qdap (version 2.3.0)

pos: Parts of Speech Tagging

Description

pos - Apply part of speech tagger to transcript(s).

pos_by - Apply part of speech tagger to transcript(s) by zero or more grouping variable(s).

pos_tags - Useful for interpreting the parts of speech tags created by pos and pos_by.

Usage

pos(text.var, parallel = FALSE, cores = detectCores()/2,
  progress.bar = TRUE, na.omit = FALSE, digits = 1, percent = TRUE,
  zero.replace = 0, gc.rate = 10)
pos_by(text.var, grouping.var = NULL, digits = 1, percent = TRUE,
  zero.replace = 0, ...)
pos_tags(type = "pretty")

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.

cores

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

progress.bar

logical. If TRUE attempts to provide a OS appropriate progress bar. If parallel is TRUE this argument is ignored. Note that setting this argument to TRUE may slow down the function.

na.omit

logical. If TRUE missing values (NA) will be omitted.

digits

Integer; number of decimal places to round when printing.

percent

logical. If TRUE output given as percent. If FALSE the output is proportion.

zero.replace

Value to replace 0 values with.

gc.rate

An integer value. This is a necessary argument because of a problem with the garbage collection in the openNLP function that pos wraps. Consider adjusting this argument upward if the error java.lang.OutOfMemoryError occurs.

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.

type

An optional character string giving the output of the pos tags. This must be one of the strings "pretty" (a left justified version of the output optimized for viewing but not good for export), "matrix" (a matrix version of the output), "dataframe"\ "df" (a dataframe version of the output), "all" (a list of all three of the previous output types).

…

Other argument supplied to pos.

Value

pos - returns a list of 4:

text

The original text

POStagged

The original words replaced with parts of speech in context.

POSprop

Dataframe of the proportion of parts of speech by row.

POSfreq

Dataframe of the frequency of parts of speech by row.

POSrnp

Dataframe of the frequency and proportions of parts of speech by row.

percent

The value of percent used for plotting purposes.

zero.replace

The value of zero.replace used for plotting purposes.

pos_by - returns a list of 6:

text

The original text

POStagged

The original words replaced with parts of speech in context.

POSprop

Dataframe of the proportion of parts of speech by row.

POSfreq

Dataframe of the frequency of parts of speech by row.

POSrnp

Dataframe of the frequency and proportions of parts of speech by row.

pos.by.prop

Dataframe of the proportion of parts of speech by grouping variable.

pos.by.freq

Dataframe of the frequency of parts of speech by grouping variable.

pos.by.rnp

Dataframe of the frequency and proportions of parts of speech by grouping variable.

percent

The value of percent used for plotting purposes.

zero.replace

The value of zero.replace used for plotting purposes.

References

http:/opennlp.apache.org

See Also

Maxent_POS_Tag_Annotator, colcomb2class

Examples

# NOT RUN {
posdat <- pos(DATA$state)
ltruncdf(posdat, 7, 4)
## str(posdat)
names(posdat)
posdat$text           #original text

## Methods
preprocessed(posdat)  #words replaced with parts of speech
counts(posdat)        #frequency of parts of speech by row
proportions(posdat)   #proportion of parts of speech by row

## Methods Plotting
plot(preprocessed(posdat))
plot(counts(posdat))
plot(proportions(posdat))
plot(posdat)

out1 <- pos(DATA$state, parallel = TRUE) # not always useful
ltruncdf(out1, 7, 4)

#use pos_tags to interpret part of speech tags used by pos & pos_by
pos_tags()[1:10, ]
pos_tags("matrix")[1:10, ]
pos_tags("dataframe")[1:10, ]
pos_tags("df")[1:10, ]
ltruncdf(pos_tags("all"), 3)

posbydat <- with(DATA, pos_by(state, sex))
names(posbydat)

## Methods
scores(posbydat)   
preprocessed(posbydat)
counts(posbydat)     
proportions(posbydat)   

## Methods Plotting
plot(preprocessed(posbydat))
plot(counts(posbydat))
plot(proportions(posbydat))
plot(posbydat)

ltruncdf(posbydat, 7, 4)
truncdf(posbydat$pos.by.prop, 4)

POSby <- with(DATA, pos_by(state, list(adult, sex)))
plot(POSby, values = TRUE, digits = 2)
#or more quickly - reuse the output from before
out2 <- with(DATA, pos_by(posbydat, list(adult, sex)))

## Definite/Indefinite Noun 
## 2 approached compared...
## The later is more efficient but less accurate

## ------------------------##
## Part off speech tagging ##
## ------------------------##
pos_after <- function(text.var, words, pos){

    posses <- strsplit(as.character(text.var[["POStagged"]][["POStagged"]]), "\\s+")
    namespos <- lapply(posses, function(x) {
        y <- unlist(strsplit(x, "/"))
        setNames(y[c(TRUE, FALSE)], y[c(FALSE, TRUE)])
    })

    lapply(namespos, function(x, thewords = words, thepos = pos){
        locs <- which(x %in% thewords)
        locs <- locs[!is.na(locs)]

        if (identical(unclass(locs), integer(0))) return(NA_character_)

        nounlocs <- which(names(x) %in% thepos)

        unname(x[unique(sapply(locs, function(x){ 
            min(nounlocs[nounlocs - x > 0])
        }))])
    })  
}

out2 <- setNames(lapply(list(a=c("a", "an"), the="the"), function(x) {
    o <- pos_after(rajPOS, x, c("NN", "NNS", "NNP", "NNPS"))
    m <- stats::setNames(data.frame(sort(table(unlist(o))), 
        stringsAsFactors = FALSE), c("word", "freq"))
    m[m$freq> 3, ]
}), c("a", "the"))


dat2 <- setNames(Reduce(function(x, y) {
    merge(x, y, by = "word", all = TRUE)}, out2), c("Word", "A", "THE"))

dat2 <- reshape2::melt(dat2, id="Word", variable.name="Article", value.name="freq")

dat2 <- dat2[order(dat2$freq, dat2$Word), ]

ord2 <- aggregate(freq ~ Word, dat2, sum)

dat2$Word <- factor(dat2$Word, levels=ord2[order(ord2[[2]]), 1])
rownames(dat2) <- NULL
ggplot(dat2, aes(x=freq, y=Word)) +
    geom_point()+ facet_grid(~Article) +
    ggtitle("Part Of Speech Parsing Approach")

dev.new()

## --------------------##
## Regular Expressions ##
## --------------------##

library(qdapRegex);library(ggplot2);library(reshape2)

out <- setNames(lapply(c("@after_a", "@after_the"), function(x) {
    o <- rm_default(stringi:::stri_trans_tolower(raj$dialogue),
        pattern = x, extract=TRUE)
    m <- stats::setNames(data.frame(sort(table(unlist(o))), 
        stringsAsFactors = FALSE), c("word", "freq"))
    m[m$freq> 3, ]
}), c("a", "the"))

dat <- setNames(Reduce(function(x, y) {
    merge(x, y, by = "word", all = TRUE)}, out), c("Word", "A", "THE"))

dat <- reshape2::melt(dat, id="Word", variable.name="Article", value.name="freq")

dat <- dat[order(dat$freq, dat$Word), ]

ord <- aggregate(freq ~ Word, dat, sum)

dat$Word <- factor(dat$Word, levels=ord[order(ord[[2]]), 1])
rownames(dat) <- NULL
ggplot(dat, aes(x=freq, y=Word)) + 
    geom_point()+ facet_grid(~Article) + 
    ggtitle("Regex Approach")
# }