Rpackages Ianhowson Com Cran Qdap Man Polarity HTML

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<- Back to Bridging the Gap Between Qualitative Data and Quantitative Analysis

Polarity Score (Sentiment Analysis)Descriptionpolarity - Approximate the sentiment (polarity) of text by grouping variable(s).

Usage1234567

polarity(text.var, grouping.var = NULL, polarity.frame = qdapDictionaries::key.pol, constrain = FALSE, negators = qdapDictionaries::negation.words, amplifiers = qdapDictionaries::amplification.words, deamplifiers = qdapDictionaries::deamplification.words, question.weight = 0, amplifier.weight = 0.8, n.before = 4, n.after = 2, rm.incomplete = FALSE, digits = 3, ...)

Argumentstext.var The text variable.

grouping.var The grouping variables. Default NULL generates one word list for all text. Also takes a single grouping

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variable or a list of 1 or more grouping variables.

polarity.frame A dataframe or hash key of positive/negative words and weights.

constrain logical. If TRUE polarity values are constrained to be between -1 and 1 using the following

transformation:

((1 - (1/(1 + exp(polarity)))) * 2) - 1

negators A character vector of terms reversing the intent of a positive or negative word.

amplifiers A character vector of terms that increase the intensity of a positive or negative word.

deamplifiers A character vector of terms that decrease the intensity of a positive or negative word.

question.weight The weighting of questions (values from 0 to 1). Default 0 corresponds with the belief that questions

(pure questions) are not polarized. A weight may be applied based on the evidence that the

questions function with polarity.

amplifier.weight The weight to apply to amplifiers/deamplifiers (values from 0 to 1). This value will multiply the

polarized terms by 1 + this value.

n.before The number of words to consider as valence shifters before the polarized word.

n.after The number of words to consider as valence shifters after the polarized word.

rm.incomplete logical. If TRUE text rows ending with qdap's incomplete sentence end mark ( | ) will be removed

from the analysis.

digits Integer; number of decimal places to round when printing.

... Other arguments supplied to strip .

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DetailsThe equation used by the algorithm to assign value to polarity of each sentence fist utilizes the sentiment dictionary (Hu

and Liu, 2004) to tag polarized words. A context cluster (x_i^T) of words is pulled from around this polarized word (default

4 words before and two words after) to be considered as valence shifters. The words in this context cluster are tagged as

neutral (x_i^0), negator (x_i^N), amplifier (x_i^a), or de-amplifier (x_i^d). Neutral words hold no value in the equation but do

affect word count (n). Each polarized word is then weighted w based on the weights from the polarity.frame argument

and then further weighted by the number and position of the valence shifters directly surrounding the positive or

negative word. The researcher may provide a weight c to be utilized with amplifiers/de-amplifiers (default is .8; deamplifier

weight is constrained to -1 lower bound). Last, these context cluster (x_i^T) are summed and divided by the square root of

the word count (√n) yielding an unbounded polarity score (C). Note that context clusters containing a comma before the

polarized word will only consider words found after the comma.

C=x_i^2/√(n)

Where:

x_i^T=∑((1 + c * (x_i^A - x_i^D)) * w(-1)^(∑x_i^N))

x_i^A=∑(w_neg * x_i^a)

x_i^D = max(x_i^D', -1)

x_i^D'=∑(- w_neg * x_i^a + x_i^d)

w_neg= (∑x_i^N) mod 2

ValueReturns a list of:

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all A dataframe of scores per row with:

group.var - the grouping variable

wc - word count

polarity - sentence polarity score

pos.words - words considered positive

neg.words - words considered negative

text.var - the text variable

group A dataframe with the average polarity score by grouping variable:

group.var - the grouping variable

total.sentences - Total sentences spoken.

total.words - Total words used.

ave.polarity - The sum of all polarity scores for that group divided by number of sentences spoken.

sd.polarity - The standard deviation of that group's sentence level polarity scores.

stan.mean.polarity - A standardized polarity score calculated by taking the average polarity score for a

group divided by the standard deviation.

digits integer value od number of digits to display; mostly internal use

Note

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The polarity score is dependent upon the polarity dictionary used. This function defaults to the word polarity dictionary

used by Hu, M., & Liu, B. (2004), however, this may not be appropriate for the context of children in a classroom. The user

may (is encouraged) to provide/augment the dictionary (see the sentiment_frame function). For instance the word

"sick" in a high school setting may mean that something is good, whereas "sick" used by a typical adult indicates

something is not right or negative connotation (deixis).

Also note that polarity assumes you've run sentSplit .

