‘Aping the People’: Populist Identification as Mimesis in Narendra Modi’s Speeches
Anonymous
13/04/2020
1 Introduction
NB for the reader
This is Markdown document of the submitted article ‘Aping the People’: Populist Identification as Mimesis in Narendra Modi’s Speeches.
2 Preparing the corpus for analysis
Preparation: Step 1
A dataframe is created. CSV and text files are in the same repository. Texts and CSVs are UTF8 formatted, CSV is comma separated.
setwd("C:/Users/jtmartelli/Google Drive/Textual_analysis/R/aping2")
txtvars <-read.csv("metadata.csv",stringsAsFactors = FALSE)
bodytexts <-readtext('*.txt')
bodytexts$id<-gsub('.txt','',bodytexts$doc_id)
dataframe<-merge(bodytexts,txtvars,by='id')Preparation: Step 2
A corpus called “workcorpus” is generated from the dataframe.
workcorpus <- corpus(dataframe)
texts(workcorpus) <- iconv(texts(workcorpus), from = "UTF-8", to = "ASCII", sub = "")Preparation: Step 3
A sub-corpus called “subworkcorpus” is created from the corpus “workcorpus” in order to analyze only the speeches of the corpus.
#head(docvars(workcorpus))
subworkcorpus<-corpus_subset(workcorpus, format %in% c('speech'))
ndoc(subworkcorpus)## [1] 4199Preparation: Step 4
The sub-corpus “subworkcorpus” is tokenized.
tokssubworkcorpus <- tokens(subworkcorpus, remove_punct = FALSE, remove_numbers = FALSE, remove_symbols = FALSE, remove_separators = TRUE, remove_hyphens = FALSE, remove_url = FALSE, concatenator = "_")
head(tokssubworkcorpus[[1]], 50)## [1] "Friends" "and" "comrades" "," "Jai"
## [6] "Hind" "!" "Six" "days" "ago"
## [11] "," "my" "colleagues" "and" "I"
## [16] "sat" "on" "the" "chairs" "of"
## [21] "high" "office" "in" "the" "Government"
## [26] "of" "India" "." "A" "new"
## [31] "Government" "came" "into" "being" "in"
## [36] "this" "ancient" "land" "," "the"
## [41] "Interim" "or" "Provisional" "Government" "we"
## [46] "called" "it" "," "the" "stepping"Preparation: Step 5
Unigrams proxying entries of variables of interest are replaced by ngrams.
popngrams <- read.csv("C:/Users/jtmartelli/Google Drive/Textual_analysis/R/aping/dictionaries/popngrams.csv", as.is = TRUE, header = FALSE)
dicopopgram <- dictionary(split(popngrams[,2], popngrams[,1]))
ngramstokssubworkcorpus <- tokens_lookup(tokssubworkcorpus, dicopopgram, valuetype = 'glob', exclusive = FALSE, capkeys = FALSE, case_insensitive = TRUE)Preparation: Step 6
A document-feature matrix is created from the tokenized corpus.
dfmtokssubworkcorpus <- dfm(ngramstokssubworkcorpus, remove_punct = FALSE, tolower = FALSE, dictionary_regex=TRUE, language = "english", stem = FALSE, clean = FALSE, verbose= TRUE) # if no ngram dictionary is loaded, then used the insert tokssubworkcorpus instead.3 The analysis
Dictionaries
A dictionary of populist features containing five-level hierarchical entries (see Fig.2).
#Step 1:
#Dictionaries are imported. There are two dictionaries: 5-level dictionary (nested lists) used for the analysis and a flat list for comparison purposes (Pauwels 2011). Only the 5-level dictionary is used for the analysis.
popdicoH <- dictionary(file = "C:/Users/jtmartelli/Google Drive/Textual_analysis/R/aping2/dictionaries/populism.yml", tolower = FALSE)
names(popdicoH) #that is the matrix one## [1] "poprelatedC" "poprelatedE"popdicoL <- dictionary(list(populism=c("elit*","consensus*","undemocratic*","referend*","currupt*","propagand*","politici*","deceit*","deceiv*","betray*","shame*","scandal*","truth*","dishonest*","establishm*","ruling*","absurd*", "arrogant*", "promis*", "promise*", "capitul*", "direct","mafia","freedom_of_expression","undemocratic","particrat*", "politic*","regime*","shameless","tradition*","people"))) #that is the list-wise dictionary using Pauwels (2011) list.
#Step2:
#Applying the dictionary to the DFM
popdicodfmHspeech1 <- dfm_lookup(dfmtokssubworkcorpus, dictionary = popdicoH, valuetype = "glob", levels=1)
popdicodfmHspeech2 <- dfm_lookup(dfmtokssubworkcorpus, dictionary = popdicoH, valuetype = "glob", levels=2)
popdicodfmHspeech3 <- dfm_lookup(dfmtokssubworkcorpus, dictionary = popdicoH, valuetype = "glob", levels=3)
popdicodfmHspeech4 <- dfm_lookup(dfmtokssubworkcorpus, dictionary = popdicoH, valuetype = "glob", levels=4)
popdicodfmHspeech5 <- dfm_lookup(dfmtokssubworkcorpus, dictionary = popdicoH, valuetype = "glob", levels=5)Computing the proportions of linguistic measures per PM for the five levels of the dictionary (1:5)
#In order to calculate proportions of linguistic measures among PMs, a column with the features that are not included in the dictionary is added in the DFM.
keeponlydico <- tokens_select(ngramstokssubworkcorpus, pattern = popdicoH, valuetype = 'glob', case_insensitive = FALSE, selection = 'keep')
dfmkeeponlydico <- dfm(keeponlydico, remove_punct = FALSE, tolower = FALSE, dictionary_regex=TRUE, language = "english", stem = FALSE, clean = FALSE, verbose= TRUE)
onlytherest <- tokens_select(ngramstokssubworkcorpus, pattern = popdicoH, valuetype = 'glob', case_insensitive = FALSE, selection = 'remove')
dfmonlytherest <- dfm(onlytherest, remove_punct = FALSE, tolower = FALSE, dictionary_regex=TRUE, language = "english", stem = FALSE, clean = FALSE, verbose= TRUE)
u <-dfmonlytherest
U<-rowSums(u)
#Adding to the DFM a column for Prime Ministers and another one for the year of the speeches
catdfmkeeponlydico1 <- dfm_lookup(dfmkeeponlydico, dictionary = popdicoH, levels=1)
v1<-cbind(catdfmkeeponlydico1,U)
v2<-convert(cbind(dfmtokssubworkcorpus@docvars$year,v1),to="data.frame")
v3<-cbind(dfmtokssubworkcorpus@docvars$loc,v2)
colnames(v3)[1]<-"PM"
colnames(v3)[2]<-"Speech"
colnames(v3)[3]<-"Year"
colnames(v3)[6]<-"TheRest"
catdfmkeeponlydico2 <- dfm_lookup(dfmkeeponlydico, dictionary = popdicoH, levels=2)
v4<-cbind(catdfmkeeponlydico2,U)
v5<-convert(cbind(dfmtokssubworkcorpus@docvars$year,v4),to="data.frame")
v6<-cbind(dfmtokssubworkcorpus@docvars$loc,v5)
colnames(v6)[1]<-"PM"
colnames(v6)[2]<-"Speech"
colnames(v6)[3]<-"Year"
colnames(v6)[8]<-"TheRest"
catdfmkeeponlydico3 <- dfm_lookup(dfmkeeponlydico, dictionary = popdicoH, levels=3)
v7<-cbind(catdfmkeeponlydico3,U)
v8<-convert(cbind(dfmtokssubworkcorpus@docvars$year,v7),to="data.frame")
v9<-cbind(dfmtokssubworkcorpus@docvars$loc,v8)
colnames(v9)[1]<-"PM"
colnames(v9)[2]<-"Speech"
colnames(v9)[3]<-"Year"
colnames(v9)[14]<-"TheRest"
catdfmkeeponlydico4 <- dfm_lookup(dfmkeeponlydico, dictionary = popdicoH, levels=4)
v10<-cbind(catdfmkeeponlydico4,U)
v11<-convert(cbind(dfmtokssubworkcorpus@docvars$year,v10),to="data.frame")
v12<-cbind(dfmtokssubworkcorpus@docvars$loc,v11)
colnames(v12)[1]<-"PM"
colnames(v12)[2]<-"Speech"
colnames(v12)[3]<-"Year"
colnames(v12)[54]<-"TheRest"
catdfmkeeponlydico5 <- dfm_lookup(dfmkeeponlydico, dictionary = popdicoH, levels=5)
v13<-cbind(catdfmkeeponlydico5,U)
v14<-convert(cbind(dfmtokssubworkcorpus@docvars$year,v13),to="data.frame")
v15<-cbind(dfmtokssubworkcorpus@docvars$loc,v14)
colnames(v15)[1]<-"PM"
colnames(v15)[2]<-"Speech"
colnames(v15)[3]<-"Year"
colnames(v15)[8]<-"TheRest"
#Computing the percentage of each column for the different levels of the dictionary
v16 <- v3
for(i in 1:nrow(v3)){
for(j in 4:ncol(v3)){
v16[i,j]<-v3[i,j]/sum(v3[i,(4:ncol(v3))])
}
}
v17 <- v6
for(i in 1:nrow(v6)){
for(j in 4:ncol(v6)){
v17[i,j]<-v6[i,j]/sum(v6[i,(4:ncol(v6))])
}
}
v18 <- v9
for(i in 1:nrow(v9)){
for(j in 4:ncol(v9)){
v18[i,j]<-v9[i,j]/sum(v9[i,(4:ncol(v9))])
}
}
v19 <- v12
for(i in 1:nrow(v12)){
for(j in 4:ncol(v12)){
v19[i,j]<-v12[i,j]/sum(v12[i,(4:ncol(v12))])
}
}
v20 <- v15
for(i in 1:nrow(v15)){
for(j in 4:ncol(v15)){
v20[i,j]<-v15[i,j]/sum(v15[i,(4:ncol(v15))])
}
}
#Grouping dictionary entries in three groups according to their relashionship with populism: prone, averse, neutral
#hypothesis A
proneA<-cbind(v17$popproneC)
averseA<-cbind(v17$popaverseC)
neutralA<-cbind(v17$popproneE+v17$popaverseE+v17$TheRest)
#hypothesis B
proneB<-cbind(v17$popproneE)
averseB<-cbind(v17$popaverseE)
neutralB<-cbind(v17$popproneC+v17$popaverseC+v17$TheRest)
#hypothesis A+B
proneAB<-cbind(v17$popproneC+v17$popproneE)
averseAB<-cbind(v17$popaverseC+v17$popaverseE)
neutralAB<-cbind(v17$TheRest)
#plotting results
plot(v17$PM, proneA, main="H1: Populist prone features across PMs (a)", sub="H1 variables: Deintermediation+Intimacy+Simplicity",
xlab="Prime Ministers", ylab="% of features")
plot(v17$PM, averseA, main="H1: Populist averse features across PMs (b)", sub="H1 variables: Deintermediation+Intimacy+Simplicity",
xlab="Prime Ministers", ylab="% of features")
plot(v17$PM, neutralA, main="H1: Populist neutral features across PMs (c)", sub="H1 variables: Deintermediation+Intimacy+Simplicity",
xlab="Prime Ministers", ylab="% of features")
plot(v17$PM, proneB, main="H2: Populist prone features across PMs (d)", sub="H2 variables: Acrimonious emotions+Authority",
xlab="Prime Ministers", ylab="% of features")
plot(v17$PM, averseB, main="H2: Populist averse features across PMs (e)", sub="H2 variables: Acrimonious emotions+Authority",
xlab="Prime Ministers", ylab="% of features")
plot(v17$PM, neutralB, main="H2: Populist neutral features across PMs (f)", sub="H2 variables: Acrimonious emotions+Authority",
xlab="Prime Ministers", ylab="% of features")
plot(v17$PM, proneAB, main="Populist prone", sub="H1+H2",
xlab="Prime Ministers", ylab="% of features")
plot(v17$PM, averseAB, main="Populist averse", sub="H1+H2",
xlab="Prime Ministers", ylab="% of features")
plot(v17$PM, neutralAB, main="Populist neutral", sub="H1+H2",
xlab="Prime Ministers", ylab="% of features")
#Computing Anovas for the selected variables of the different levels of the dictionary
v16t<-v16[,-1:-3]
v16t<-as.matrix(v16t)
PMs<-as.factor(v16$PM)
resultnewanova1 <- aov(v16t~PMs)
#summary(resultnewanova1)
#coefficients(resultnewanova1)
v17t<-v17[,-1:-3]
v17t<-as.matrix(v17t)
PMs<-as.factor(v17$PM)
resultnewanova2 <- aov(v17t~PMs)
#summary(resultnewanova2)
#coefficients(resultnewanova2)
v17a<-cbind(proneA,averseA,neutralA)
PMs<-as.factor(v17$PM)
resultnewanova2a <- aov(v17a~PMs)
#summary(resultnewanova2a)
#coefficients(resultnewanova2a)
v17b<-cbind(proneB,averseB,neutralB)
PMs<-as.factor(v17$PM)
resultnewanova2b <- aov(v17b~PMs)
#summary(resultnewanova2b)
