Document-Term Matrix Categorization

Reduces the dimensions of a document-term matrix by dictionary-based categorization.

Usage

lma_termcat(dtm, dict, term.weights = NULL, bias = NULL,
  bias.name = "_intercept", escape = TRUE, partial = FALSE,
  glob = TRUE, term.filter = NULL, term.break = 20000,
  to.lower = FALSE, dir = getOption("lingmatch.dict.dir"),
  coverage = FALSE)

Arguments

dtm

A matrix with terms as column names.

dict

The name of a provided dictionary (osf.io/y6g5b/wiki) or of a file found in dir, or a list object with named character vectors as word lists, or the path to a file to be read in by read.dic.

term.weights

A list object with named numeric vectors lining up with the character vectors in dict, used to weight the terms in each dict vector. If a category in dict is not specified in term.weights, or the dict and term.weights vectors aren't the same length, the weights for that category will be 1.

bias

A list or named vector specifying a constant to add to the named category. If a term matching bias.name is included in a category, it's associated weight will be used as the bias for that category.

bias.name

A character specifying a term to be used as a category bias; default is '_intercept'.

escape

Logical indicating whether the terms in dict should not be treated as plain text (including asterisk wild cards). If TRUE, regular expression related characters are escaped. Set to TRUE if you get PCRE compilation errors.

partial

Logical; if TRUE terms are partially matched (not padded by ^ and $).

glob

Logical; if TRUE (default), will convert initial and terminal asterisks to partial matches.

term.filter

A regular expression string used to format the text of each term (passed to gsub). For example, if terms are part-of-speech tagged (e.g., 'a_DT'), '_.*' would remove the tag.

term.break

If a category has more than term.break characters, it will be processed in chunks. Reduce from 20000 if you get a PCRE compilation error.

to.lower

Logical; if TRUE will lowercase dictionary terms. Otherwise, dictionary terms will be converted to match the terms if they are single-cased. Set to FALSE to always keep dictionary terms as entered.

dir

Path to a folder in which to look for dict;
will look in '~/Dictionaries' by default.
Set a session default with options(lingmatch.dict.dir = 'desired/path').

coverage

Logical; if TRUE, will calculate coverage (number of unique term matches) for each category.

Value

A matrix with a row per dtm row and columns per dictionary category (with added coverage_ versions if coverage is TRUE), and a WC attribute with original word counts.

See also

For applying pattern-based dictionaries (to raw text) see lma_patcat.

Other Dictionary functions: download.dict(), lma_patcat(), read.dic(), select.dict()

Examples

if (FALSE) {

# Score texts with the NRC Affect Intensity Lexicon

dict <- readLines("https://saifmohammad.com/WebDocs/NRC-AffectIntensity-Lexicon.txt")
dict <- read.table(
  text = dict[-seq_len(grep("term\tscore", dict, fixed = TRUE)[[1]])],
  col.names = c("term", "weight", "category")
)

text <- c(
  angry = paste(
    "We are outraged by their hateful brutality,",
    "and by the way they terrorize us with their hatred."
  ),
  fearful = paste(
    "The horrific torture of that terrorist was tantamount",
    "to the terrorism of terrorists."
  ),
  joyous = "I am jubilant to be celebrating the bliss of this happiest happiness.",
  sad = paste(
    "They are nearly suicidal in their mourning after",
    "the tragic and heartbreaking holocaust."
  )
)

emotion_scores <- lma_termcat(text, dict)
if (require("splot")) splot(emotion_scores ~ names(text), leg = "out")

## or use the standardized version (which includes more categories)

emotion_scores <- lma_termcat(text, "nrc_eil", dir = "~/Dictionaries")
emotion_scores <- emotion_scores[, c("anger", "fear", "joy", "sadness")]
if (require("splot")) splot(emotion_scores ~ names(text), leg = "out")
}