dictionary(
x,
file = NULL,
format = NULL,
separator = " ",
tolower = TRUE,
tokenize = FALSE,
levels = 1:100,
encoding = "utf-8"
)a named list of valuetype patterns or an existing dictionary
object. See examples. This argument should be omitted if file is specified.
file identifier for a foreign dictionary.
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:
"wordstat"format used by Provalis Research's WordStat software
"LIWC"format used by the Linguistic Inquiry and Word Count software
"yoshikoder"format used by Yoshikoder software
"lexicoder"format used by Lexicoder
"YAML"the standard YAML format
the character in between multi-word dictionary values. This
defaults to " ".
if TRUE, convert all dictionary values to lowercase.
if TRUE segment dictionary values by separators to using
the built-in tokenizer. Useful for Japanese and Chinese dictionaries.
integers specifying the levels of entries in x or file to be
included in the object.
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.
A dictionary class object, essentially a specially classed named list of characters.
A dictionary object can include multi-word expressions segmented by
separator. When it is applied to tokens object, they match both sequences
of separate tokens and compounded tokens.
Dictionary objects can be subsetted using
[ and
[[, operating the same as the equivalent
list operators. If dictionary() is applied to existing
objects, it is possible to select levels.
Dictionary objects can be coerced from and to lists using as.dictionary()
and as.list(), and checked using is.dictionary().
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).
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/
corp <- corpus_subset(data_corpus_inaugural, Year > 2000)
toks <- tokens(corp)
dict <- dictionary(list(
tax = c("tax", "taxes", "taxing"), # fixed patterns
economy = list("econom*", # glob patterns
job = c("work*", "job*")), # nested keys
health = c("health care", "public health") # multi-word expressions
))
# compound tokens
tokens_compound(toks, pattern = dict) |>
dfm() |>
dfm_select(dict)
#> Document-feature matrix of: 7 documents, 16 features (65.18% sparse) and 4 docvars.
#> features
#> docs work economy taxes working public_health economic jobs health_care
#> 2001-Bush 4 2 1 1 1 0 0 0
#> 2005-Bush 6 0 0 0 0 1 0 0
#> 2009-Obama 6 3 0 0 0 0 3 1
#> 2013-Obama 4 1 0 0 0 2 2 1
#> 2017-Trump 1 0 1 0 0 0 4 0
#> 2021-Biden 6 0 0 0 0 0 3 1
#> features
#> docs worked workers
#> 2001-Bush 0 0
#> 2005-Bush 0 0
#> 2009-Obama 1 2
#> 2013-Obama 0 1
#> 2017-Trump 0 2
#> 2021-Biden 0 0
#> [ reached max_ndoc ... 1 more document, reached max_nfeat ... 6 more features ]
tokens_lookup(toks, dictionary = dict, levels = 1) |>
dfm()
#> Document-feature matrix of: 7 documents, 3 features (23.81% sparse) and 4 docvars.
#> features
#> docs tax economy health
#> 2001-Bush 1 7 1
#> 2005-Bush 0 7 0
#> 2009-Obama 0 17 1
#> 2013-Obama 1 12 1
#> 2017-Trump 1 8 0
#> 2021-Biden 0 10 1
#> [ reached max_ndoc ... 1 more document ]
# subset a dictionary
dict[1:2]
#> Dictionary object with 2 primary key entries and 2 nested levels.
#> - [tax]:
#> - tax, taxes, taxing
#> - [economy]:
#> - econom*
#> - [job]:
#> - work*, job*
dict[c("economy")]
#> Dictionary object with 1 primary key entry and 2 nested levels.
#> - [economy]:
#> - econom*
#> - [job]:
#> - work*, job*
# update a dictionary
dictionary(dict, levels = 2)
#> Dictionary object with 1 key entry.
#> - [job]:
#> - work*, job*
if (FALSE) { # \dontrun{
dfmat <- dfm(tokens(data_corpus_inaugural))
# import the Laver-Garry dictionary from Provalis Research
download.file("https://provalisresearch.com/Download/LaverGarry.zip",
tf <- tempfile(), mode = "wb")
unzip(tf, exdir = (td <- tempdir()))
dict_lg <- dictionary(file = paste(td, "LaverGarry.cat", sep = "/"))
dfm_lookup(dfmat, dict_lg)
# import a LIWC formatted dictionary from http://www.moralfoundations.org
download.file("http://bit.ly/37cV95h", tf <- tempfile())
dict_liwc <- dictionary(file = tf, format = "LIWC")
dfm_lookup(dfmat, dict_liwc)
} # }