#coefficients(resultnewanova2b)
v17ab<-cbind(proneAB,averseAB,neutralAB)
PMs<-as.factor(v17$PM)
resultnewanova2ab <- aov(v17ab~PMs)
#summary(resultnewanova2ab)
#coefficients(resultnewanova2ab)
v18t<-v18[,-1:-3]
v18t<-as.matrix(v18t)
PMs<-as.factor(v18$PM)
resultnewanova3 <- aov(v18t~PMs)
#summary(resultnewanova3)
#coefficients(resultnewanova3)
v19t<-v19[,-1:-3]
v19t<-as.matrix(v19t)
PMs<-as.factor(v19$PM)
resultnewanova4 <- aov(v19t~PMs)
#summary(resultnewanova4)
#coefficients(resultnewanova4)
v20t<-v20[,-1:-3]
v20t<-as.matrix(v20t)
PMs<-as.factor(v20$PM)
resultnewanova5 <- aov(v20t~PMs)
#summary(resultnewanova5)
#coefficients(resultnewanova5)
#Extracting the F values from the Anovas for the various levels of the dictionary
fvalues1<-c()
for (f in 1:3){
fvalues1 <- c(fvalues1,summary.aov(resultnewanova1)[[f]][["F value"]][[1]])
}
fvalues1<-round(fvalues1,digits=2)
#fvalues1
fvalues2<-c()
for (f in 1:5){
fvalues2 <- c(fvalues2,summary.aov(resultnewanova2)[[f]][["F value"]][[1]])
}
fvalues2<-round(fvalues2,digits=2)
#fvalues2
fvalues2a<-c()
for (f in 1:3){
fvalues2a <- c(fvalues2a,summary.aov(resultnewanova2a)[[f]][["F value"]][[1]])
}
fvalues2a<-round(fvalues2a,digits=2)
#fvalues2a
fvalues2b<-c()
for (f in 1:3){
fvalues2b <- c(fvalues2b,summary.aov(resultnewanova2b)[[f]][["F value"]][[1]])
}
fvalues2b<-round(fvalues2b,digits=2)
#fvalues2b
fvalues2ab<-c()
for (f in 1:3){
fvalues2ab <- c(fvalues2ab,summary.aov(resultnewanova2ab)[[f]][["F value"]][[1]])
}
fvalues2ab<-round(fvalues2ab,digits=2)
#fvalues2ab
fvalues3<-c()
for (f in 1:11){
fvalues3 <- c(fvalues3,summary.aov(resultnewanova3)[[f]][["F value"]][[1]])
}
fvalues3<-round(fvalues3,digits=2)
#fvalues3
fvalues4<-c()
for (f in 1:51){
fvalues4 <- c(fvalues4,summary.aov(resultnewanova4)[[f]][["F value"]][[1]])
}
fvalues4<-round(fvalues4,digits=2)
#fvalues4
fvalues5<-c()
for (f in 1:5){
fvalues5 <- c(fvalues5,summary.aov(resultnewanova5)[[f]][["F value"]][[1]])
}
fvalues5<-round(fvalues5,digits=2)
#fvalues5
#Extracting the effect sizes estimates over every column of the various dictionrary levels
resultanovapm1<-c()
for(i in 1:3)
{
anovatemp<-aov(v16t[,i]~as.factor(v16$PM))
correctiontemp<-eta_sq(anovatemp)
correctionnumeric<-round(as.numeric(as.character(correctiontemp[2])),digits=3)
resultanovapm1<-c(resultanovapm1,correctionnumeric)
}
#resultanovapm1
resultanovapm2<-c()
for(i in 1:5)
{
anovatemp<-aov(v17t[,i]~as.factor(v17$PM))
correctiontemp<-eta_sq(anovatemp)
correctionnumeric<-round(as.numeric(as.character(correctiontemp[2])),digits=3)
resultanovapm2<-c(resultanovapm2,correctionnumeric)
}
#resultanovapm2
resultanovapm2a<-c()
for(i in 1:3)
{
anovatemp<-aov(v17a[,i]~as.factor(v17$PM))
correctiontemp<-eta_sq(anovatemp)
correctionnumeric<-round(as.numeric(as.character(correctiontemp[2])),digits=3)
resultanovapm2a<-c(resultanovapm2a,correctionnumeric)
}
#resultanovapm2a
resultanovapm2b<-c()
for(i in 1:3)
{
anovatemp<-aov(v17b[,i]~as.factor(v17$PM))
correctiontemp<-eta_sq(anovatemp)
correctionnumeric<-round(as.numeric(as.character(correctiontemp[2])),digits=3)
resultanovapm2b<-c(resultanovapm2b,correctionnumeric)
}
#resultanovapm2b
resultanovapm2ab<-c()
for(i in 1:3)
{
anovatemp<-aov(v17ab[,i]~as.factor(v17$PM))
correctiontemp<-eta_sq(anovatemp)
correctionnumeric<-round(as.numeric(as.character(correctiontemp[2])),digits=3)
resultanovapm2ab<-c(resultanovapm2ab,correctionnumeric)
}
#resultanovapm2ab
resultanovapm3<-c()
for(i in 1:11)
{
anovatemp<-aov(v18t[,i]~as.factor(v18$PM))
correctiontemp<-eta_sq(anovatemp)
correctionnumeric<-round(as.numeric(as.character(correctiontemp[2])),digits=3)
resultanovapm3<-c(resultanovapm3,correctionnumeric)
}
#resultanovapm3
resultanovapm4<-c()
for(i in 1:51)
{
anovatemp<-aov(v19t[,i]~as.factor(v19$PM))
correctiontemp<-eta_sq(anovatemp)
correctionnumeric<-round(as.numeric(as.character(correctiontemp[2])),digits=3)
resultanovapm4<-c(resultanovapm4,correctionnumeric)
}
#resultanovapm4
resultanovapm5<-c()
for(i in 1:5)
{
anovatemp<-aov(v20t[,i]~as.factor(v20$PM))
correctiontemp<-eta_sq(anovatemp)
correctionnumeric<-round(as.numeric(as.character(correctiontemp[2])),digits=3)
resultanovapm5<-c(resultanovapm5,correctionnumeric)
}
#resultanovapm5
#Labelling the vectors containing the effect sizes and the F values
fvaluesfordf1<-c("F*",fvalues1)
anovaresultsfordf1<-c("n^2",resultanovapm1)
fvaluesfordf2<-c("F*",fvalues2)
anovaresultsfordf2<-c("n^2",resultanovapm2)
fvaluesfordf2a<-c("F*",fvalues2a)
anovaresultsfordf2a<-c("n^2",resultanovapm2a)
fvaluesfordf2b<-c("F*",fvalues2b)
anovaresultsfordf2b<-c("n^2",resultanovapm2b)
fvaluesfordf2ab<-c("F*",fvalues2ab)
anovaresultsfordf2ab<-c("n^2",resultanovapm2ab)
fvaluesfordf3<-c("F*",fvalues3)
anovaresultsfordf3<-c("n^2",resultanovapm3)
fvaluesfordf4<-c("F*",fvalues4)
anovaresultsfordf4<-c("n^2",resultanovapm4)
fvaluesfordf5<-c("F*",fvalues5)
anovaresultsfordf5<-c("n^2",resultanovapm5)
#Removing/adding unnecessary columns
v16u<-v16[,-2:-3]
v17u<-v17[,-2:-3]
v17ua<-cbind(as.character(v17$PM),v17a)
v17ub<-cbind(as.character(v17$PM),v17b)
v17uab<-cbind(as.character(v17$PM),v17ab)
v18u<-v18[,-2:-3]
v19u<-v19[,-2:-3]
v20u<-v20[,-2:-3]
#Grouping the results by Prime Minister
v16v<-aggregate(v16u, by=list(v16u$PM),FUN=mean,na.action = na.omit)
v16v$PM <- NULL
colnames(v16v)[1]<-"PM"
v17v<-aggregate(v17u, by=list(v17u$PM),FUN=mean,na.action = na.omit)
v17v$PM <- NULL
colnames(v17v)[1]<-"PM"
v17va<-aggregate(v17a, by=list(v17u$PM),FUN=mean,na.action = na.omit)
colnames(v17va)[1]<-"PM"
colnames(v17va)[2]<-"proneA"
colnames(v17va)[3]<-"averseA"
colnames(v17va)[4]<-"neutralA"
v17vb<-aggregate(v17b, by=list(v17u$PM),FUN=mean,na.action = na.omit)
colnames(v17vb)[1]<-"PM"
colnames(v17vb)[2]<-"proneB"
colnames(v17vb)[3]<-"averseB"
colnames(v17vb)[4]<-"neutralB"
v17vab<-aggregate(v17ab, by=list(v17u$PM),FUN=mean,na.action = na.omit)
colnames(v17vab)[1]<-"PM"
colnames(v17vab)[2]<-"proneAB"
colnames(v17vab)[3]<-"averseAB"
colnames(v17vab)[4]<-"neutralAB"
v18v<-aggregate(v18u, by=list(v18u$PM),FUN=mean,na.action = na.omit)
v18v$PM <- NULL
colnames(v18v)[1]<-"PM"
v19v<-aggregate(v19u, by=list(v19u$PM),FUN=mean,na.action = na.omit)
v19v$PM <- NULL
colnames(v19v)[1]<-"PM"
v20v<-aggregate(v20u, by=list(v20u$PM),FUN=mean,na.action = na.omit)
v20v$PM <- NULL
colnames(v20v)[1]<-"PM"
#Rounding the results
v16V <- v16v
for(i in 1:nrow(v16V)){
for(j in 2:ncol(v16V)){
v16V[i,j]<-round(v16V[i,j],digits=3)
}
}
v17V <- v17v
for(i in 1:nrow(v17V)){
for(j in 2:ncol(v17V)){
v17V[i,j]<-round(v17V[i,j],digits=3)
}
}
v17VA <- v17va
for(i in 1:nrow(v17VA)){
for(j in 2:ncol(v17VA)){
v17VA[i,j]<-round(as.numeric(v17VA[i,j]),digits=3)
}
}
v17VB <- v17vb
for(i in 1:nrow(v17VB)){
for(j in 2:ncol(v17VB)){
v17VB[i,j]<-round(as.numeric(v17VB[i,j]),digits=3)
}
}
v17VAB <- v17vab
for(i in 1:nrow(v17VAB)){
for(j in 2:ncol(v17VAB)){
v17VAB[i,j]<-round(as.numeric(v17VAB[i,j]),digits=3)
}
}
v18V <- v18v
for(i in 1:nrow(v18V)){
for(j in 2:ncol(v18V)){
v18V[i,j]<-round(v18V[i,j],digits=3)
}
}
v19V <- v19v
for(i in 1:nrow(v19V)){
for(j in 2:ncol(v19V)){
v19V[i,j]<-round(v19V[i,j],digits=3)
}
}
v20V <- v20v
for(i in 1:nrow(v20V)){
for(j in 2:ncol(v20V)){
v20V[i,j]<-round(v20V[i,j],digits=3)
}
}
#Putting the Anova and effect sizes at the end of the tables of proportions for each dictionary level (Table 2, base for Figure 3)
x<-rbind(as.matrix(v16V),fvaluesfordf1,anovaresultsfordf1)
pretable1 <- x[,-1]
rownames(pretable1) <- x[,1]
x<-rbind(as.matrix(v17V),fvaluesfordf2,anovaresultsfordf2)
pretable2 <- x[,-1]
rownames(pretable2) <- x[,1]
x<-rbind(as.matrix(v17VA),fvaluesfordf2a,anovaresultsfordf2a)
pretable2a <- x[,-1]
rownames(pretable2a) <- x[,1]
x<-rbind(as.matrix(v17VB),fvaluesfordf2b,anovaresultsfordf2b)
pretable2b <- x[,-1]
rownames(pretable2b) <- x[,1]
x<-rbind(as.matrix(v17VAB),fvaluesfordf2ab,anovaresultsfordf2ab)
pretable2ab <- x[,-1]
rownames(pretable2ab) <- x[,1]
x<-rbind(as.matrix(v18V),fvaluesfordf3,anovaresultsfordf3)
pretable3 <- x[,-1]
rownames(pretable3) <- x[,1]
x<-rbind(as.matrix(v19V),fvaluesfordf4,anovaresultsfordf4)
pretable4 <- x[,-1]
rownames(pretable4) <- x[,1]
x<-rbind(as.matrix(v20V),fvaluesfordf5,anovaresultsfordf5)
pretable5 <- x[,-1]
rownames(pretable5) <- x[,1]Computing the ratios of prone/averse features of linguistic measures per PM for the various levels of the dictionary
#The ratio populist prone/populist averse (dictionary level 2) is generated for the two model specifications (Hypothesis 1 and 2)
popproneC <- cbind(v17$popproneC)
popaverseC <- cbind(v17$popaverseC)
ratioC <- as.matrix(popproneC/popaverseC)
popproneE <- cbind(v17$popproneE)
popaverseE <- cbind(v17$popaverseE)
ratioE <- as.matrix(popproneE/popaverseE)
v21<-v17[,-4:-8]
ratiosCE <- data.frame(v21,ratioC,ratioE)
#The ratios prone/averse for other dictionary levels (H1: Deintermediation+Intimacy+Simplicity, H2: Acrimonious emotions+Authority) are generated
deintermediationprone <- cbind(v18$deintermediationprone)
deintermediationaverse <- cbind(v18$deintermediationaverse)
H1.deintermediation<- as.matrix(deintermediationprone/deintermediationaverse)
intimacyprone <- cbind(v18$intimacyprone)
intimacyaverse <- cbind(v18$intimacyaverse)
H1.intimacy<- as.matrix(intimacyprone/intimacyaverse)
simplicityprone <- cbind(v18$simplicityprone)
simplicityaverse <- cbind(v18$simplicityaverse)
H1.simplicity<- as.matrix(simplicityprone/simplicityaverse)
emoacrimonyprone <- cbind(v18$emoacrimonyprone)
emoacrimonyaverse <- cbind(v18$emoacrimonyaverse)
H2.neg.emotions<- as.matrix(emoacrimonyprone/emoacrimonyaverse)
authorityprone <- cbind(v18$authorityprone)
authorityaverse <- cbind(v18$authorityaverse)
H2.authority<- as.matrix(authorityprone/authorityaverse)
v22<-v18[,-4:-14]
varsCE <- data.frame(v22,H1.deintermediation,H1.intimacy,H1.simplicity,H2.neg.emotions,H2.authority)
#Plotting results for the populist prone/populist averse ratio (dictionary level 2) for H1 and H2 (base for Figure 4)
plot(v21$PM, ratioC, main="H1: Ratio of prone/averse populist features across PMs (a/b)", sub="H1 variables: Deintermediation+Intimacy+Simplicity", xlab="Prime Ministers", ylab="Ratio")
