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Categorize Texts

Source: R/lma_patcat.R
lma_patcat.Rd

Categorize raw texts using a pattern-based dictionary.

Usage

lma_patcat(text, dict = NULL, pattern.weights = "weight",
  pattern.categories = "category", bias = NULL, to.lower = TRUE,
  return.dtm = FALSE, drop.zeros = FALSE, exclusive = TRUE,
  boundary = NULL, fixed = TRUE, globtoregex = FALSE,
  name.map = c(intname = "_intercept", term = "term"),
  dir = getOption("lingmatch.dict.dir"))

Arguments

text

A vector of text to be categorized. Texts are padded by 2 spaces, and potentially lowercased.

dict

At least a vector of terms (patterns), usually a matrix-like object with columns for terms, categories, and weights.

pattern.weights

A vector of weights corresponding to terms in dict, or the column name of weights found in dict.

pattern.categories

A vector of category names corresponding to terms in dict, or the column name of category names found in dict.

bias

A constant to add to each category after weighting and summing. Can be a vector with names corresponding to the unique values in dict[, category], but is usually extracted from dict based on the intercept included in each category (defined by name.map['intname']).

to.lower

Logical indicating whether text should be converted to lowercase before processing.

return.dtm

Logical; if TRUE, only a document-term matrix will be returned, rather than the weighted, summed, and biased category values.

drop.zeros

logical; if TRUE, categories or terms with no matches will be removed.

exclusive

Logical; if FALSE, each dictionary term is searched for in the original text. Otherwise (by default), terms are sorted by length (with longer terms being searched for first), and matches are removed from the text (avoiding subsequent matches to matched patterns).

boundary

A string to add to the beginning and end of each dictionary term. If TRUE, boundary will be set to ' ', avoiding pattern matches within words. By default, dictionary terms are left as entered.

fixed

Logical; if FALSE, patterns are treated as regular expressions.

globtoregex

Logical; if TRUE, initial and terminal asterisks are replaced with \\b\\w* and \\w*\\b respectively. This will also set fixed to FALSE unless fixed is specified.

name.map

A named character vector:

  • intname: term identifying category biases within the term list; defaults to '_intercept'

  • term: name of the column containing terms in dict; defaults to 'term'

Missing names are added, so names can be specified positional (e.g., c('_int', 'terms')), or only some can be specified by name (e.g., c(term = 'patterns')), leaving the rest default.

dir

Path to a folder in which to look for dict if it is the name of a file to be passed to read.dic.

Value

A matrix with a row per text and columns per dictionary category, or (when return.dtm = TRUE) a sparse matrix with a row per text and column per term. Includes a WC attribute with original word counts, and a categories attribute with row indices associated with each category if return.dtm = TRUE.

See also

For applying term-based dictionaries (to a document-term matrix) see lma_termcat().

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

Examples

# example text
text <- c(
  paste(
    "Oh, what youth was! What I had and gave away.",
    "What I took and spent and saw. What I lost. And now? Ruin."
  ),
  paste(
    "God, are you so bored?! You just want what's gone from us all?",
    "I miss the you that was too. I love that you."
  ),
  paste(
    "Tomorrow! Tomorrow--nay, even tonight--you wait, as I am about to change.",
    "Soon I will off to revert. Please wait."
  )
)

# make a document-term matrix with pre-specified terms only
lma_patcat(text, c("bored?!", "i lo", ". "), return.dtm = TRUE)
#> 3 x 3 sparse Matrix of class "dgTMatrix"
#>      bored?! i lo . 
#> [1,]       .    1  4
#> [2,]       1    1  2
#> [3,]       .    .  3

# get counts of sets of letter
lma_patcat(text, list(c("a", "b", "c"), c("d", "e", "f")))
#>      cat1 cat2
#> [1,]   14    7
#> [2,]    8    8
#> [3,]   10    9
#> attr(,"WC")
#> [1] 21 16 19
#> attr(,"time")
#> patcat 
#>      0 

# same thing with regular expressions
lma_patcat(text, list("[abc]", "[def]"), fixed = FALSE)
#>      cat1 cat2
#> [1,]   14    7
#> [2,]    8    8
#> [3,]   10    9
#> attr(,"WC")
#> [1] 21 16 19
#> attr(,"time")
#> patcat 
#>      0 

# match only words
lma_patcat(text, list("i"), boundary = TRUE)
#>      category
#> [1,]        3
#> [2,]        2
#> [3,]        2
#> attr(,"WC")
#> [1] 3 2 2
#> attr(,"time")
#> patcat 
#>      0 

# match only words, ignoring punctuation
lma_patcat(
  text, c("you", "tomorrow", "was"),
  fixed = FALSE,
  boundary = "\\b", return.dtm = TRUE
)
#> 3 x 3 sparse Matrix of class "dgTMatrix"
#>      tomorrow you was
#> [1,]        .   .   1
#> [2,]        .   4   1
#> [3,]        2   1   .

if (FALSE) { # \dontrun{

# read in the temporal orientation lexicon from the World Well-Being Project
tempori <- read.csv(paste0(
  "https://raw.githubusercontent.com/wwbp/lexica/master/",
  "temporal_orientation/temporal_orientation_lexicon.csv"
))

lma_patcat(text, tempori)

# or use the standardized version
tempori_std <- read.dic("wwbp_prospection", dir = "~/Dictionaries")

lma_patcat(text, tempori_std)

## get scores on the same scale by adjusting the standardized values
tempori_std[, -1] <- tempori_std[, -1] / 100 *
  select.dict("wwbp_prospection")$selected[, "original_max"]

lma_patcat(text, tempori_std)[, unique(tempori$category)]
} # }