(
x,
file = NULL,
format = NULL,
separator = " ",
tolower = TRUE,
encoding = "utf-8"
)
x |
a named list of character vector dictionary entries, including
valuetype pattern matches, and including multi-word expressions
separated by |
file |
file identifier for a foreign dictionary |
format |
character identifier for the format of the foreign dictionary. If not supplied, the format is guessed from the dictionary file's extension. Available options are:
|
separator |
the character in between multi-word dictionary values. This
defaults to |
tolower |
if |
encoding |
additional optional encoding value for reading in imported dictionaries. This uses the iconv labels for encoding. See the "Encoding" section of the help for file. |
Dictionaries can be subsetted using
[
and
[[
, operating the same as the equivalent
list operators.
Dictionaries can be coerced from lists using as.dictionary()
,
coerced to named lists of characters using
as.list()
, and checked using
is.dictionary()
.
A dictionary class object, essentially a specially classed named list of characters.
WordStat dictionaries page, from Provalis Research https://provalisresearch.com/products/content-analysis-software/wordstat-dictionary/.
Pennebaker, J.W., Chung, C.K., Ireland, M., Gonzales, A., & Booth, R.J. (2007). The development and psychometric properties of LIWC2007. [Software manual]. Austin, TX (https://www.liwc.app/).
Yoshikoder page, from Will Lowe https://conjugateprior.org/software/yoshikoder/.
Lexicoder format, https://www.snsoroka.com/data-lexicoder/
as.dictionary()
,
as.list()
, is.dictionary()
corp <- corpus_subset(data_corpus_inaugural, Year>1900)
dict <- dictionary(list(christmas = c("Christmas", "Santa", "holiday"),
opposition = c("Opposition", "reject", "notincorpus"),
taxing = "taxing",
taxation = "taxation",
taxregex = "tax*",
country = "america"))
tokens(corp) |>
tokens_lookup(dictionary = dict) |>
dfm()
# subset a dictionary
dict[1:2]
dict[c("christmas", "opposition")]
dict[["opposition"]]
# combine dictionaries
c(dict["christmas"], dict["country"])
## Not run:
dfmat <- dfm(tokens(data_corpus_inaugural))
# import the Laver-Garry dictionary from Provalis Research
dictfile <- tempfile()
download.file("https://provalisresearch.com/Download/LaverGarry.zip",
dictfile, mode = "wb")
unzip(dictfile, exdir = (td <- tempdir()))
dictlg <- dictionary(file = paste(td, "LaverGarry.cat", sep = "/"))
dfm_lookup(dfmat, dictlg)
# import a LIWC formatted dictionary from http://www.moralfoundations.org
download.file("http://bit.ly/37cV95h", tf <- tempfile())
dictliwc <- dictionary(file = tf, format = "LIWC")
dfm_lookup(dfmat, dictliwc)
## End(Not run)
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