plot(v21$PM, ratioE, main="H2: Ratio of prone/averse populist features across PMs (d/e)", sub="H2 variables: Acrimonious emotions+Authority", xlab="Prime Ministers", ylab="Ratio", ylim = c(0, 9))
#Plotting results for the prone/averse ratios (H1, H2) for other dictionary levels
plot(v22$PM, H1.deintermediation, main="Ratio of prone/averse deintermediation features across PMs", sub="H1", xlab="Prime Ministers", ylab="Ratio", ylim = c(0, 5))
plot(v22$PM, H1.intimacy, main="Ratio of prone/averse intimacy features across PMs", sub="H1", xlab="Prime Ministers", ylab="Ratio", ylim = c(0, 32))
plot(v22$PM, H1.simplicity, main="Ratio of prone/averse simplicity features across PMs", sub="H1", xlab="Prime Ministers", ylab="Ratio", ylim = c(0, 2))
plot(v22$PM, H2.neg.emotions, main="Ratio of prone/averse emotionally acrimonious features across PMs", sub="H2", xlab="Prime Ministers", ylab="Ratio", ylim = c(0, 11))
plot(v22$PM, H2.authority, main="Ratio of prone/averse authority features across PMs", sub="H2", xlab="Prime Ministers", ylab="Ratio", ylim = c(0, 40))
#Grouping the results by Prime Minister
ratiosCEv<-aggregate(ratiosCE, by=list(ratiosCE$PM),FUN=mean,na.action = na.omit)
ratiosCEv$PM <- NULL
ratiosCEv$Speech <- NULL
ratiosCEv$Year <- NULL
colnames(ratiosCEv)[1]<-"PM"
#Computing Anovas for the populist prone/populist averse ratios (dictionary level 2) for H1 and H2
ratiosCEt<-ratiosCE[,-1:-3]
ratiosCEt<-as.matrix(ratiosCEt)
PMs<-as.factor(ratiosCE$PM)
resultnewanova6 <- aov(ratiosCEt~PMs)
#summary(resultnewanova6)
#coefficients(resultnewanova6)
#Extracting the F values from the Anovas for the various dictionary levels
fvalues6<-c()
for (f in 1:2){
fvalues6 <- c(fvalues6,summary.aov(resultnewanova6)[[f]][["F value"]][[1]])
}
fvalues6<-round(fvalues6,digits=3)
#fvalues6
#Extracting the effect sizes estimates over every column of the various dictionary levels
resultanovapm6<-c()
for(i in 1:2)
{
anovatemp<-aov(ratiosCEt[,i]~as.factor(ratiosCE$PM))
correctiontemp<-eta_sq(anovatemp)
correctionnumeric<-round(as.numeric(as.character(correctiontemp[2])),digits=3)
resultanovapm6<-c(resultanovapm6,correctionnumeric)
}
#resultanovapm6
#Labelling the vectors containing the effect sizes and the F values
fvaluesfordf6<-c("F*",fvalues6)
anovaresultsfordf6<-c("n^2",resultanovapm6)
#Rounding the results
ratiosCEV <- ratiosCEv
for(i in 1:nrow(ratiosCEV)){
for(j in 2:ncol(ratiosCEV)){
ratiosCEV[i,j]<-round(ratiosCEV[i,j],digits=3)
}
}
#Putting the Anova results and effect sizes at the end of the table of prone/populist averse ratios (dictionary level 2) for H1 and H2
x<-rbind(as.matrix(ratiosCEV),fvaluesfordf6,anovaresultsfordf6)
pretable6 <- x[,-1]
rownames(pretable6) <- x[,1]Computing the ratios of prone/averse features of linguistic measures time-wise for the various levels of the dictionary
#A generalized linear model is computed on the populist categories (dictionary level 4) for H1 and H2
lm.h1a <- glm(v19$Year ~ v19$longwords + v19$cognitivepro + v19$conceptualnotion + v19$principles + v19$institutionalpro + v19$politicalparties + v19$we + v19$assent + v19$insight + v19$thirdsp + v19$family + v19$religion + v19$gendered + v19$leisure + v19$body + v19$health + v19$friends + v19$home + v19$pronouns + v19$phyfeel + v19$foodfarm + v19$money + v19$festival + v19$elite + v19$nonelite + v19$shortsentences + v19$numbers + v19$pastfocus + v19$culturalnationalism + v19$time + v19$iyou + v19$electoralpro + v19$personalgov + v19$rhetoricalq + v19$futurefocus + v19$persreward + v19$persreward + v19$motion)
#summary(lm.h1a)
lm.h2a <- glm(v19$Year ~ v19$positiveemo + v19$tentative + v19$negativeemo + v19$anger + v19$anxiety + v19$sadness + v19$swearwords + v19$risk + v19$communities + v19$conflict + v19$certainty + v19$achieve + v19$power)
#summary(lm.h2a)
#A generalized linear model is computed on the variables (dicitonary level 3)
lm.h1b <- glm(v18$Year ~ v18$simplicityaverse + v18$deintermediationaverse + v18$intimacyaverse + v18$intimacyprone + v18$simplicityprone + v18$deintermediationprone)
#summary(lm.h1b)
lm.h2b <- glm(v18$Year ~ v18$emoacrimonyaverse + v18$authorityaverse + v18$emoacrimonyprone + v18$authorityprone)
#summary(lm.h2b)
#A linear model is computed on the populist ratios (dictionary level 2)
lm.h1c <- lm(ratiosCE$Year ~ ratiosCE$ratioC)
#summary(lm.h1c)
lm.h2c <- lm(ratiosCE$Year ~ ratiosCE$ratioE)
#summary(lm.h2c)
y<-ratiosCE$ratioC
#plot(y~as.numeric(ratiosCE$Year))
z<-ratiosCE$ratioE
#plot(e~as.numeric(ratiosCE$Year))
#Compute the mean of scores for each year
avplotyear<-c() #empty vector
sdplotyear<-c()
for(i in unique(ratiosCE$Year)){ #takes all the years mentioned once
allratiosperyear<-ratiosCE[ratiosCE[,3]==i,4] #finds out all the rows in column 4 with year == i
sdperyear<-sd(allratiosperyear)
averageplot<-sum(allratiosperyear)/length(allratiosperyear) #computes the average for each year
avplotyear<-rbind(avplotyear,c(i,averageplot))#binds year and averages
sdplotyear<-rbind(sdplotyear,c(i,sdperyear))#binds year and standard deviations
}
avplotyear2<-c() #empty vector
sdplotyear2<-c()
for(i in unique(ratiosCE$Year)){ #takes all the years mentioned once
allratiosperyear2<-ratiosCE[ratiosCE[,3]==i,5] #finds out all the rows in column 4 with year == i
sdperyear2<-sd(allratiosperyear2)
averageplot2<-sum(allratiosperyear2)/length(allratiosperyear2) #computes the average for each year
avplotyear2<-rbind(avplotyear2,c(i,averageplot2))#binds year and averages
sdplotyear2<-rbind(sdplotyear2,c(i,sdperyear2))#binds year and standard deviations
}Figures
#RUN FIGURES SEPARATELY
#Preparing Figure 1
colourchart<-c("cornflowerblue","darkblue","gold1","red1","cadetblue1","green4","green2","blue2","darkorange4","cyan3","darkorange2")
dfmperpm <- dfm_group(dfmtokssubworkcorpus, groups = "loc")
npm<-ntoken(dfmperpm)
npm<-as.data.frame(npm)
PmOrder<-c("nehru","indira","desai","charan","rajiv","vpsingh","chandra","rao","vajpayee","mms","modi")
npm<-setDT(npm, keep.rownames = TRUE)[]
rn<-as.matrix(npm$rn)
npmO<-npm[match(PmOrder, rn),]
namespms<-c("J.Nehru","I.Gandhi","M.Desai","C.Singh","R.Gandhi","VP.Singh","C.Shekhar","PVN.Rao","AB.Vajpayee","M.Singh","N.Modi")
barplot(npmO$npm, main="Distribution of features by Prime Ministers in the DIPMS corpus", sub="(1946-2019)", xlab="Prime Ministers", ylab="Number of features", col=c("cornflowerblue","darkblue","gold1","red1","cadetblue1","green4","green2","blue2","darkorange4","cyan3","darkorange2"),names.arg=namespms, cex.main=1.5)
#Figure 1 (title: Distribution of Features by Year and Prime Ministers in the DIPMS Corpus)
dfmperyear <- dfm_group(dfmtokssubworkcorpus, groups = "year")
npy<-ntoken(dfmperyear)
year_vars <- c(1946:2019)
myColors <- ifelse(year_vars >= 1946 & year_vars <= 1964 , "cornflowerblue" ,
ifelse(year_vars >= 1964 & year_vars <= 1975 , "darkblue",
ifelse(year_vars >= 1979 & year_vars <= 1983 , "darkblue",
ifelse(year_vars >= 1976 & year_vars <= 1977 , "gold1",
ifelse(year_vars == 1978, "red1",
ifelse(year_vars >= 1984 & year_vars <= 1987, "cadetblue1",
ifelse(year_vars == 1988, "green4",
ifelse(year_vars == 1989, "green2",
ifelse(year_vars >= 1990 & year_vars <= 1994, "blue2",
ifelse(year_vars >= 1995 & year_vars <= 2000, "darkorange4",
ifelse(year_vars >= 2001 & year_vars <= 2010, "cyan3",
ifelse(year_vars >= 2011 & year_vars <= 2019, "darkorange2",
"grey90" ))))))))))))
barplot(npy, main="Distribution of features by year and Prime Ministers in the DIPMS corpus", sub="(1964-2019: The years of term transitions are rounded)", xlab="Years", ylab="Number of features", las=2, mgp=c(3.18,1,0), col=myColors, cex.main=1.5) 
#sub="(J.Nehru 46-64, I.Gandhi 66-77/80-84, M.Desai 77-79, C.Singh 79, R.Gandhi 84-89, VP.Singh 89-90, C.Shekhar 90-91, PVN.Rao 91-95, AB.Vajpayee 98-04, M.Singh 04-14, N.Modi 14-19)",
plot(1,1,type="n",axes=FALSE,xlab="",ylab="")#empty plot for legend
legend(x="top",inset=0,
legend=c("J.Nehru (1946-1964)","I.Gandhi (1966-1977)","M.Desai (1977-1979)","C.Singh (1979)","R.Gandhi (1984-1989)","VP.Singh (1989-1990)","C.Shekhar (1990-1991)","PVN.Rao (1991-1995)","AB.Vajpayee (1998-2004)","M.Singh (2004-2014)","N.Modi (2014-2019)"),
col=colourchart,lwd=5,cex=1,horiz=FALSE) #size of the legend
#Figure 2 (title: Reingold-Tilford Tree Network Diagram of the 5-Level Nested Lists of the Dictionary Along with its 15 First Entries)
treepop<-read_yaml(file = "C:/Users/jtmartelli/Google Drive/Textual_analysis/R/aping2/dictionaries/tree.yml", fileEncoding = "UTF-8")
treepopNode <- as.Node(treepop)
#plot(treepopNode)
print(treepopNode, limit=15)## levelName
## 1 Root
## 2 ¦--Populist mimesis(hypothesis1)
## 3 ¦ ¦--Averse(a)
## 4 ¦ ¦ ¦--Deintermediation(a)
## 5 ¦ ¦ ¦ ¦--assent
## 6 ¦ ¦ ¦ ¦ ¦--extension
## 7 ¦ ¦ ¦ ¦ °--seed
## 8 ¦ ¦ ¦ ¦--insight
## 9 ¦ ¦ ¦ ¦ ¦--extension
## 10 ¦ ¦ ¦ ¦ °--seed
## 11 ¦ ¦ ¦ ¦--institpro
## 12 ¦ ¦ ¦ ¦ °--adhoc
## 13 ¦ ¦ ¦ °--parties
## 14 ¦ ¦ ¦ °--adhoc
## 15 ¦ ¦ °--... 2 nodes w/ 11 sub
## 16 ¦ °--... 1 nodes w/ 86 sub
## 17 °--... 1 nodes w/ 131 subuseRtreeList <- ToListExplicit(treepopNode, unname = TRUE)
radialNetwork(useRtreeList, fontSize = 13)#Figure 3 (title: Proportions of the Populist Categories (Level 4 of the Dictionary) Among PMs)
ColumnNames<-names(v19v)
#tabletitle<-ColumnNames[-1] #titles
tabletitle<-c("Assent","Insight","Institutional processes","Political parties", "We", "Cognitive processes", "Conceptual notions", "Long words", "Democratic principles","Electoral processes", "Future focus", "I and you", "Motion", "Personalized governance", "Personalized rewards", "Rhetorical questions", "Body", "Family", "Food and farming", "Friends", "Gendered references", "Health", "Home", "Leisure", "Money", "Physical feel", "Pronouns", "Religion", "Third-person", "Cultural nationalism", "Elites", "Festival", "Non-elites", "Numbers", "Past focus", "Short sentences", "Time", "Tentative", "Positive emotions", "Achieve", "Certainty", "Power", "Anger", "Anxiety","Community references", "Conflict", "Negative emotions", "Risk", "Sadness", "Swear words", "The rest")
#tabletitle[1]<-"Institutional Processes"#Just changes first
#tabletitle<-c("Institutional Processes","Political Parties")#Change all by putting names in the vector as text. NB: it will work only if there is a vector of all 51 entries.
