dfm_lookup: Apply a dictionary to a dfm
In koheiw/quanteda.core: Quantitative Analysis of Textual Data

Description Usage Arguments Note See Also Examples

Apply a dictionary to a dfm by looking up all dfm features for matches in a a set of dictionary values, and replace those features with a count of the dictionary's keys. If exclusive = FALSE then the behaviour is to apply a "thesaurus", where each value match is replaced by the dictionary key, converted to capitals if capkeys = TRUE (so that the replacements are easily distinguished from features that were terms found originally in the document).

dfm_lookup(
  x,
  dictionary,
  levels = 1:5,
  exclusive = TRUE,
  valuetype = c("glob", "regex", "fixed"),
  case_insensitive = TRUE,
  capkeys = !exclusive,
  nomatch = NULL,
  verbose = quanteda_options("verbose")
)

`x`	the dfm to which the dictionary will be applied
`dictionary`	a dictionary class object
`levels`	levels of entries in a hierarchical dictionary that will be applied
`exclusive`	if `TRUE`, remove all features not in dictionary, otherwise, replace values in dictionary with keys while leaving other features unaffected
`valuetype`	the type of pattern matching: `"glob"` for "glob"-style wildcard expressions; `"regex"` for regular expressions; or `"fixed"` for exact matching. See valuetype for details.
`case_insensitive`	logical; if `TRUE`, ignore case when matching a `pattern` or dictionary values
`capkeys`	if `TRUE`, convert dictionary keys to uppercase to distinguish them from other features
`nomatch`	an optional character naming a new feature that will contain the counts of features of `x` not matched to a dictionary key. If `NULL` (default), do not tabulate unmatched features.
`verbose`	print status messages if `TRUE`

If using dfm_lookup with dictionaries containing multi-word values, matches will only occur if the features themselves are multi-word or formed from ngrams. A better way to match dictionary values that include multi-word patterns is to apply tokens_lookup() to the tokens, and then construct the dfm.

dfm_replace

dict <- dictionary(list(christmas = c("Christmas", "Santa", "holiday"),
                          opposition = c("Opposition", "reject", "notincorpus"),
                          taxglob = "tax*",
                          taxregex = "tax.+$",
                          country = c("United_States", "Sweden")))
dfmat <- dfm(c("My Christmas was ruined by your opposition tax plan.",
               "Does the United_States or Sweden have more progressive taxation?"),
             remove = stopwords("english"))
dfmat

# glob format
dfm_lookup(dfmat, dict, valuetype = "glob")
dfm_lookup(dfmat, dict, valuetype = "glob", case_insensitive = FALSE)

# regex v. glob format: note that "united_states" is a regex match for "tax*"
dfm_lookup(dfmat, dict, valuetype = "glob")
dfm_lookup(dfmat, dict, valuetype = "regex", case_insensitive = TRUE)

# fixed format: no pattern matching
dfm_lookup(dfmat, dict, valuetype = "fixed")
dfm_lookup(dfmat, dict, valuetype = "fixed", case_insensitive = FALSE)

# show unmatched tokens
dfm_lookup(dfmat, dict, nomatch = "_UNMATCHED")