qdap (version 2.2.1)

word_proximity: Proximity Matrix Between Words

Description

word_proximity - Generate proximity measures to ascertain a mean distance measure between word uses. weight - weight Method for word_proximity.

Usage

word_proximity(text.var, terms, grouping.var = NULL, parallel = TRUE,
  cores = parallel::detectCores()/2)

## S3 method for class 'word_proximity':
weight(x, type = "scale", ...)

Arguments

text.var

The text variable.

terms

A vector of quoted terms.

grouping.var

The grouping variables. Default NULL generates one word list for all text. Also takes a single grouping variable or a list of 1 or more grouping variables.

parallel

logical. If TRUE attempts to run the function on multiple cores. Note that this may not mean a speed boost if you have one core or if the data set is smaller as the cluster takes time to create.

cores

The number of cores to use if parallel = TRUE. Default is half the number of available cores.

x

An object to be weighted.

type

A weighting type of: c("scale_log", "scale", "rev_scale", "rev_scale_log", "log", "sqrt", "scale_sqrt", "rev_sqrt", "rev_scale_sqrt").

...

ignored.

Value

• Returns a list of matrices of proximity measures in the unit of average sentences between words (defaults to scaled).

Details

Note that row names are the first word and column names are the second comparison word. The values for Word A compared to Word B will not be the same as Word B compared to Word A. This is because, unlike a true distance measure, word_proximity's matrix is asymmetrical. word_proximity computes the distance by taking each sentence position for Word A and comparing it to the nearest sentence location for Word B.

See Also

word_proximity

Examples

wrds <- word_list(pres_debates2012$dialogue,
    stopwords = c("it's", "that's", Top200Words))
wrds2 <- tolower(sort(wrds$rfswl[[1]][, 1]))

(x <- with(pres_debates2012, word_proximity(dialogue, wrds2)))
plot(x)
plot(weight(x))
plot(weight(x, "rev_scale_log"))

(x2 <- with(pres_debates2012, word_proximity(dialogue, wrds2, person)))

## The spaces around `terms` are important
(x3 <- with(DATA, word_proximity(state, spaste(qcv(the, i)))))
(x4 <- with(DATA, word_proximity(state, qcv(the, i))))