ReferencesHu, M., & Liu, B. (2004). Mining opinion features in customer reviews. National Conference on Artificial Intelligence.

http://www.slideshare.net/jeffreybreen/r-by-example-mining-twitter-for

http://hedonometer.org/papers.html Links to papers on hedonometrics

See Alsohttps://github.com/trestletech/Sermon-Sentiment-Analysis

Examples 1 2 3 4 5 6 7 8

## Not run: with(DATA, polarity(state, list(sex, adult)))(poldat <- with(sentSplit(DATA, 4), polarity(state, person)))counts(poldat)scores(poldat)plot(poldat)

poldat2 <- with(mraja1spl, polarity(dialogue,

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list(sex, fam.aff, died)))colsplit2df(scores(poldat2))plot(poldat2)plot(scores(poldat2))cumulative(poldat2)

poldat3 <- with(rajSPLIT, polarity(dialogue, person))poldat3[["group"]][, "OL"] <- outlier_labeler(scores(poldat3)[, "ave.polarity"])poldat3[["all"]][, "OL"] <- outlier_labeler(counts(poldat3)[, "polarity"])htruncdf(scores(poldat3), 10)htruncdf(counts(poldat3), 15, 8)plot(poldat3)plot(poldat3, nrow=4)qheat(scores(poldat3)[, -7], high="red", order.b="ave.polarity")

## Create researcher defined sentiment.framePOLKEY <- sentiment_frame(positive.words, negative.words)POLKEYc("abrasive", "abrupt", "happy") %hl% POLKEY

# Augmenting the sentiment.framemycorpus <- c("Wow that's a raw move.", "His jokes are so corny")counts(polarity(mycorpus))

POLKEY <- sentiment_frame(c(positive.words, "raw"), c(negative.words, "corny"))counts(polarity(mycorpus, polarity.frame=POLKEY))

## ANIMATION#===========

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(deb2 <- with(subset(pres_debates2012, time=="time 2"), polarity(dialogue, person)))

bg_black <- Animate(deb2, neutral="white", current.speaker.color="grey70")print(bg_black, pause=.75)

bgb <- vertex_apply(bg_black, label.color="grey80", size=20, color="grey40")bgb <- edge_apply(bgb, label.color="yellow")print(bgb, bg="black", pause=.75)

## Save itlibrary(animation)library(igraph)library(plotrix)

loc <- folder(animation_polarity)

## Set up the plotting functionoopt <- animation::ani.options(interval = 0.1)

FUN <- function() { Title <- "Animated Polarity: 2012 Presidential Debate 2" Legend <- c(-1.1, -1.25, -.2, -1.2) Legend.cex <- 1 lapply(seq_along(bgb), function(i) { par(mar=c(2, 0, 1, 0), bg="black") set.seed(10) plot.igraph(bgb[[i]], edge.curved=TRUE) mtext(Title, side=3, col="white") color.legend(Legend[1], Legend[2], Legend[3], Legend[4], c("Negative", "Neutral", "Positive"), attributes(bgb)[["legend"]],

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cex = Legend.cex, col="white") animation::ani.pause() })}

FUN()

## Detect OStype <- if(.Platform$OS.type == "windows") shell else system

saveHTML(FUN(), autoplay = FALSE, loop = TRUE, verbose = FALSE, ani.height = 500, ani.width=500, outdir = file.path(loc, "new"), single.opts = "'controls': ['first', 'play', 'loop', 'speed'], 'delayMin': 0")

## Detect OStype <- if(.Platform$OS.type == "windows") shell else system

saveHTML(FUN(), autoplay = FALSE, loop = TRUE, verbose = FALSE, ani.height = 1000, ani.width=650, outdir = loc, single.opts = "'controls': ['first', 'play', 'loop', 'speed'], 'delayMin': 0")

## Animated corresponding text plot Animate(deb2, type="text")

#=====================### Complex Animation ###=====================#library(animation)library(grid)

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library(gridBase)library(qdap)library(reports)library(igraph)library(plotrix)library(gridExtra)

deb2dat <- subset(pres_debates2012, time=="time 2")deb2dat[, "person"] <- factor(deb2dat[, "person"])(deb2 <- with(deb2dat, polarity(dialogue, person)))

## Set up the network versionbg_black <- Animate(deb2, neutral="white", current.speaker.color="grey70")bgb <- vertex_apply(bg_black, label.color="grey80", size=30, label.size=22, color="grey40")bgb <- edge_apply(bgb, label.color="yellow")

## Set up the bar versiondeb2_bar <- Animate(deb2, as.network=FALSE)

## Generate a folderloc2 <- folder(animation_polarity2)

## Set up the plotting functionoopt <- animation::ani.options(interval = 0.1)

FUN2 <- function(follow=FALSE, theseq = seq_along(bgb)) {

Title <- "Animated Polarity: 2012 Presidential Debate 2" Legend <- c(.2, -1.075, 1.5, -1.005)

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Legend.cex <- 1

lapply(theseq, function(i) { if (follow) { png(file=sprintf("%s/images/Rplot%s.png", loc2, i), width=650, height=725) } ## Set up the layout layout(matrix(c(rep(1, 9), rep(2, 4)), 13, 1, byrow = TRUE))

## Plot 1 par(mar=c(2, 0, 2, 0), bg="black") #par(mar=c(2, 0, 2, 0)) set.seed(20) plot.igraph(bgb[[i]], edge.curved=TRUE) mtext(Title, side=3, col="white") color.legend(Legend[1], Legend[2], Legend[3], Legend[4], c("Negative", "Neutral", "Positive"), attributes(bgb)[["legend"]], cex = Legend.cex, col="white")