RowOrder<-c(7,4,3,2,8,11,1,9,10,5,6)
#for(i in c(3,5,7,9)) #If it is needed to pull out specific columns
for(i in 2:52)#all columns
{
FreqValues<-as.numeric(as.character(v19v[,i]))
par(mar=c(0.5,2,1,1))
plot(1:length(FreqValues),FreqValues,mgp = c(3, 1, 0),xaxt='n',xlab="",ylab="Relative Frequency",main=tabletitle[i-1],type='n',las=0,ylim=c(0,max(FreqValues)),cex.main=2)
#axis(2,at=c(0,max(FreqValues)),labels=c(0,round(max(FreqValues),digits=2)))
for(k in 1:11)
{
segments(k,0,k,FreqValues[RowOrder[k]],lwd=4,col=colourchart[k])
}
}
plot(1,1,type="n",axes=FALSE,xlab="",ylab="")##empty plot for legend
legend(x="top",inset=0,
legend=c("J.Nehru","I.Gandhi","M.Desai","C.Singh","R.Gandhi","VP.Singh","C.Shekhar","PVN.Rao","AB.Vajpayee","M.Singh","N.Modi"),
col=colourchart,lwd=5,cex=1,horiz=FALSE) #size of the legend
#Figures 4a and 4b (title: Ratio of Prone/Averse Populist Features Across PMs for Model Specifications H1 and H2 [a/b & c/d]
x1 = factor(v21$PM, levels=c("nehru","indira","desai","charan","rajiv","vpsingh","chandra","rao","vajpayee","mms","modi"))
myColors <- ifelse(levels(x1)=="nehru" , "cornflowerblue" ,
ifelse(levels(x1)=="indira", "darkblue",
ifelse(levels(x1)=="desai", "gold1",
ifelse(levels(x1)=="charan", "red1",
ifelse(levels(x1)=="rajiv", "cadetblue1",
ifelse(levels(x1)=="vpsingh", "green4",
ifelse(levels(x1)=="chandra", "green2",
ifelse(levels(x1)=="rao", "blue2",
ifelse(levels(x1)=="vajpayee", "darkorange4",
ifelse(levels(x1)=="mms", "cyan3",
ifelse(levels(x1)=="modi", "darkorange2",
"grey90" )))))))))))
plot(x1, ratioC, main="H1: Ratio of prone/averse populist features across PMs (a/b)", sub="H1 variables: Deintermediation+Intimacy+Simplicity", xlab="Prime Ministers", ylab="Ratio", xaxt = "n",col=myColors, cex.main=1.5)
#if not ordered replace x1 with v21$PM
axis(1, at=1:11, labels=c("J.Nehru","I.Gandhi","M.Desai","C.Singh","R.Gandhi","VP.Singh","C.Shekhar","PVN.Rao","AB.Vajpayee","M.Singh","N.Modi"))
plot(x1, proneA, main="H1: Populist prone features across PMs (a)", sub="H1 variables: Deintermediation+Intimacy+Simplicity",
xlab="Prime Ministers", ylab="% of features", xaxt = "n",col=myColors, cex.main=1.5) #if not ordered replace x1 with v17$PM
axis(1, at=1:11, labels=c("J.Nehru","I.Gandhi","M.Desai","C.Singh","R.Gandhi","VP.Singh","C.Shekhar","PVN.Rao","AB.Vajpayee","M.Singh","N.Modi"))
plot(x1, averseA, main="H1: Populist averse features across PMs (b)", sub="H1 variables: Deintermediation+Intimacy+Simplicity",
xlab="Prime Ministers", ylab="% of features", xaxt = "n",col=myColors, cex.main=1.5) #if not ordered replace x1 with v17$PM
axis(1, at=1:11, labels=c("J.Nehru","I.Gandhi","M.Desai","C.Singh","R.Gandhi","VP.Singh","C.Shekhar","PVN.Rao","AB.Vajpayee","M.Singh","N.Modi"))
plot(x1, neutralA, main="H1: Populist neutral features across PMs (c)", sub="H1 variables: Deintermediation+Intimacy+Simplicity",
xlab="Prime Ministers", ylab="% of features", xaxt = "n",col=myColors, cex.main=1.5) #if not ordered replace x1 with v17$PM
axis(1, at=1:11, labels=c("J.Nehru","I.Gandhi","M.Desai","C.Singh","R.Gandhi","VP.Singh","C.Shekhar","PVN.Rao","AB.Vajpayee","M.Singh","N.Modi"))
plot(x1, ratioE, main="H2: Ratio of prone/averse populist features across PMs (d/e)", sub="H2 variables: Acrimonious emotions+Authority", xlab="Prime Ministers", ylab="Ratio", ylim = c(0, 9), xaxt = "n",col=myColors, cex.main=1.5) #if not ordered replace x1 with v21$PM
axis(1, at=1:11, labels=c("J.Nehru","I.Gandhi","M.Desai","C.Singh","R.Gandhi","VP.Singh","C.Shekhar","PVN.Rao","AB.Vajpayee","M.Singh","N.Modi"))
plot(x1, proneB, main="H2: Populist prone features across PMs (d)", sub="H2 variables: Acrimonious emotions+Authority",
xlab="Prime Ministers", ylab="% of features", xaxt = "n",col=myColors, cex.main=1.5) #if not ordered replace x1 with v17$PM
axis(1, at=1:11, labels=c("J.Nehru","I.Gandhi","M.Desai","C.Singh","R.Gandhi","VP.Singh","C.Shekhar","PVN.Rao","AB.Vajpayee","M.Singh","N.Modi"))
plot(x1, averseB, main="H2: Populist averse features across PMs (e)", sub="H2 variables: Acrimonious emotions+Authority",
xlab="Prime Ministers", ylab="% of features", xaxt = "n",col=myColors, cex.main=1.5) #if not ordered replace x1 with v17$PM
axis(1, at=1:11, labels=c("J.Nehru","I.Gandhi","M.Desai","C.Singh","R.Gandhi","VP.Singh","C.Shekhar","PVN.Rao","AB.Vajpayee","M.Singh","N.Modi"))
plot(x1, neutralB, main="H2: Populist neutral features across PMs (f)", sub="H2 variables: Acrimonious emotions+Authority",
xlab="Prime Ministers", ylab="% of features", xaxt = "n",col=myColors, cex.main=1.5) #if not ordered replace x1 with v17$PM
axis(1, at=1:11, labels=c("J.Nehru","I.Gandhi","M.Desai","C.Singh","R.Gandhi","VP.Singh","C.Shekhar","PVN.Rao","AB.Vajpayee","M.Singh","N.Modi"))
#Figure 5 (title: Year-wise Populist Ratios Among PMs for Model Specifications H1 and H2)
myColors <- ifelse(year_vars >= 1946 & year_vars <= 1964 , "cornflowerblue" ,
ifelse(year_vars >= 1964 & year_vars <= 1975 , "darkblue",
ifelse(year_vars >= 1979 & year_vars <= 1983 , "darkblue",
ifelse(year_vars >= 1976 & year_vars <= 1977 , "gold1",
ifelse(year_vars == 1978, "red1",
ifelse(year_vars >= 1984 & year_vars <= 1987, "cadetblue1",
ifelse(year_vars == 1988, "green4",
ifelse(year_vars == 1989, "green2",
ifelse(year_vars >= 1990 & year_vars <= 1994, "blue2",
ifelse(year_vars >= 1995 & year_vars <= 2000, "darkorange4",
ifelse(year_vars >= 2001 & year_vars <= 2010, "cyan3",
ifelse(year_vars >= 2011 & year_vars <= 2019, "darkorange2",
"grey90" ))))))))))))
plot(y~as.numeric(ratiosCE$Year),data=ratiosCE, ylim=c(0.5,3.8),col="lightgrey", main="H1: Ratio of prone/averse populist features over time", sub="H1 variables: Deintermediation+Intimacy+Simplicity", xlab="Years", ylab="Ratio", cex.main=1.5)
points(avplotyear, pch=19, col=myColors)
lines(avplotyear, col="black",lty=3)
lines(sdplotyear[,1],avplotyear[,2]+sdplotyear[,2],lty=2, col="black")
lines(sdplotyear[,1],avplotyear[,2]-sdplotyear[,2],lty=2, col="black")
plot(z~as.numeric(ratiosCE$Year),data=ratiosCE, ylim=c(0.5,8.8),col="lightgrey", main="H2: Ratio of prone/averse populist features over time", sub="H2 variables: Acrimonious emotions+Authority", xlab="Years", ylab="Ratio", cex.main=1.5)
points(avplotyear2, pch=19, col=myColors)
lines(avplotyear2, col="black",lty=3)
lines(sdplotyear2[,1],avplotyear2[,2]+sdplotyear2[,2],lty=2, col="black")
lines(sdplotyear2[,1],avplotyear2[,2]-sdplotyear2[,2],lty=2, col="black")
Table
#Preparing Table 1
tabletitle<-c("Assent","Insight","Institutional processes","Political parties", "We", "Cognitive processes", "Conceptual notions", "Long words", "Democratic principles","Electoral processes", "Future focus", "I and you", "Motion", "Personalized governance", "Personalized rewards", "Rhetorical questions", "Body", "Family", "Food and farming", "Friends", "Gendered references", "Health", "Home", "Leisure", "Money", "Physical feel", "Pronouns", "Religion", "Third-person", "Cultural nationalism", "Elites", "Festival", "Non-elites", "Numbers", "Past focus", "Short sentences", "Time", "Tentative", "Positive emotions", "Achieve", "Certainty", "Power", "Anger", "Anxiety","Community references", "Conflict", "Negative emotions", "Risk", "Sadness", "Swear words", "The rest")
#Table1a (title: Proportions, variance and effect sizes of dictionary categories among PMs)
pretable4f<-pretable4
colnames(pretable4f)[1]<-"Assent"
colnames(pretable4f)[2]<-"Insight"
colnames(pretable4f)[3]<-"Institutional processes"
colnames(pretable4f)[4]<-"Political parties"
colnames(pretable4f)[5]<-"We"
colnames(pretable4f)[6]<-"Cognitive processes"
colnames(pretable4f)[7]<-"Conceptual notions"
colnames(pretable4f)[8]<-"Long words"
colnames(pretable4f)[9]<-"Democratic principles"
colnames(pretable4f)[10]<-"Electoral processes"
colnames(pretable4f)[11]<-"Future focus"
colnames(pretable4f)[12]<-"I and you"
colnames(pretable4f)[13]<-"Motion"
colnames(pretable4f)[14]<-"Personalized governance"
colnames(pretable4f)[15]<-"Personalized rewards"
colnames(pretable4f)[16]<-"Rhetorical questions"
colnames(pretable4f)[17]<-"Body"
colnames(pretable4f)[18]<-"Family"
colnames(pretable4f)[19]<-"Food and farming"
colnames(pretable4f)[20]<-"Friends"
colnames(pretable4f)[21]<-"Gendered references"
colnames(pretable4f)[22]<-"Health"
colnames(pretable4f)[23]<-"Home"
colnames(pretable4f)[25]<-"Leisure"
colnames(pretable4f)[24]<-"Money"
colnames(pretable4f)[26]<-"Physical feel"
colnames(pretable4f)[27]<-"Pronouns"
colnames(pretable4f)[28]<-"Religion"
colnames(pretable4f)[29]<-"Third-person"
