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Select Dictionaries

Source: R/select.dict.R
select.dict.Rd

Retrieve information and links to dictionaries (lexicons/word lists) available at osf.io/y6g5b.

Usage

select.dict(query = NULL, dir = getOption("lingmatch.dict.dir"),
  check.md5 = TRUE, mode = "wb")

Arguments

query

A character matching a dictionary name, or a set of keywords to search for in dictionary information.

dir

Path to a folder containing dictionaries, or where you want them to be saved. Will look in getOption('lingmatch.dict.dir') and '~/Dictionaries' by default.

check.md5

Logical; if TRUE (default), retrieves the MD5 checksum from OSF, and compares it with that calculated from the downloaded file to check its integrity.

mode

Passed to download.file when downloading files.

Value

A list with varying entries:

  • info: The version of osf.io/kjqb8 stored internally; a data.frame with dictionary names as row names, and information about each dictionary in columns.
    Also described at osf.io/y6g5b/wiki/dict_variables, here short (corresponding to the file name [{short}.(csv|dic)] and wiki urls [https://osf.io/y6g5b/wiki/{short}]) is set as row names and removed:

    • name: Full name of the dictionary.

    • description: Description of the dictionary, relating to its purpose and development.

    • note: Notes about processing decisions that additionally alter the original.

    • constructor: How the dictionary was constructed:

      • algorithm: Terms were selected by some automated process, potentially learned from data or other resources.

      • crowd: Several individuals rated the terms, and in aggregate those ratings translate to categories and weights.

      • mixed: Some combination of the other methods, usually in some iterative process.

      • team: One of more individuals make decisions about term inclusions, categories, and weights.

    • subject: Broad, rough subject or purpose of the dictionary:

      • emotion: Terms relate to emotions, potentially exemplifying or expressing them.

      • general: A large range of categories, aiming to capture the content of the text.

      • impression: Terms are categorized and weighted based on the impression they might give.

      • language: Terms are categorized or weighted based on their linguistic features, such as part of speech, specificity, or area of use.

      • social: Terms relate to social phenomena, such as characteristics or concerns of social entities.

    • terms: Number of unique terms across categories.

    • term_type: Format of the terms:

      • glob: Include asterisks which denote inclusion of any characters until a word boundary.

      • glob+: Glob-style asterisks with regular expressions within terms.

      • ngram: Includes any number of words as a term, separated by spaces.

      • pattern: A string of characters, potentially within or between words, or spanning words.

      • regex: Regular expressions.

      • stem: Unigrams with common endings removed.

      • unigram: Complete single words.

    • weighted: Indicates whether weights are associated with terms. This determines the file type of the dictionary: dictionaries with weights are stored as .csv, and those without are stored as .dic files.

    • regex_characters: Logical indicating whether special regular expression characters are present in any term, which might need to be escaped if the terms are used in regular expressions. Glob-type terms allow complete parens (at least one open and one closed, indicating preceding or following words), and initial and terminal asterisks. For all other terms, [](){}*.^$+?\| are counted as regex characters. These could be escaped in R with gsub('([][)(}{*.^$+?\\|])', '\\\1', terms) if terms is a character vector, and in Python with (importing re) [re.sub(r'([][(){}*.^$+?\|])', r'\\1', term) for term in terms] if terms is a list.

    • categories: Category names in the order in which they appear in the dictionary file, separated by commas.

    • ncategories: Number of categories.

    • original_max: Maximum value of the original dictionary before standardization: original values / max(original values) * 100. Dictionaries with no weights are considered to have a max of 1.

    • osf: ID of the file on OSF, translating to the file's URL: https://osf.io/osf.

    • wiki: URL of the dictionary's wiki.

    • downloaded: Path to the file if downloaded, and '' otherwise.

  • selected: A subset of info selected by query.

See also

Other Dictionary functions: dictionary_meta(), download.dict(), lma_patcat(), lma_termcat(), read.dic(), report_term_matches()

Examples

# just retrieve information about available dictionaries
dicts <- select.dict()$info
dicts[1:10, 4:9]
#>                  constructor    subject terms term_type weighted
#> adicat_function         team   language   759     glob+    FALSE
#> adict                  mixed     social 12168     ngram    FALSE
#> afinn                   team    emotion  3381   unigram     TRUE
#> agency_communion        team     social   447      glob    FALSE
#> allslang                team   language 10109     ngram    FALSE
#> anew                   crowd impression  1034   unigram     TRUE
#> anew_emotion           crowd    emotion  1034   unigram     TRUE
#> banbuilder              team impression   199   unigram    FALSE
#> banned                  team impression    77     ngram    FALSE
#> cost_benefit            team     social   154      glob    FALSE
#>                  regex_characters
#> adicat_function              TRUE
#> adict                        TRUE
#> afinn                       FALSE
#> agency_communion            FALSE
#> allslang                     TRUE
#> anew                        FALSE
#> anew_emotion                 TRUE
#> banbuilder                  FALSE
#> banned                      FALSE
#> cost_benefit                FALSE

# select all dictionaries mentioning sentiment or emotion
sentiment_dicts <- select.dict("sentiment emotion")$selected
sentiment_dicts[1:10, 4:9]
#>              constructor    subject  terms term_type weighted regex_characters
#> afinn               team    emotion   3381   unigram     TRUE            FALSE
#> anew_emotion       crowd    emotion   1034   unigram     TRUE             TRUE
#> depechemood    algorithm    emotion 114000   unigram     TRUE            FALSE
#> emolex         algorithm    emotion  28480   unigram     TRUE            FALSE
#> emosenticnet   algorithm    emotion  13175     ngram    FALSE             TRUE
#> emote              crowd impression   2197   unigram     TRUE            FALSE
#> galc                team    emotion    274      glob    FALSE            FALSE
#> huliu               team    emotion   6789   unigram    FALSE             TRUE
#> inquirer            team    general   8624   unigram    FALSE             TRUE
#> labmt              crowd    emotion   3934   unigram     TRUE            FALSE