## Plot2 plot.new() vps <- baseViewports()

uns <- unit(c(-1.3,.5,-.75,.25), "cm") p <- deb2_bar[[i]] + theme(plot.margin = uns, text=element_text(color="white"), plot.background = element_rect(fill = "black", color="black")) print(p,vp = vpStack(vps$figure,vps$plot))

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animation::ani.pause()

if (follow) { dev.off() } })

}

FUN2()

## Detect OStype <- if(.Platform$OS.type == "windows") shell else system

saveHTML(FUN2(), autoplay = FALSE, loop = TRUE, verbose = FALSE, ani.height = 1000, ani.width=650, outdir = loc2, single.opts = "'controls': ['first', 'play', 'loop', 'speed'], 'delayMin': 0")

FUN2(TRUE)

#=====================#library(animation)library(grid)library(gridBase)library(qdap)library(reports)library(igraph)library(plotrix)library(gplots)

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deb2dat <- subset(pres_debates2012, time=="time 2")deb2dat[, "person"] <- factor(deb2dat[, "person"])(deb2 <- with(deb2dat, polarity(dialogue, person)))

## Set up the network versionbg_black <- Animate(deb2, neutral="white", current.speaker.color="grey70")bgb <- vertex_apply(bg_black, label.color="grey80", size=30, label.size=22, color="grey40")bgb <- edge_apply(bgb, label.color="yellow")

## Set up the bar versiondeb2_bar <- Animate(deb2, as.network=FALSE)

## Set up the line versiondeb2_line <- plot(cumulative(deb2_bar))

## Generate a folderloc2b <- folder(animation_polarity2)

## Set up the plotting functionoopt <- animation::ani.options(interval = 0.1)

FUN2 <- function(follow=FALSE, theseq = seq_along(bgb)) {

Title <- "Animated Polarity: 2012 Presidential Debate 2" Legend <- c(.2, -1.075, 1.5, -1.005) Legend.cex <- 1

lapply(theseq, function(i) { if (follow) { png(file=sprintf("%s/images/Rplot%s.png", loc2b, i),

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width=650, height=725) } ## Set up the layout layout(matrix(c(rep(1, 9), rep(2, 4)), 13, 1, byrow = TRUE))

## Plot 1 par(mar=c(2, 0, 2, 0), bg="black") #par(mar=c(2, 0, 2, 0)) set.seed(20) plot.igraph(bgb[[i]], edge.curved=TRUE) mtext(Title, side=3, col="white") color.legend(Legend[1], Legend[2], Legend[3], Legend[4], c("Negative", "Neutral", "Positive"), attributes(bgb)[["legend"]], cex = Legend.cex, col="white")

## Plot2 plot.new() vps <- baseViewports()

uns <- unit(c(-1.3,.5,-.75,.25), "cm") p <- deb2_bar[[i]] + theme(plot.margin = uns, text=element_text(color="white"), plot.background = element_rect(fill = "black", color="black")) print(p,vp = vpStack(vps$figure,vps$plot)) animation::ani.pause()

if (follow) { dev.off() }

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})

}

FUN2()

## Detect OStype <- if(.Platform$OS.type == "windows") shell else system

saveHTML(FUN2(), autoplay = FALSE, loop = TRUE, verbose = FALSE, ani.height = 1000, ani.width=650, outdir = loc2b, single.opts = "'controls': ['first', 'play', 'loop', 'speed'], 'delayMin': 0")

FUN2(TRUE)

## Increased complexity## --------------------

## Helper function to cbind ggplotscbinder <- function(x, y){

uns_x <- unit(c(-1.3,.15,-.75,.25), "cm") uns_y <- unit(c(-1.3,.5,-.75,.15), "cm")

x <- x + theme(plot.margin = uns_x, text=element_text(color="white"), plot.background = element_rect(fill = "black", color="black") )

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y <- y + theme(plot.margin = uns_y, text=element_text(color="white"), plot.background = element_rect(fill = "black", color="black") )

plots <- list(x, y) grobs <- list() heights <- list()

for (i in 1:length(plots)){ grobs[[i]] <- ggplotGrob(plots[[i]]) heights[[i]] <- grobs[[i]]$heights[2:5] }

maxheight <- do.call(grid::unit.pmax, heights)

for (i in 1:length(grobs)){ grobs[[i]]$heights[2:5] <- as.list(maxheight) }

do.call("arrangeGrob", c(grobs, ncol = 2))}

deb2_combo <- Map(cbinder, deb2_bar, deb2_line)

## Generate a folderloc3 <- folder(animation_polarity3)

FUN3 <- function(follow=FALSE, theseq = seq_along(bgb)) {

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Title <- "Animated Polarity: 2012 Presidential Debate 2" Legend <- c(.2, -1.075, 1.5, -1.005) Legend.cex <- 1

lapply(theseq, function(i) { if (follow) { png(file=sprintf("%s/images/Rplot%s.png", loc3, i), width=650, height=725) } ## Set up the layout layout(matrix(c(rep(1, 9), rep(2, 4)), 13, 1, byrow = TRUE))

