rpackages ianhowson com cran qdap man polarity html
TRANSCRIPT
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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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