colnames(pretable4f)[30]<-"Cultural nationalism"
colnames(pretable4f)[31]<-"Elites"
colnames(pretable4f)[32]<-"Festival"
colnames(pretable4f)[33]<-"Non-elites"
colnames(pretable4f)[34]<-"Numbers"
colnames(pretable4f)[35]<-"Past focus"
colnames(pretable4f)[36]<-"Short sentences"
colnames(pretable4f)[37]<-"Time"
colnames(pretable4f)[38]<-"Tentative"
colnames(pretable4f)[39]<-"Positive emotions"
colnames(pretable4f)[40]<-"Achieve"
colnames(pretable4f)[41]<-"Certainty"
colnames(pretable4f)[42]<-"Power"
colnames(pretable4f)[43]<-"Anger"
colnames(pretable4f)[44]<-"Anxiety"
colnames(pretable4f)[45]<-"Community references"
colnames(pretable4f)[46]<-"Conflict"
colnames(pretable4f)[47]<-"Negative emotions"
colnames(pretable4f)[48]<-"Risk"
colnames(pretable4f)[49]<-"Sadness"
colnames(pretable4f)[50]<-"Swear words"
colnames(pretable4f)[51]<-"The rest"
rownames(pretable4f)[1]<-"C.Shekhar"
rownames(pretable4f)[2]<-"C.Singh"
rownames(pretable4f)[3]<-"M.Desai"
rownames(pretable4f)[4]<-"I.Gandhi"
rownames(pretable4f)[5]<-"M.Singh"
rownames(pretable4f)[6]<-"N.Modi"
rownames(pretable4f)[7]<-"J.Nehru"
rownames(pretable4f)[8]<-"R.Gandhi"
rownames(pretable4f)[9]<-"PVN.Rao"
rownames(pretable4f)[10]<-"AB.Vajpayee"
rownames(pretable4f)[11]<-"VP.Singh"
pretable4df<-as.data.frame(pretable4f)
PmOrderF<-c("J.Nehru","I.Gandhi","M.Desai","C.Singh","R.Gandhi","VP.Singh","C.Shekhar","PVN.Rao","AB.Vajpayee","M.Singh","N.Modi","F*","n^2")
w<-setDT(pretable4df, keep.rownames = TRUE)[]
rn<-as.matrix(w$rn)
pretable4df<-w[match(PmOrderF, rn),]
colnames(pretable4df)[1]<-"Prime Ministers"
pretable4dfo<-cbind(pretable4df$Assent,pretable4df$Insight)
Table2a<-pretable4df[,c(1,37,35,32,34,36,38,31,33,9,7,8,10,13,15,16,12,11,17,14,5,4,2,3,21,19,24,25,29,23,26,18,27,20,22,30,28,6,48,44,45,50,51,47,46,49,40,41,42,43,39,52)]
kable(Table2a,"latex",booktabs = T, align="c") %>%
kable_styling(latex_options = c("basic","scale_down")) %>%
row_spec(0, align= "c", hline_after= T, bold=T) %>%
add_header_above(c(" " = 1, "Prone" = 8, "Averse" = 4, "Prone" = 7, "Averse" = 4, "Prone" = 13, "Averse" = 1, "Prone" = 8, "Averse" = 1, "Prone" = 3, "Averse" = 1, " " = 1),italic=T) %>%
add_header_above(c(" ", "Simplicity" = 12, "Deintermediation" = 11, "Intimacy" = 14, "Acrimony" = 9, "Authority" = 4, " " = 1),italic=T) %>%
add_header_above(c(" ", "Hypothesis 1" = 37, "Hypothesis 2" = 13, " " = 1),italic=T) %>%
row_spec(12:13, bold = F, italic= T, color = "black") %>%
add_footnote(c("Results indicate the proportions of dictionary categories for each Prime Minister. The sum of all the categories and the rest of features equals 1 for each Prime Minister.",
"Linguistic measures are grouped according to their direct or inverse relashionship to populist styling (prone/averse), according to the variables of interest (Simplicity/Deintermediation/Intimacy/Acrimony/Authority) and accroding to the hypotheses (H1,H2) they are part of.",
"For 49 out of 50 categories, proportions are significantly different from one another at p$<$.001 using Bonferroni post hoc comparison tests.",
"N^2 are conservative estimates of effect sizes for the overall differences among Prime Ministers for each category."), notation = "number") %>%
landscape()#Table1b (title: Proportions, variance and effect sizes of Hypotheses 1 & 2 among PMs)
pretable1f<-pretable1
colnames(pretable1f)[1]<-"Hypothesis 1"
colnames(pretable1f)[2]<-"Hypothesis 2"
colnames(pretable1f)[3]<-"The rest"
rownames(pretable1f)[1]<-"C.Shekhar"
rownames(pretable1f)[2]<-"C.Singh"
rownames(pretable1f)[3]<-"M.Desai"
rownames(pretable1f)[4]<-"I.Gandhi"
rownames(pretable1f)[5]<-"M.Singh"
rownames(pretable1f)[6]<-"N.Modi"
rownames(pretable1f)[7]<-"J.Nehru"
rownames(pretable1f)[8]<-"R.Gandhi"
rownames(pretable1f)[9]<-"PVN.Rao"
rownames(pretable1f)[10]<-"AB.Vajpayee"
rownames(pretable1f)[11]<-"VP.Singh"
pretable1df<-as.data.frame(pretable1f)
PmOrderF<-c("J.Nehru","I.Gandhi","M.Desai","C.Singh","R.Gandhi","VP.Singh","C.Shekhar","PVN.Rao","AB.Vajpayee","M.Singh","N.Modi","F*","n^2")
w<-setDT(pretable1df, keep.rownames = TRUE)[]
rn<-as.matrix(w$rn)
pretable1df<-w[match(PmOrderF, rn),]
colnames(pretable1df)[1]<-"Prime Ministers"
Table2b<-pretable1df[,c(1,2,3,4)]
kable(Table2b,"latex",booktabs = T, align="c") %>%
kable_styling(latex_options = c("basic")) %>%
row_spec(0, align= "c", hline_after= T, bold=T) %>%
row_spec(12:13, bold = F, italic= T, color = "black")#Table1c (title: Proportions, variance and effect sizes of prone and averse linguistic measures among PMs)
pretable2f<-pretable2
rownames(pretable2f)[1]<-"C.Shekhar"
rownames(pretable2f)[2]<-"C.Singh"
rownames(pretable2f)[3]<-"M.Desai"
rownames(pretable2f)[4]<-"I.Gandhi"
rownames(pretable2f)[5]<-"M.Singh"
rownames(pretable2f)[6]<-"N.Modi"
rownames(pretable2f)[7]<-"J.Nehru"
rownames(pretable2f)[8]<-"R.Gandhi"
rownames(pretable2f)[9]<-"PVN.Rao"
rownames(pretable2f)[10]<-"AB.Vajpayee"
rownames(pretable2f)[11]<-"VP.Singh"
pretable2df<-as.data.frame(pretable2f)
PmOrderF<-c("J.Nehru","I.Gandhi","M.Desai","C.Singh","R.Gandhi","VP.Singh","C.Shekhar","PVN.Rao","AB.Vajpayee","M.Singh","N.Modi","F*","n^2")
w<-setDT(pretable2df, keep.rownames = TRUE)[]
rn<-as.matrix(w$rn)
pretable2df<-w[match(PmOrderF, rn),]
colnames(pretable2df)[1]<-"Prime Ministers"
colnames(pretable2df)[2]<-"Averse"
colnames(pretable2df)[3]<-"Prone"
colnames(pretable2df)[4]<-"Averse"
colnames(pretable2df)[5]<-"Prone"
colnames(pretable2df)[6]<-"The rest"
Table2c<-pretable2df[,c(1,3,2,5,4,6)]
kable(Table2c,"latex",booktabs = T, align="c") %>%
kable_styling(latex_options = c("basic")) %>%
row_spec(0, align= "c", hline_after= T, bold=T) %>%
add_header_above(c(" " = 1, "Hypothesis 1" = 2, "Hypothesis 2" = 2, " " = 1),italic=T) %>%
row_spec(12:13, bold = F, italic= T, color = "black")#Table1d (title: Proportions, variance and effect sizes of dictionary variables among PMs)
pretable3f<-pretable3
rownames(pretable3f)[1]<-"C.Shekhar"
rownames(pretable3f)[2]<-"C.Singh"
rownames(pretable3f)[3]<-"M.Desai"
rownames(pretable3f)[4]<-"I.Gandhi"
rownames(pretable3f)[5]<-"M.Singh"
rownames(pretable3f)[6]<-"N.Modi"
rownames(pretable3f)[7]<-"J.Nehru"
rownames(pretable3f)[8]<-"R.Gandhi"
rownames(pretable3f)[9]<-"PVN.Rao"
rownames(pretable3f)[10]<-"AB.Vajpayee"
rownames(pretable3f)[11]<-"VP.Singh"
pretable3df<-as.data.frame(pretable3f)
PmOrderF<-c("J.Nehru","I.Gandhi","M.Desai","C.Singh","R.Gandhi","VP.Singh","C.Shekhar","PVN.Rao","AB.Vajpayee","M.Singh","N.Modi","F*","n^2")
w<-setDT(pretable3df, keep.rownames = TRUE)[]
rn<-as.matrix(w$rn)
pretable3df<-w[match(PmOrderF, rn),]
colnames(pretable3df)[1]<-"Prime Ministers"
colnames(pretable3df)[2]<-"Deintermediation"
colnames(pretable3df)[3]<-"Intimacy"
colnames(pretable3df)[4]<-"Simplicity"
colnames(pretable3df)[5]<-"Deintermediation"
colnames(pretable3df)[6]<-"Intimacy"
colnames(pretable3df)[7]<-"Simplicity"
colnames(pretable3df)[8]<-"Authority"
colnames(pretable3df)[9]<-"Acrimony"
colnames(pretable3df)[10]<-"Authority"
colnames(pretable3df)[11]<-"Acrimony"
colnames(pretable3df)[12]<-"The rest"
Table2d<-pretable3df[,c(1,5,6,7,2,3,4,10,11,8,9,12)]
kable(Table2d,"latex",booktabs = T, align="c") %>%
kable_styling(latex_options = c("basic","scale_down")) %>%
row_spec(0, align= "c", hline_after= T, bold=T) %>%
add_header_above(c(" " = 1, "Prone" = 3, "Averse" = 3, "Prone" = 2, "Averse" = 2, " " = 1),italic=T) %>%
add_header_above(c(" " = 1, "Hypothesis 1" = 6, "Hypothesis 2" = 4, " " = 1),italic=T) %>%
row_spec(12:13, bold = F, italic= T, color = "black") %>%
landscape()#Table1e (title: Proportions, variance and effect sizes of dictionary types among PMs)
pretable5f<-pretable5
rownames(pretable5f)[1]<-"C.Shekhar"
rownames(pretable5f)[2]<-"C.Singh"
rownames(pretable5f)[3]<-"M.Desai"
rownames(pretable5f)[4]<-"I.Gandhi"
rownames(pretable5f)[5]<-"M.Singh"
rownames(pretable5f)[6]<-"N.Modi"
rownames(pretable5f)[7]<-"J.Nehru"
rownames(pretable5f)[8]<-"R.Gandhi"
rownames(pretable5f)[9]<-"PVN.Rao"
rownames(pretable5f)[10]<-"AB.Vajpayee"
rownames(pretable5f)[11]<-"VP.Singh"
pretable5df<-as.data.frame(pretable5f)
PmOrderF<-c("J.Nehru","I.Gandhi","M.Desai","C.Singh","R.Gandhi","VP.Singh","C.Shekhar","PVN.Rao","AB.Vajpayee","M.Singh","N.Modi","F*","n^2")
w<-setDT(pretable5df, keep.rownames = TRUE)[]
rn<-as.matrix(w$rn)
pretable5df<-w[match(PmOrderF, rn),]
colnames(pretable5df)[1]<-"Prime Ministers"
colnames(pretable5df)[2]<-"Extension"
colnames(pretable5df)[3]<-"Seeds"
colnames(pretable5df)[4]<-"Ad hoc"
colnames(pretable5df)[5]<-"Seeds gen."