## Plot 1 par(mar=c(2, 0, 2, 0), bg="black") #par(mar=c(2, 0, 2, 0)) set.seed(20) plot.igraph(bgb[[i]], edge.curved=TRUE) mtext(Title, side=3, col="white") color.legend(Legend[1], Legend[2], Legend[3], Legend[4], c("Negative", "Neutral", "Positive"), attributes(bgb)[["legend"]], cex = Legend.cex, col="white")

## Plot2 plot.new() vps <- baseViewports() p <- deb2_combo[[i]] print(p,vp = vpStack(vps$figure,vps$plot)) animation::ani.pause()

if (follow) {

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dev.off() } })}

FUN3()

type <- if(.Platform$OS.type == "windows") shell else system

saveHTML(FUN3(), autoplay = FALSE, loop = TRUE, verbose = FALSE, ani.height = 1000, ani.width=650, outdir = loc3, single.opts = "'controls': ['first', 'play', 'loop', 'speed'], 'delayMin': 0")

FUN3(TRUE)

##-----------------------------#### Constraining between -1 & 1 ####-----------------------------#### The old behavior of polarity constrained the output to be between -1 and 1## this can be replicated via the `constrain = TRUE` argument:

polarity("really hate anger")polarity("really hate anger", constrain=TRUE)

#==================### Static Network ###==================#(poldat <- with(sentSplit(DATA, 4), polarity(state, person)))m <- Network(poldat)m

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print(m, bg="grey97", vertex.color="grey75")

print(m, title="Polarity Discourse Map", title.color="white", bg="black", legend.text.color="white", vertex.label.color = "grey70", edge.label.color="yellow")

## or use themes:dev.off()m + qtheme()m + theme_nightheatdev.off()m+ theme_nightheat(title="Polarity Discourse Map")

#===============================### CUMULATIVE POLARITY EXAMPLE ###===============================## Hedonometrics ##===============================#poldat4 <- with(rajSPLIT, polarity(dialogue, act, constrain = TRUE))

polcount <- na.omit(counts(poldat4)$polarity)len <- length(polcount)

cummean <- function(x){cumsum(x)/seq_along(x)}

cumpolarity <- data.frame(cum_mean = cummean(polcount), Time=1:len)

## Calculate background rectanglesends <- cumsum(rle(counts(poldat4)$act)$lengths)starts <- c(1, head(ends + 1, -1))rects <- data.frame(xstart = starts, xend = ends + 1,

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Act = c("I", "II", "III", "IV", "V"))

library(ggplot2)ggplot() + theme_bw() + geom_rect(data = rects, aes(xmin = xstart, xmax = xend, ymin = -Inf, ymax = Inf, fill = Act), alpha = 0.17) + geom_smooth(data = cumpolarity, aes(y=cum_mean, x = Time)) + geom_hline(y=mean(polcount), color="grey30", size=1, alpha=.3, linetype=2) + annotate("text", x = mean(ends[1:2]), y = mean(polcount), color="grey30", label = "Average Polarity", vjust = .3, size=3) + geom_line(data = cumpolarity, aes(y=cum_mean, x = Time), size=1) + ylab("Cumulative Average Polarity") + xlab("Duration") + scale_x_continuous(expand = c(0,0)) + geom_text(data=rects, aes(x=(xstart + xend)/2, y=-.04, label=paste("Act", Act)), size=3) + guides(fill=FALSE) + scale_fill_brewer(palette="Set1")

## End(Not run)

Animate: Generic Animate Method

Animate.character: Animate Character

Animate.discourse_map: Discourse Map

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Animate.formality: Animate Formality

Animate.gantt: Gantt Durations

Animate.gantt_plot: Gantt Plot

Animate.lexical_classification: Animate Formality

Animate.polarity: Animate Polarity

DATA: Fictitious Classroom Dialogue

DATA.SPLIT: Fictitious Split Sentence Classroom Dialogue

DATA2: Fictitious Repeated Measures Classroom Dialogue

Dissimilarity: Dissimilarity Statistics

Filter: Filter

NAer: Replace Missing Values (NA)

Network: Generic Network Method

Network.formality: Network Formality

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Network.lexical_classification: Network Lexical Classification

Network.polarity: Network Polarity

Readability: Readability Measures

Search: Search Columns of a Data Frame

Title: Add Title to Select qdap Plots

Trim: Remove Leading/Trailing White Space

Word_Frequency_Matrix: Word Frequency Matrix

addNetwork: Add themes to a Network object.

add_incomplete: Detect Incomplete Sentences; Add | Endmark

add_s: Make Plural (or Verb to Singular) Versions of Words

adjacency_matrix: Takes a Matrix and Generates an Adjacency Matrix

all_words: Searches Text Column for Words

as.tdm: tm Package Compatibility Tools: Apply to or Convert to/from...

