qdap (version 2.3.0)

syllable_sum: Syllabication

Description

syllable_sum - Count the number of syllables per row of text.

syllable_count - Count the number of syllables in a single text string.

polysyllable_sum - Count the number of polysyllables per row of text.

combo_syllable_sum - Count the number of both syllables and polysyllables per row of text.

Usage

syllable_sum(text.var, parallel = FALSE, ...)
syllable_count(text, remove.bracketed = TRUE, algorithm.report = FALSE,
  env = qdap::env.syl)
polysyllable_sum(text.var, parallel = FALSE)
combo_syllable_sum(text.var, parallel = FALSE)

Arguments

text.var

The text variable

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.

text

A single character vector of text.

remove.bracketed

logical. If TRUE brackets are removed from the analysis.

algorithm.report

logical. If TRUE generates a report of words not found in the dictionary (i.e., syllables were calculated with an algorithm).

env

A lookup environment to lookup the number of syllables in found words.

…

Other arguments passed to syllable_count.

Value

syllable_sum - returns a vector of syllable counts per row.

syllable_count - returns a dataframe of syllable counts and algorithm/dictionary uses and, optionally, a report of words not found in the dictionary.

polysyllable_sum - returns a vector of polysyllable counts per row.

combo_syllable_sum - returns a dataframe of syllable and polysyllable counts per row.

Details

The worker function of all the syllable functions is syllable_count, though it is not intended for direct use on a transcript. This function relies on a combined dictionary lookup (based on the Nettalk Corpus (Sejnowski & Rosenberg, 1987)) and backup algorithm method.

References

Sejnowski, T.J., and Rosenberg, C.R. (1987). "Parallel networks that learn to pronounce English text" in Complex Systems, 1, 145-168.

Examples

# NOT RUN {
syllable_count("Robots like Dason lie.")
syllable_count("Robots like Dason lie.", algorithm.report = TRUE)

syllable_sum(DATA$state)
x1 <- syllable_sum(rajSPLIT$dialogue)
plot(x1)
cumulative(x1)

polysyllable_sum(DATA$state)
x2 <- polysyllable_sum(rajSPLIT$dialogue)
plot(x2)
cumulative(x2)

combo_syllable_sum(DATA$state)
x3 <- combo_syllable_sum(rajSPLIT$dialogue)
plot(x3) 
cumulative(x3)
# }