colnames(pretable5df)[6]<-"The rest"
Table2e<-pretable5df[,c(1,3,5,2,4,6)]
kable(Table2e,"latex",booktabs = T, align="c") %>%
kable_styling(latex_options = c("basic")) %>%
row_spec(0, align= "c", hline_after= T, bold=T) %>%
row_spec(12:13, bold = F, italic= T, color = "black")Appendices
#Appendix 1 (title: Summary table of the populist categories)
header <- c("Category","Abbreviation","Examples","Populist Correlates","Variable","Rel","H","Seed","Ext")
a1 <- c("Short sentences","shortsentences",".","Plainness, directness","Simplicity","Prone",1,1,0)
a2 <- c("Numbers","numbers","lakh*, crore*","Directness, truth-speaking","Simplicity","Prone",1,36,75)
a3 <- c("Elites","elites","castei*, ruling_class","Agonistic, manichaean","Simplicity","Prone",1,0,145)
a4 <- c("Non-elites","nonelite","ordinary, common_man","Agonistic, manichaean","Simplicity","Prone",1,0,78)
a5 <- c("Past focus","pastfocus","caught, brought","Negative, critique, informal","Simplicity","Prone",1,342,0)
a6 <- c("Time","time","ago, current","Storytelling, closeness","Simplicity","Prone",1,0,305)
a7 <- c("Cultural nationalism","culturalnationalism","Ganga, Ram, temple", "Relatedness, closeness","Simplicity","Prone",1,0,109)
a8 <- c("Festival","festival","Diwali, Lohri","Familiar, popular","Simplicity","Prone",1,0,100)
a9 <- c("Long words","longwords","synonymous, reciprocate","Complexity, precision","Simplicity","Averse",1,10680,0)
a10 <- c("Cognitive processes","cognitivepro","analy*, question","Analytical, complexity","Simplicity","Averse",1,800,116)
a11 <- c("Conceptual notions","conceptualnotion","*ism, *logy, *tion","Notional, complexity","Simplicity","Averse",1,0,415)
a12 <- c("Democratic principles","principles","inclusive*, secularism","Abstraction, ideational","Simplicity","Averse",1,0,60)
a13 <- c("I and you","iyou","I, my, you, your","Social, informal, honest","Deintermediation","Prone",1,67,0)
a14 <- c("Personalized governance","personalgov","scheme, provision","Individualized decision-making","Deintermediation","Prone",1,0,50)
a15 <- c("Personalized rewards","persreward","dare, bold","Boldness, success","Deintermediation","Prone",1,120,5)
a16 <- c("Future focus","futurefocus","till, soon, will","Self-driven, goal-oriented","Deintermediation","Prone",1,98,0)
a17 <- c("Electoral processes","electoralpro","majority, vote*, defeat","Permanent campaigning","Deintermediation","Prone",1,0,33)
a18 <- c("Rhetorical questions","rhetoricalq","?","Direct appeal, hailing, true/false","Deintermediation","Prone",1,0,1)
a19 <- c("Motion","motion","arrive, travel, go","Narrative, action-based","Deintermediation","Prone",1,325,29)
a20 <- c("Political parties","politicalparties","Bharatiya_Janata_Party","Political brokerage","Deintermediation","Averse",1,0,241)
a21 <- c("Institutional procesess","institutionalpro","adopt*, draft, Committee","Institutional brokerage","Deintermediation","Averse",1,0,217)
a22 <- c("Assent","assent","alright, yes","Agreement, compliance, mediation","Deintermediation","Averse",1,34,6)
a23 <- c("Insight","insight","realize, question, infer","Thought process, dialogue","Deintermediation","Averse",1,259,13)
a24 <- c("Friends","friends","fellow, comrad*","Closeness, casual, warmness","Intimacy","Prone",1,95,5)
a25 <- c("Family","family","mother, cousin, auntie","Closeness, kinship","Intimacy","Prone",1,129,82)
a26 <- c("Home","home","bed, kitchen, cylinder","Closeness, everyday, simplicity","Intimacy","Prone",1,98,38)
a27 <- c("Leisure","leisure","sport*, kite, film*","Closeness, everyday, commonness","Intimacy","Prone",1,64,43)
a28 <- c("Religion","religion","god, faith, guru","Closeness, belonging, commonness","Intimacy","Prone",1,168,246)
a29 <- c("Health","health","ill, diet, defecat*","Closeness, commonness, personal","Intimacy","Prone",1,286,51)
a30 <- c("Money","money","shop, pay*, job","Commonness, personal, concrete","Intimacy","Prone",1,215,79)
a31 <- c("Body","body","skin, tongue, sweat","Personal, sensory","Intimacy","Prone",1,215,5)
a32 <- c("Physical feel","phyfeel","cold, dry, warm","Personal, sensory","Intimacy","Prone",1,128,6)
a33 <- c("Food and farming","foodfarm","milk, rice, honey","Commonness, belonging","Intimacy","Prone",1,183,81)
a34 <- c("Gendered references","gendered","madam, boy, papa","Socially connected, commonness","Intimacy","Prone",1,237,21)
a35 <- c("Third-person","thirdsp","he, her, they","Socially connected, out-group","Intimacy","Prone",1,35,0)
a36 <- c("Pronouns","pronouns","somebod*, others, mine","Informal, personal","Intimacy","Prone",1,190,0)
a37 <- c("We","we","we, us, our","Detached, high status","Intimacy","Averse",1,16,0)
a38 <- c("Negative emotions","negativeemo","abuse*, dread*, furious*","Agonistic, affective","Acrimony","Prone",2,734,252)
a39 <- c("Anger","anger","assault*, lies, rape*","Agonistic, affective","Acrimony","Prone",2,230,121)
a40 <- c("Anxiety","anxiety","fear, panic*, threat*","Agonistic, affective","Acrimony","Prone",2,121,146)
a41 <- c("Sadness","sadness","fail*, gloom*, tragic","Agonistic, affective","Acrimony","Prone",2,136,16)
a42 <- c("Swear words","swearwords","idiot*, creep, goon*","Agonistic, affective","Acrimony","Prone",2,128,9)
a43 <- c("Conflict","conflict","against, exclude, blame","Agonistic, affective","Acrimony","Prone",2,36,129)
a44 <- c("Community references","community","adivasi*, caste, Muslim*","Agonistic, affective","Acrimony","Prone",2,152,0)
a45 <- c("Risk","risk","crisis, troubl*, wrong","Affective, uncertainty","Acrimony","Prone",2,102,15)
a46 <- c("Positive emotions","positiveemo","perfect, inspir*, impress*","Affective, non-conflictual","Acrimony","Averse",2,455,16)
a47 <- c("Achieve","achieve","eradicate, forge, deliver","Performance, charisma", "Authority","Prone",2,213,94)
a48 <- c("Certainty","certainty","blatant*, boldness, clear","Resolve, charisma","Authority","Prone",2,113,34)
a49 <- c("Power","power","up, strong, army","Decisiveness, charisma","Authority","Prone",2,517,45)
a50 <- c("Tentative","tentative","depends, guess, or","Indecisiveness, compromise","Authority","Averse",2,177,11)
Appendix1<- rbind(header, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20, a21, a22, a23, a24, a25, a26, a27, a28, a29, a30, a31, a32, a33, a34, a35, a36, a37, a38, a39, a40, a41, a42, a43, a44, a45, a46, a47, a48, a49, a50)
colnames(Appendix1) <- Appendix1[1, ]
Appendix1 <- Appendix1[-1, ]
rownames(Appendix1) <- NULL
Appendix1 <- as.data.frame(Appendix1)
names(Appendix1)[6] <- paste0(names(Appendix1)[6],footnote_marker_number(1))
names(Appendix1)[7] <- paste0(names(Appendix1)[7],footnote_marker_number(2))
names(Appendix1)[8] <- paste0(names(Appendix1)[8],footnote_marker_number(3))
names(Appendix1)[9] <- paste0(names(Appendix1)[9],footnote_marker_number(4))
kable(Appendix1,"latex",booktabs = T, align="l", escape = F) %>%
kable_styling(latex_options = c("basic","scale_down")) %>%
row_spec(0, align= "l", hline_after= T, bold=T) %>%
footnote(number = c("Relationship to populist style", "Hypotheses","Seeds/list of features","Extension/Ad hoc features"))#Prepararing Appendix 2
colnames(v1)[3]<-"therest"
s <- colMeans(v1)
ss <- sum(s)
h1h2<-s/ss
#h1h2
#sum(h1h2)
colnames(v4)[5]<-"therest"
s <- colMeans(v4)
ss <- sum(s)
proneaverse<-s/ss
#proneaverse
#sum(proneaverse)
colnames(v7)[11]<-"therest"
s <- colMeans(v7)
ss <- sum(s)
variables<-s/ss
#variables
#sum(variables)
colnames(v10)[51]<-"therest"
s <- colMeans(v10)
ss <- sum(s)
categories<-s/ss
#categories
#sum(categories)
colnames(v13)[5]<-"therest"
s <- colMeans(v13)
ss <- sum(s)
seeds<-s/ss
#seeds
#sum(seeds)
h1h2t<-setDT(as.list(h1h2), keep.rownames = TRUE)[]
h1h2df<-as.data.frame(h1h2t)
rownames(h1h2df)<-"Hypotheses"
proneaverset<-setDT(as.list(proneaverse), keep.rownames = TRUE)[]
proneaversedf<-as.data.frame(proneaverset)
rownames(proneaversedf)<-"Pop. prone & averse"
variablest<-setDT(as.list(variables), keep.rownames = TRUE)[]
variablesdf<-as.data.frame(variablest)
rownames(variablesdf)<-"Pop. variables"
categoriest<-setDT(as.list(categories), keep.rownames = TRUE)[]
categoriesdf<-as.data.frame(categoriest)
rownames(categoriesdf)<-"Pop. categories"
seedst<-setDT(as.list(seeds), keep.rownames = TRUE)[]
seedsdf<-as.data.frame(seedst)
rownames(seedsdf)<-"Seeds/extension"
#Appendix 2a (title: Summary statistics of Hypotheses 1 & 2)
v1f<-v1
colnames(v1f)[1]<-"Hypothesis 1"
colnames(v1f)[2]<-"Hypothesis 2"
colnames(v1f)[3]<-"The rest"
#Appendix 2b (title: Summary statistics of features’ relationship to populist styling)
v4f<-v4
colnames(v4f)[1]<-"Pop. prone (H1)"
colnames(v4f)[2]<-"Pop. prone (H2)"
colnames(v4f)[3]<-"Pop. averse (H1)"
colnames(v4f)[4]<-"Pop. averse (H2)"
colnames(v4f)[5]<-"The rest"
#Appendix 2c (title: Summary statistics of dictionary variables)
v7f<-v7
colnames(v7f)[1]<-"Deintermediation (H1,a.)"
colnames(v7f)[2]<-"Intimacy (H1,a.)"
colnames(v7f)[3]<-"Simplicity (H1,a.)"
colnames(v7f)[4]<-"Deintermediation (H1,p.)"
colnames(v7f)[5]<-"Intimacy (H1,p.)"
colnames(v7f)[6]<-"Simplicity (H1,p.)"
colnames(v7f)[7]<-"Authority (H2,a.)"
colnames(v7f)[8]<-"Acrimony (H2,a.)"
colnames(v7f)[9]<-"Authority (H2,p.)"
colnames(v7f)[10]<-"Acrimony (H2,p.)"