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bag_o_words: Bag of Words

beg2char: Grab Begin/End of String to Character

blank2NA: Replace Blanks in a dataframe

bracketX: Bracket Parsing

build_qdap_vignette: Replace Temporary Introduction to qdap Vignette

capitalizer: Capitalize Select Words

chain: qdap Chaining

check_spelling: Check Spelling

check_spelling_interactive.character: Check Spelling

check_spelling_interactive.check_spelling: Check Spelling

check_spelling_interactive.factor: Check Spelling

check_text: Check Text For Potential Problems

chunker: Break Text Into Ordered Word Chunks

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clean: Remove Escaped Characters

cm_2long: A Generic to Long Function

cm_code.blank: Blank Code Transformation

cm_code.combine: Combine Codes

cm_code.exclude: Exclude Codes

cm_code.overlap: Find Co-occurrence Between Codes

cm_code.transform: Transform Codes

cm_combine.dummy: Find Co-occurrence Between Dummy Codes

cm_df.fill: Range Coding

cm_df.temp: Break Transcript Dialogue into Blank Code Matrix

cm_df.transcript: Transcript With Word Number

cm_df2long: Transform Codes to Start-End Durations

cm_distance: Distance Matrix Between Codes

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cm_dummy2long: Convert cm_combine.dummy Back to Long

cm_long2dummy: Stretch and Dummy Code cm_xxx2long

cm_range.temp: Range Code Sheet

cm_range2long: Transform Codes to Start-End Durations

cm_time.temp: Time Span Code Sheet

cm_time2long: Transform Codes to Start-End Times

colSplit: Separate a Column Pasted by paste2

colcomb2class: Combine Columns to Class

colsplit2df: Wrapper for colSplit that Returns Dataframe(s)

comma_spacer: Ensure Space After Comma

common: Find Common Words Between Groups

common.list: list Method for common

condense: Condense Dataframe Columns

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counts: Generic Counts Method

counts.SMOG: Readability Measures

counts.automated_readability_index: Readability Measures

counts.character_table: Term Counts

counts.coleman_liau: Readability Measures

counts.end_mark_by: Question Counts

counts.flesch_kincaid: Readability Measures

counts.formality: Formality

counts.fry: Readability Measures

counts.linsear_write: Readability Measures

counts.object_pronoun_type: Question Counts

counts.polarity: Polarity

counts.pos: Parts of Speech

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counts.pos_by: Parts of Speech

counts.pronoun_type: Question Counts

counts.question_type: Question Counts

counts.subject_pronoun_type: Question Counts

counts.termco: Term Counts

counts.word_length: Word Length Counts

counts.word_position: Word Position

counts.word_stats: Word Stats

cumulative: Cumulative Scores

data_viewing: Dataframe Viewing

dir_map: Map Transcript Files from a Directory to a Script

discourse_map: Discourse Mapping

dispersion_plot: Lexical Dispersion Plot

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dist_tab: SPSS Style Frequency Tables

diversity: Diversity Statistics

duplicates: Find Duplicated Words in a Text String

end_inc: Test for Incomplete Sentences

end_mark: Sentence End Marks

env.syl: Syllable Lookup Environment

exclude: Exclude Elements From a Vector

formality: Formality Score

freq_terms: Find Frequent Terms

gantt: Gantt Durations

gantt_plot: Gantt Plot

gantt_rep: Generate Unit Spans for Repeated Measures

gantt_wrap: Gantt Plot

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gradient_cloud: Gradient Word Cloud

hamlet: Hamlet (Complete & Split by Sentence)

igraph_params: Apply Parameter to List of Igraph Vertices/Edges

imperative: Intuitively Remark Sentences as Imperative

incomplete_replace: Denote Incomplete End Marks With "|"

inspect_text: Inspect Text Vectors

is.global: Test If Environment is Global

justification: Text Justification

key_merge: Merge Demographic Information with Person/Text Transcript

kullback_leibler: Kullback Leibler Statistic

lexical_classification: Lexical Classification Score

mraja1: Romeo and Juliet: Act 1 Dialogue Merged with Demographics

mraja1spl: Romeo and Juliet: Act 1 Dialogue Merged with Demographics and...

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mraja1spl: Romeo and Juliet: Act 1 Dialogue Merged with Demographics and...

multicsv: Read/Write Multiple csv Files at a Time

multigsub: Multiple gsub

multiscale: Nested Standardization

name2sex: Names to Gender

new_project: Project Template

ngrams: Generate ngrams

object_pronoun_type: Count Object Pronouns Per Grouping Variable

outlier_detect: Detect Outliers in Text

outlier_labeler: Locate Outliers in Numeric String

paste2: Paste an Unspecified Number Of Text Columns

phrase_net: Phrase Nets

plot.Network: Plots a Network Object

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plot.SMOG: Plots a SMOG Object

plot.animated_character: Plots an animated_character Object

plot.animated_discourse_map: Plots an animated_discourse_map Object

plot.animated_formality: Plots a animated_formality Object

plot.animated_lexical_classification: Plots an animated_lexical_classification Object

plot.animated_polarity: Plots an animated_polarity Object

plot.automated_readability_index: Plots a automated_readability_index Object

plot.character_table: Plots a character_table Object

plot.cm_distance: Plots a cm_distance object

plot.cmspans: Plots a cmspans object

plot.coleman_liau: Plots a coleman_liau Object

plot.combo_syllable_sum: Plots a combo_syllable_sum Object

plot.cumulative_animated_formality: Plots a cumulative_animated_formality Object

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plot.cumulative_animated_lexical_classification: Plots a cumulative_animated_lexical_classification Object

plot.cumulative_animated_polarity: Plots a cumulative_animated_polarity Object

plot.cumulative_combo_syllable_sum: Plots a cumulative_combo_syllable_sum Object

plot.cumulative_end_mark: Plots a cumulative_end_mark Object

plot.cumulative_formality: Plots a cumulative_formality Object

plot.cumulative_lexical_classification: Plots a cumulative_lexical_classification Object

plot.cumulative_polarity: Plots a cumulative_polarity Object

plot.cumulative_syllable_freq: Plots a cumulative_syllable_freq Object

plot.discourse_map: Plots a discourse_map Object

plot.diversity: Plots a diversity object

plot.end_mark: Plots an end_mark Object

plot.end_mark_by: Plots a end_mark_by Object

plot.end_mark_by_count: Plots a end_mark_by_count Object

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plot.end_mark_by_preprocessed: Plots a end_mark_by_preprocessed Object

plot.end_mark_by_proportion: Plots a end_mark_by_proportion Object

plot.end_mark_by_score: Plots a end_mark_by_score Object

plot.flesch_kincaid: Plots a flesch_kincaid Object

plot.formality: Plots a formality Object

plot.formality_scores: Plots a formality_scores Object

plot.freq_terms: Plots a freq_terms Object

plot.gantt: Plots a gantt object

plot.kullback_leibler: Plots a kullback_leibler object

plot.lexical: Plots a lexical Object

plot.lexical_classification: Plots a lexical_classification Object

plot.lexical_classification_preprocessed: Plots a lexical_classification_preprocessed Object

plot.lexical_classification_score: Plots a lexical_classification_score Object

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plot.linsear_write: Plots a linsear_write Object

plot.linsear_write_count: Plots a linsear_write_count Object

plot.linsear_write_scores: Plots a linsear_write_scores Object

plot.object_pronoun_type: Plots an object_pronoun_type Object

plot.polarity: Plots a polarity Object

plot.polarity_count: Plots a polarity_count Object

plot.polarity_score: Plots a polarity_score Object

plot.pos: Plots a pos Object

plot.pos_by: Plots a pos_by Object

plot.pos_preprocessed: Plots a pos_preprocessed Object

plot.pronoun_type: Plots an pronoun_type Object

plot.question_type: Plots a question_type Object

plot.question_type_preprocessed: Plots a question_type_preprocessed Object

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plot.readability_count: Plots a readability_count Object

plot.readability_score: Plots a readability_score Object

plot.rmgantt: Plots a rmgantt object

plot.sent_split: Plots a sent_split Object

plot.subject_pronoun_type: Plots an subject_pronoun_type Object

plot.sum_cmspans: Plot Summary Stats for a Summary of a cmspans Object

plot.sums_gantt: Plots a sums_gantt object

plot.syllable_freq: Plots a syllable_freq Object

plot.table_count: Plots a table_count Object

plot.table_proportion: Plots a table_proportion Object

plot.table_score: Plots a table_score Object

plot.termco: Plots a termco object

plot.type_token_ratio: Plots a type_token_ratio Object

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plot.weighted_wfm: Plots a weighted_wfm object

plot.wfdf: Plots a wfdf object

plot.wfm: Plots a wfm object

plot.word_cor: Plots a word_cor object

plot.word_length: Plots a word_length Object

plot.word_position: Plots a word_position object

plot.word_proximity: Plots a word_proximity object

plot.word_stats: Plots a word_stats object

plot.word_stats_counts: Plots a word_stats_counts Object

polarity: Polarity Score (Sentiment Analysis)

pos: Parts of Speech Tagging

potential_NA: Search for Potential Missing Values

power: Power Score (Sentiment Analysis)

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preprocessed: Generic Preprocessed Method

preprocessed.check_spelling_interactive: Check Spelling

preprocessed.end_mark_by: Question Counts

preprocessed.formality: Formality

preprocessed.lexical_classification: Lexical Classification

preprocessed.object_pronoun_type: Question Counts

preprocessed.pos: Parts of Speech

preprocessed.pos_by: Parts of Speech

preprocessed.pronoun_type: Question Counts

preprocessed.question_type: Question Counts

preprocessed.subject_pronoun_type: Question Counts

preprocessed.word_position: Word Position

pres_debate_raw2012: First 2012 U.S. Presidential Debate

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pres_debates2012: 2012 U.S. Presidential Debates