colnames(v7f)[11]<-"The rest"
#Appendix 2d (title: Summary statistics of dictionary categories)
v10f<-v10
colnames(v10f)[1]<-"Assent"
colnames(v10f)[2]<-"Insight"
colnames(v10f)[3]<-"Institutional processes"
colnames(v10f)[4]<-"Political parties"
colnames(v10f)[5]<-"We"
colnames(v10f)[6]<-"Cognitive processes"
colnames(v10f)[7]<-"Conceptual notions"
colnames(v10f)[8]<-"Long words"
colnames(v10f)[9]<-"Democratic principles"
colnames(v10f)[10]<-"Electoral processes"
colnames(v10f)[11]<-"Future focus"
colnames(v10f)[12]<-"I and you"
colnames(v10f)[13]<-"Motion"
colnames(v10f)[14]<-"Personalized governance"
colnames(v10f)[15]<-"Personalized rewards"
colnames(v10f)[16]<-"Rhetorical questions"
colnames(v10f)[17]<-"Body"
colnames(v10f)[18]<-"Family"
colnames(v10f)[19]<-"Food and farming"
colnames(v10f)[20]<-"Friends"
colnames(v10f)[21]<-"Gendered references"
colnames(v10f)[22]<-"Health"
colnames(v10f)[23]<-"Home"
colnames(v10f)[25]<-"Leisure"
colnames(v10f)[24]<-"Money"
colnames(v10f)[26]<-"Physical feel"
colnames(v10f)[27]<-"Pronouns"
colnames(v10f)[28]<-"Religion"
colnames(v10f)[29]<-"Third-person"
colnames(v10f)[30]<-"Cultural nationalism"
colnames(v10f)[31]<-"Elites"
colnames(v10f)[32]<-"Festival"
colnames(v10f)[33]<-"Non-elites"
colnames(v10f)[34]<-"Numbers"
colnames(v10f)[35]<-"Past focus"
colnames(v10f)[36]<-"Short sentences"
colnames(v10f)[37]<-"Time"
colnames(v10f)[38]<-"Tentative"
colnames(v10f)[39]<-"Positive emotions"
colnames(v10f)[40]<-"Achieve"
colnames(v10f)[41]<-"Certainty"
colnames(v10f)[42]<-"Power"
colnames(v10f)[43]<-"Anger"
colnames(v10f)[44]<-"Anxiety"
colnames(v10f)[45]<-"Community references"
colnames(v10f)[46]<-"Conflict"
colnames(v10f)[47]<-"Negative emotions"
colnames(v10f)[48]<-"Risk"
colnames(v10f)[49]<-"Sadness"
colnames(v10f)[50]<-"Swear words"
colnames(v10f)[51]<-"The rest"
#Appendix 2e (title: Summary statistics of dictionary types)
v13f<-v13
colnames(v13f)[1]<-"Extension"
colnames(v13f)[2]<-"Seeds"
colnames(v13f)[3]<-"Ad hoc"
colnames(v13f)[4]<-"Seeds gen."
colnames(v13f)[5]<-"The rest"
#Generating Appendix 2
Table1a<-stargazer(as.data.frame(v1f), type="latex")##
## % Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Sat, May 02, 2020 - 18:53:42
## \begin{table}[!htbp] \centering
## \caption{}
## \label{}
## \begin{tabular}{@{\extracolsep{5pt}}lccccccc}
## \\[-1.8ex]\hline
## \hline \\[-1.8ex]
## Statistic & \multicolumn{1}{c}{N} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{St. Dev.} & \multicolumn{1}{c}{Min} & \multicolumn{1}{c}{Pctl(25)} & \multicolumn{1}{c}{Pctl(75)} & \multicolumn{1}{c}{Max} \\
## \hline \\[-1.8ex]
## Hypothesis 1 & 4,199 & 2,448.623 & 1,991.624 & 52 & 1,147.5 & 3,115.5 & 24,993 \\
## Hypothesis 2 & 4,199 & 697.675 & 558.376 & 6 & 334 & 891 & 6,782 \\
## The rest & 4,199 & 947.060 & 710.134 & 13 & 461 & 1,223 & 10,423 \\
## \hline \\[-1.8ex]
## \end{tabular}
## \end{table}Table1b<-stargazer(as.data.frame(v4f), type="latex")##
## % Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Sat, May 02, 2020 - 18:53:42
## \begin{table}[!htbp] \centering
## \caption{}
## \label{}
## \begin{tabular}{@{\extracolsep{5pt}}lccccccc}
## \\[-1.8ex]\hline
## \hline \\[-1.8ex]
## Statistic & \multicolumn{1}{c}{N} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{St. Dev.} & \multicolumn{1}{c}{Min} & \multicolumn{1}{c}{Pctl(25)} & \multicolumn{1}{c}{Pctl(75)} & \multicolumn{1}{c}{Max} \\
## \hline \\[-1.8ex]
## Pop. prone (H1) & 4,199 & 1,050.940 & 826.194 & 12 & 501 & 1,336 & 12,703 \\
## Pop. prone (H2) & 4,199 & 1,397.683 & 1,235.847 & 40 & 615.5 & 1,731 & 12,290 \\
## Pop. averse (H1) & 4,199 & 163.895 & 128.722 & 2 & 82 & 206 & 1,476 \\
## Pop. averse (H2) & 4,199 & 533.780 & 436.675 & 4 & 248 & 688 & 5,306 \\
## The rest & 4,199 & 947.060 & 710.134 & 13 & 461 & 1,223 & 10,423 \\
## \hline \\[-1.8ex]
## \end{tabular}
## \end{table}Table1c<-stargazer(as.data.frame(v7f), type="latex")##
## % Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Sat, May 02, 2020 - 18:53:42
## \begin{table}[!htbp] \centering
## \caption{}
## \label{}
## \begin{tabular}{@{\extracolsep{5pt}}lccccccc}
## \\[-1.8ex]\hline
## \hline \\[-1.8ex]
## Statistic & \multicolumn{1}{c}{N} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{St. Dev.} & \multicolumn{1}{c}{Min} & \multicolumn{1}{c}{Pctl(25)} & \multicolumn{1}{c}{Pctl(75)} & \multicolumn{1}{c}{Max} \\
## \hline \\[-1.8ex]
## Deintermediation (H1,a.) & 4,199 & 176.964 & 156.615 & 0 & 78 & 226 & 2,619 \\
## Intimacy (H1,a.) & 4,199 & 97.707 & 90.619 & 0 & 40 & 124 & 1,014 \\
## Simplicity (H1,a.) & 4,199 & 776.270 & 601.541 & 12 & 372 & 995 & 9,070 \\
## Deintermediation (H1,p.) & 4,199 & 243.522 & 222.100 & 2 & 102 & 305 & 2,741 \\
## Intimacy (H1,p.) & 4,199 & 689.321 & 610.305 & 14 & 304 & 850 & 6,346 \\
## Simplicity (H1,p.) & 4,199 & 464.840 & 421.663 & 10 & 197 & 577.5 & 4,631 \\
## Authority (H2,a.) & 4,199 & 63.037 & 77.741 & 0 & 18 & 78 & 928 \\
## Acrimony (H2,a.) & 4,199 & 100.858 & 65.464 & 2 & 56 & 129 & 548 \\
## Authority (H2,p.) & 4,199 & 306.640 & 227.246 & 4 & 150 & 400 & 3,036 \\
## Acrimony (H2,p.) & 4,199 & 227.140 & 232.163 & 0 & 78 & 294 & 2,720 \\
## The rest & 4,199 & 947.060 & 710.134 & 13 & 461 & 1,223 & 10,423 \\
## \hline \\[-1.8ex]
## \end{tabular}
## \end{table}Table1d<-stargazer(as.data.frame(v10f), type="latex")##
## % Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Sat, May 02, 2020 - 18:53:42
## \begin{table}[!htbp] \centering
## \caption{}
## \label{}
## \begin{tabular}{@{\extracolsep{5pt}}lccccccc}
## \\[-1.8ex]\hline
## \hline \\[-1.8ex]
## Statistic & \multicolumn{1}{c}{N} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{St. Dev.} & \multicolumn{1}{c}{Min} & \multicolumn{1}{c}{Pctl(25)} & \multicolumn{1}{c}{Pctl(75)} & \multicolumn{1}{c}{Max} \\
## \hline \\[-1.8ex]
## Assent & 4,199 & 3.491 & 4.806 & 0 & 0 & 4 & 60 \\
## Insight & 4,199 & 83.250 & 77.437 & 0 & 34 & 107 & 1,204 \\
## Institutional processes & 4,199 & 88.401 & 84.727 & 0 & 35 & 113 & 1,374 \\
## Political parties & 4,199 & 1.822 & 6.140 & 0 & 0 & 1 & 102 \\
## We & 4,199 & 97.707 & 90.619 & 0 & 40 & 124 & 1,014 \\
## Cognitive processes & 4,199 & 392.755 & 366.333 & 2 & 158 & 500 & 4,896 \\
## Conceptual notions & 4,199 & 123.648 & 90.965 & 2 & 59 & 165 & 1,404 \\
## Long words & 4,199 & 239.661 & 171.019 & 6 & 120 & 320 & 2,586 \\
## Democratic principles & 4,199 & 20.204 & 26.197 & 0 & 4 & 27 & 342 \\
## Electoral processes & 4,199 & 5.593 & 12.483 & 0 & 0 & 6 & 192 \\
## Future focus & 4,199 & 50.789 & 49.976 & 0 & 18 & 66 & 706 \\
## I and you & 4,199 & 72.540 & 81.791 & 0 & 24 & 88 & 988 \\
## Motion & 4,199 & 70.956 & 61.284 & 0 & 28 & 94 & 654 \\
## Personalized governance & 4,199 & 13.634 & 21.307 & 0 & 2 & 16 & 242 \\
## Personalized rewards & 4,199 & 26.392 & 21.084 & 0 & 12 & 34 & 256 \\
## Rhetorical questions & 4,199 & 3.619 & 14.109 & 0 & 0 & 3 & 731 \\
## Body & 4,199 & 9.423 & 10.606 & 0 & 2 & 12 & 116 \\
## Family & 4,199 & 13.347 & 29.498 & 0 & 2 & 12 & 444 \\
## Food and farming & 4,199 & 16.516 & 32.190 & 0 & 2 & 16 & 462 \\
## Friends & 4,199 & 9.114 & 12.803 & 0 & 2 & 12 & 224 \\
## Gendered references & 4,199 & 29.894 & 43.606 & 0 & 4 & 38 & 476 \\
## Health & 4,199 & 24.367 & 30.267 & 0 & 8 & 28 & 416 \\
## Home & 4,199 & 14.605 & 24.363 & 0 & 2 & 16 & 404 \\
## Money & 4,199 & 17.960 & 24.281 & 0 & 4 & 22 & 260 \\
## Leisure & 4,199 & 57.909 & 70.934 & 0 & 14 & 74 & 786 \\
## Physical feel & 4,199 & 8.721 & 10.024 & 0 & 2 & 12 & 114 \\
## Pronouns & 4,199 & 417.514 & 380.249 & 0 & 182 & 521 & 4,694 \\
## Religion & 4,199 & 22.296 & 33.040 & 0 & 4 & 26 & 408 \\
## Third-person & 4,199 & 47.656 & 53.891 & 0 & 14 & 62 & 676 \\
## Cultural nationalism & 4,199 & 25.818 & 39.019 & 0 & 6 & 28 & 586 \\
## Elites & 4,199 & 7.833 & 11.260 & 0 & 2 & 10 & 150 \\
## Festival & 4,199 & 1.511 & 6.210 & 0 & 0 & 0 & 144 \\
## Non-elites & 4,199 & 46.389 & 61.441 & 0 & 12 & 53 & 800 \\
## Numbers & 4,199 & 45.056 & 48.070 & 0 & 15 & 58 & 616 \\
## Past focus & 4,199 & 99.229 & 107.541 & 0 & 34 & 124 & 1,370 \\
## Short sentences & 4,199 & 93.660 & 76.310 & 1 & 44 & 118 & 933 \\
## Time & 4,199 & 145.344 & 123.689 & 0 & 64 & 186 & 1,222 \\
## Tentative & 4,199 & 63.037 & 77.741 & 0 & 18 & 78 & 928 \\
## Positive emotions & 4,199 & 100.858 & 65.464 & 2 & 56 & 129 & 548 \\
## Achieve & 4,199 & 104.441 & 75.122 & 0 & 50 & 142 & 1,118 \\
## Certainty & 4,199 & 61.935 & 52.180 & 0 & 28 & 80 & 788 \\
## Power & 4,199 & 140.264 & 115.435 & 2 & 64 & 180 & 1,144 \\
## Anger & 4,199 & 17.771 & 24.348 & 0 & 4 & 22 & 276 \\
## Anxiety & 4,199 & 22.997 & 24.369 & 0 & 6 & 30 & 220 \\
## Community references & 4,199 & 17.544 & 23.401 & 0 & 4 & 22 & 256 \\
## Conflict & 4,199 & 75.831 & 86.083 & 0 & 22 & 98 & 1,178 \\
## Negative emotions & 4,199 & 61.941 & 66.884 & 0 & 18 & 80 & 710 \\
## Risk & 4,199 & 22.957 & 25.576 & 0 & 6 & 30 & 326 \\
## Sadness & 4,199 & 7.975 & 9.185 & 0 & 2 & 12 & 86 \\
## Swear words & 4,199 & 0.123 & 0.608 & 0 & 0 & 0 & 16 \\
## The rest & 4,199 & 947.060 & 710.134 & 13 & 461 & 1,223 & 10,423 \\
## \hline \\[-1.8ex]
## \end{tabular}
## \end{table}Table1e<-stargazer(as.data.frame(v13f), type="latex")##
## % Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Sat, May 02, 2020 - 18:53:43
## \begin{table}[!htbp] \centering
## \caption{}
## \label{}
## \begin{tabular}{@{\extracolsep{5pt}}lccccccc}
## \\[-1.8ex]\hline
## \hline \\[-1.8ex]
## Statistic & \multicolumn{1}{c}{N} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{St. Dev.} & \multicolumn{1}{c}{Min} & \multicolumn{1}{c}{Pctl(25)} & \multicolumn{1}{c}{Pctl(75)} & \multicolumn{1}{c}{Max} \\
## \hline \\[-1.8ex]
## Extension & 4,199 & 215.261 & 182.162 & 0 & 94 & 272 & 1,834 \\
## Seeds & 4,199 & 2,196.643 & 1,854.684 & 28 & 1,006 & 2,809 & 22,650 \\
## Ad hoc & 4,199 & 494.732 & 396.057 & 12 & 228 & 628 & 5,007 \\
## Seeds gen. & 4,199 & 239.661 & 171.019 & 6 & 120 & 320 & 2,586 \\
## The rest & 4,199 & 947.060 & 710.134 & 13 & 461 & 1,223 & 10,423 \\
## \hline \\[-1.8ex]
## \end{tabular}
## \end{table}#Appendix 3 (title: Effect of time on populist categories and populist ratios for hypotheses 1 and 2)
stargazer( lm.h1a,lm.h2a,lm.h1c,lm.h2c,