print.Dissimilarity: Prints a Dissimilarity object

print.Network: Prints a Network Object

print.SMOG: Prints an SMOG Object

print.adjacency_matrix: Prints an adjacency_matrix Object

print.all_words: Prints an all_words Object

print.animated_character: Prints an animated_character Object

print.animated_discourse_map: Prints an animated_discourse_map Object

print.animated_formality: Prints a animated_formality Object

print.animated_lexical_classification: Prints an animated_lexical_classification Object

print.animated_polarity: Prints an animated_polarity Object

print.automated_readability_index: Prints an automated_readability_index Object

print.boolean_qdap: Prints a boolean_qdap object

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print.character_table: Prints a character_table object

print.check_spelling: Prints a check_spelling Object

print.check_spelling_interactive: Prints a check_spelling_interactive Object

print.check_text: Prints a check_text Object

print.cm_distance: Prints a cm_distance Object

print.coleman_liau: Prints an coleman_liau Object

print.colsplit2df: Prints a colsplit2df Object.

print.combo_syllable_sum: Prints an combo_syllable_sum object

print.cumulative_animated_formality: Prints a cumulative_animated_formality Object

print.cumulative_animated_lexical_classification: Prints a cumulative_animated_lexical_classification Object

print.cumulative_animated_polarity: Prints a cumulative_animated_polarity Object

print.cumulative_combo_syllable_sum: Prints a cumulative_combo_syllable_sum Object

print.cumulative_end_mark: Prints a cumulative_end_mark Object

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print.cumulative_end_mark: Prints a cumulative_end_mark Object

print.cumulative_formality: Prints a cumulative_formality Object

print.cumulative_lexical_classification: Prints a cumulative_lexical_classification Object

print.cumulative_polarity: Prints a cumulative_polarity Object

print.cumulative_syllable_freq: Prints a cumulative_syllable_freqObject

print.discourse_map: Prints a discourse_map Object

print.diversity: Prints a diversity object

print.end_mark: Prints an end_mark object

print.end_mark_by: Prints an end_mark_by object

print.end_mark_by_preprocessed: Prints a end_mark_by_preprocessed object

print.flesch_kincaid: Prints an flesch_kincaid Object

print.formality: Prints a formality Object

print.formality_scores: Prints a formality_scores object

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print.fry: Prints an fry Object

print.inspect_text: Prints an inspect_text Object

print.kullback_leibler: Prints a kullback_leibler Object.

print.lexical_classification: Prints an lexical_classification Object

print.lexical_classification_by: Prints a lexical_classification Object

print.lexical_classification_preprocessed: Prints a lexical_classification_preprocessed Object

print.lexical_classification_score: Prints a lexical_classification_score Object

print.linsear_write: Prints an linsear_write Object

print.linsear_write_count: Prints a linsear_write_count Object

print.linsear_write_scores: Prints a linsear_write_scores Object

print.ngrams: Prints an ngrams object

print.object_pronoun_type: Prints a object_pronoun_type object

print.phrase_net: Prints a phrase_net Object

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print.polarity: Prints an polarity Object

print.polarity_count: Prints a polarity_count Object

print.polarity_score: Prints a polarity_score Object

print.polysyllable_sum: Prints an polysyllable_sum object

print.pos: Prints a pos Object.

print.pos_by: Prints a pos_by Object.

print.pos_preprocessed: Prints a pos_preprocessed object

print.pronoun_type: Prints a pronoun_type object

print.qdapProj: Prints a qdapProj Object

print.qdap_context: Prints a qdap_context object

print.question_type: Prints a question_type object

print.question_type_preprocessed: Prints a question_type_preprocessed object

print.readability_count: Prints a readability_count Object

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print.readability_score: Prints a readability_score Object

print.sent_split: Prints a sent_split object

print.sub_holder: Prints a sub_holder object

print.subject_pronoun_type: Prints a subject_pronoun_type object

print.sum_cmspans: Prints a sum_cmspans object

print.sums_gantt: Prints a sums_gantt object

print.syllable_sum: Prints an syllable_sum object

print.table_count: Prints a table_count object

print.table_proportion: Prints a table_proportion object

print.table_score: Prints a table_score object

print.termco: Prints a termco object.

print.trunc: Prints a trunc object

print.type_token_ratio: Prints a type_token_ratio Object

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print.wfm: Prints a wfm Object

print.wfm_summary: Prints a wfm_summary Object

print.which_misspelled: Prints a which_misspelled Object

print.word_associate: Prints a word_associate object

print.word_cor: Prints a word_cor object

print.word_length: Prints a word_length object

print.word_list: Prints a word_list Object

print.word_position: Prints a word_position object.

print.word_proximity: Prints a word_proximity object

print.word_stats: Prints a word_stats object

print.word_stats_counts: Prints a word_stats_counts object

pronoun_type: Count Object/Subject Pronouns Per Grouping Variable

prop: Convert Raw Numeric Matrix or Data Frame to Proportions

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proportions: Generic Proportions Method

proportions.character_table: Term Counts

proportions.end_mark_by: Question Counts

proportions.formality: Formality

proportions.object_pronoun_type: Question Counts

proportions.pos: Parts of Speech

proportions.pos_by: Parts of Speech

proportions.pronoun_type: Question Counts

proportions.question_type: Question Counts

proportions.subject_pronoun_type: Question Counts

proportions.termco: Term Counts

proportions.word_length: Word Length Counts

proportions.word_position: Word Position

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qcombine: Combine Columns

qcv: Quick Character Vector

qdap: qdap: Quantitative Discourse Analysis Package

qdap_df: Create qdap Specific Data Structure

qheat: Quick Heatmap

qprep: Quick Preparation of Text

qtheme: Add themes to a Network object.

question_type: Count of Question Type

raj: Romeo and Juliet (Unchanged & Complete)

raj.act.1: Romeo and Juliet: Act 1

raj.act.1POS: Romeo and Juliet: Act 1 Parts of Speech by Person

raj.act.2: Romeo and Juliet: Act 2

raj.act.3: Romeo and Juliet: Act 3

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raj.act.4: Romeo and Juliet: Act 4

raj.act.5: Romeo and Juliet: Act 5

raj.demographics: Romeo and Juliet Demographics

rajPOS: Romeo and Juliet Split in Parts of Speech

rajSPLIT: Romeo and Juliet (Complete & Split)

random_data: Generate Random Dialogue Data

rank_freq_plot: Rank Frequency Plot

raw.time.span: Minimal Raw Time Span Data Set

read.transcript: Read Transcripts Into R

replace_abbreviation: Replace Abbreviations

replace_contraction: Replace Contractions

replace_number: Replace Numbers With Text Representation

replace_ordinal: Replace Mixed Ordinal Numbers With Text Representation

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replace_symbol: Replace Symbols With Word Equivalents

replacer: Replace Cells in a Matrix or Data Frame

rm_row: Remove Rows That Contain Markers

rm_stopwords: Remove Stop Words

sample.time.span: Minimal Time Span Data Set

scores: Generic Scores Method

scores.SMOG: Readability Measures

scores.automated_readability_index: Readability Measures

scores.character_table: Term Counts

scores.coleman_liau: Readability Measures

scores.end_mark_by: Question Counts

scores.flesch_kincaid: Readability Measures

scores.formality: Formality

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scores.fry: Readability Measures

scores.lexical_classification: Lexical Classification

scores.linsear_write: Readability Measures

scores.object_pronoun_type: Question Counts

scores.polarity: Polarity

scores.pos_by: Parts of Speech

scores.pronoun_type: Question Counts

scores.question_type: Question Counts

scores.subject_pronoun_type: Question Counts

scores.termco: Term Counts

scores.word_length: Word Length Counts

scores.word_position: Word Position

scores.word_stats: Word Stats

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scores.word_stats: Word Stats

scrubber: Clean Imported Text

sentSplit: Sentence Splitting

space_fill: Replace Spaces

spaste: Add Leading/Trailing Spaces

speakerSplit: Break and Stretch if Multiple Persons per Cell

stemmer: Stem Text

strWrap: Wrap Character Strings to Format Paragraphs

strip: Strip Text

subject_pronoun_type: Count Subject Pronouns Per Grouping Variable

summary.cmspans: Summarize a cmspans object

summary.wfdf: Summarize a wfdf object

summary.wfm: Summarize a wfm object

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syllabication: Syllabication

synonyms: Search For Synonyms

t.DocumentTermMatrix: Transposes a DocumentTermMatrix object

t.TermDocumentMatrix: Transposes a TermDocumentMatrix object

termco: Search For and Count Terms

termco_c: Combine Columns from a termco Object

tot_plot: Visualize Word Length by Turn of Talk

trans_cloud: Word Clouds by Grouping Variable

trans_context: Print Context Around Indices

trans_venn: Venn Diagram by Grouping Variable

type_token_ratio: Type-Token Ratio

unique_by: Find Unique Words by Grouping Variable

visual: Generic visual Method

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0 Comments R Packages 1

visual.discourse_map: Discourse Map

weight: Weight a qdap Object

word_associate: Find Associated Words

word_cor: Find Correlated Words

word_count: Word Counts

word_diff_list: Differences In Word Use Between Groups

word_length: Count of Word Lengths Type

word_list: Raw Word Lists/Frequency Counts

word_network_plot: Word Network Plot

word_position: Word Position

word_proximity: Proximity Matrix Between Words

word_stats: Descriptive Word Statistics

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Detect otolith outline | shapeR1 comment • 5 months ago

Claire Moore — Hi everyone, I am trying to use thispackage (shapeR), I have set up the folders assuggested, and have all information in the …

Search results for 'lordif'1 comment • 6 months ago

Peyman Jafari — I intend to test DIF across twogroups based on PedsQl instrument. However,according to the reviewers comment …

Calculate the group 4 IHA parameters. | IHA1 comment • 5 months ago

alison — So if I would like to run Group4 using the type= 6 option, so that my results mimic the TNC's IHAsoftware how would I do this?

Model selection utility functions for 'tvcm' objects. |vcrpart1 comment • 9 days ago

Maira Fatoretto — hello how can use find the devianceresiduals to olmm?

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