type = "latex", style = "ajps",
column.labels= c("Full model H1", "Full model H2", "Ratio model H1", "Ratio model H2"),
dep.var.labels.include = FALSE,
intercept.bottom = FALSE,
notes = c( "Standard errors are in parentheses. The full and ratio models (H1 and H2) are based on yearly time series."),
notes.append = TRUE,
model.numbers = FALSE,
star.cutoffs = c( .1,.05,.01,.001 ),
digits = 2,
covariate.labels = c("Intercept","Long words","Cognitive processes","Conceptual notions","Principles","Institutional processes","Political parties","We","Assent","Insight","Third-person","Family","Religion","Gendered","Leisure","Body","Health","Friends","Home","Pronouns","Physical feel","Food farm","Money","Festival","Elites","Non-elites","Short sentences","Numbers","Past focus","Cultural nationalism","Time","I and you", "Electoral processes","Personal governance","Rhetorical questions","Future focus","Personal reward","Motion","Positive emotions","Tentative","Negative emotions","Anger","Anxiety","Sadness","Swearwords","Risk","Communities","Conflict","Certainty","Achieve","Power","Ratio H1","Ratio H2")
)##
## % Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Sat, May 02, 2020 - 18:53:43
## \begin{table}[!htbp] \centering
## \caption{}
## \label{}
## \begin{tabular}{@{\extracolsep{5pt}}lcccc}
## \\[-1.8ex]\hline \\[-1.8ex]
## \\[-1.8ex] & \multicolumn{2}{c}{\textbf{normal}} & \multicolumn{2}{c}{\textbf{OLS}} \\
## & \textbf{Full model H1} & \textbf{Full model H2} & \textbf{Ratio model H1} & \textbf{Ratio model H2} \\
## \hline \\[-1.8ex]
## Intercept & 1962.51$^{****}$ & 2019.18$^{****}$ & 1981.83$^{****}$ & 1998.71$^{****}$ \\
## & (4.69) & (1.66) & (0.79) & (0.88) \\
## Long words & 57.05$^{***}$ & & & \\
## & (19.31) & & & \\
## Cognitive processes & $-$267.28$^{****}$ & & & \\
## & (18.45) & & & \\
## Conceptual notions & 265.82$^{****}$ & & & \\
## & (30.41) & & & \\
## Principles & $-$227.80$^{****}$ & & & \\
## & (38.73) & & & \\
## Institutional processes & 211.03$^{****}$ & & & \\
## & (21.74) & & & \\
## Political parties & 417.57$^{**}$ & & & \\
## & (180.43) & & & \\
## We & $-$61.49$^{**}$ & & & \\
## & (29.33) & & & \\
## Assent & $-$909.34$^{****}$ & & & \\
## & (201.00) & & & \\
## Insight & 166.88$^{****}$ & & & \\
## & (41.51) & & & \\
## Third-person & 19.04 & & & \\
## & (47.50) & & & \\
## Family & 220.13$^{****}$ & & & \\
## & (66.59) & & & \\
## Religion & 283.73$^{****}$ & & & \\
## & (35.18) & & & \\
## Gendered & $-$5.66 & & & \\
## & (38.66) & & & \\
## Leisure & 146.59$^{****}$ & & & \\
## & (34.50) & & & \\
## Body & 80.55 & & & \\
## & (98.96) & & & \\
## Health & 59.36$^{*}$ & & & \\
## & (34.77) & & & \\
## Friends & 231.30$^{***}$ & & & \\
## & (75.28) & & & \\
## Home & 314.10$^{****}$ & & & \\
## & (61.21) & & & \\
## Pronouns & 48.70$^{**}$ & & & \\
## & (22.18) & & & \\
## Physical feel & 53.15 & & & \\
## & (119.10) & & & \\
## Food farm & 216.95$^{****}$ & & & \\
## & (44.75) & & & \\
## Money & 293.32$^{****}$ & & & \\
## & (27.27) & & & \\
## Festival & 113.54 & & & \\
## & (145.81) & & & \\
## Elites & 288.82$^{**}$ & & & \\
## & (112.10) & & & \\
## Non-elites & 98.99$^{**}$ & & & \\
## & (41.44) & & & \\
## Short sentences & 256.11$^{****}$ & & & \\
## & (51.31) & & & \\
## Numbers & 29.52 & & & \\
## & (43.30) & & & \\
## Past focus & $-$174.05$^{****}$ & & & \\
## & (26.51) & & & \\
## Cultural nationalism & 321.92$^{****}$ & & & \\
## & (51.93) & & & \\
## Time & 273.56$^{****}$ & & & \\
## & (23.67) & & & \\
## I and you & 268.06$^{****}$ & & & \\
## & (33.25) & & & \\
## Electoral processes & 134.55 & & & \\
## & (115.79) & & & \\
## Personal governance & 279.84$^{****}$ & & & \\
## & (72.27) & & & \\
## Rhetorical questions & 313.35$^{****}$ & & & \\
## & (83.83) & & & \\
## Future focus & $-$9.87 & & & \\
## & (40.46) & & & \\
## Personal reward & 251.98$^{***}$ & & & \\
## & (79.77) & & & \\
## Motion & 110.97$^{***}$ & & & \\
## & (35.15) & & & \\
## Positive emotions & & $-$133.14$^{****}$ & & \\
## & & (24.95) & & \\
## Tentative & & $-$842.88$^{****}$ & & \\
## & & (38.88) & & \\
## Negative emotions & & $-$1022.60$^{****}$ & & \\
## & & (73.40) & & \\
## Anger & & 671.68$^{****}$ & & \\
## & & (95.64) & & \\
## Anxiety & & 482.79$^{****}$ & & \\
## & & (81.50) & & \\
## Sadness & & 458.92$^{****}$ & & \\
## & & (123.55) & & \\
## Swearwords & & 853.53 & & \\
## & & (1394.31) & & \\
## Risk & & 230.38$^{***}$ & & \\
## & & (75.39) & & \\
## Communities & & 137.62$^{***}$ & & \\
## & & (47.33) & & \\
## Conflict & & $-$467.18$^{****}$ & & \\
## & & (38.79) & & \\
## Certainty & & $-$84.78$^{**}$ & & \\
## & & (42.10) & & \\
## Achieve & & 68.67$^{***}$ & & \\
## & & (24.18) & & \\
## Power & & 128.96$^{****}$ & & \\
## & & (20.31) & & \\
## Ratio H1 & & & 9.65$^{****}$ & \\
## & & & (0.54) & \\
## Ratio H2 & & & & $-$1.12$^{****}$ \\
## & & & & (0.25) \\
## N & 4199 & 4199 & 4199 & 4199 \\
## R-squared & & & 0.07 & 0.005 \\
## Adj. R-squared & & & 0.07 & 0.005 \\
## Log Likelihood & $-$16697.66 & $-$16937.73 & & \\
## Residual Std. Error (df = 4197) & & & 18.15 & 18.78 \\
## F Statistic (df = 1; 4197) & & & 315.75$^{****}$ & 20.50$^{****}$ \\
## AIC & 33471.32 & 33903.46 & & \\
## \hline \\[-1.8ex]
## \multicolumn{5}{l}{$^{***}$p $<$ .01; $^{**}$p $<$ .05; $^{*}$p $<$ .1} \\
## \multicolumn{5}{l}{Standard errors are in parentheses. The full and ratio models (H1 and H2) are based on yearly time series.} \\
## \end{tabular}
## \end{table}Contextualization of the Features of the populist dictionary
#Method: Below is an example of the target-word collocations performed on the 'seed' features of the LIWC dictionary and used to generate the 'extension' features of the populist dictionary (level 5)
nostoptokssubworkcorpus <- tokens_select(ngramstokssubworkcorpus, pattern = stopwords('en'), selection = 'remove', case_insensitive = TRUE)
notpuncnostoptokssubworkcorpus <- nostoptokssubworkcorpus %>% tokens_remove('[\\p{P}\\p{S}]', valuetype = 'regex', padding = TRUE)
v2.1 <- c("abnormal", "abnormality", "absolute", "absolutely", "accept", "acceptance", "accepted", "accepting", "accepts", "accountability", "accurate","accurately", "acknowledge", "acknowledge", "activate","activate", "actually", "adjust", "adjusting","adjusting", "admit", "admits", "admitted", "admitting", "affect", "affected", "affecting", "affects", "afterthought", "afterthoughts", "against", "aggravate","aggravates","aggravating", "aggravated", "ain't", "aint", "all", "allot", "almost", "allow", "allows", "allowing", "allowed", "alot", "alternative", "alternatives", "although", "altogether", "always", "ambiguous", "ambiguity", "ambiguity", "analysis","analyses", "analytical", "analytic", "answer", "answers", "any", "anybody", "anyhow", "anyone","anyones", "anything", "anytime", "anywhere", "apart", "apparent", "apparently", "appear", "appeared", "appearing", "appears", "appreciate","appreciated", "apprehensive", "approximate", "approximated", "approximation", "approximatively", "arbitrary", "aren't", "arent", "assume", "assumes", "assure","assures", "assurance", "attention", "attentive", "attentionate", "attribute","attributes", "aware", "awareness", "barely", "based", "basis", "bc", "became", "because", "become", "becomes", "becoming", "belief", "beliefs", "believe", "believed", "believes", "believing", "besides", "bet", "bets", "betting", "blatant", "blatantely", "blur","blurred", "bosses", "but", "can't", "cannot", "cant", "category", "categories", "cause", "caused", "causes", "causing", "certain", "chance", "chances", "change", "change","changed", "changes", "changing", "choice", "choices", "choose", "chooses", "clarify", "clarified", "clarification", "clear", "clearly", "closure", "clue", "coherent", "coherence", "commit", "commited", "commitment", "commitments", "commits", "committed", "committing", "compel", "compels", "compelling", "complete", "completed", "completely", "completes", "complex", "complexity", "compliance", "compliant", "complicate", "complicated", "complicates", "complicating", "complication", "complications", "complied", "complies", "comply", "complies", "complying", "comprehsive", "comprehend", "comprehending", "concentrate", "concentrating", "concentrated", "conclude", "concluded", "concluding", "conclusion", "conclusions", "concluded", "conclusive", "confess", "confessed", "confession", "confidence", "confident", "confidently", "confuse", "confused", "confuses", "confusing", "confusion", "confusions", "conscious", "consciously", "consequence", "consequences", "consider", "consideration", "considered", "considering", "considers", "contemplate", "contemplating", "contingent", "control", "convince", "convinces", "convinced", "convincing", "correct", "correction", "corrections", "correlate", "correlates", "correlation", "cos", "could", "could've", "couldn't", "couldnt", "couldve", "coz", "create", "created", "creates", "creating", "creation", "creations", "creative", "creativity", "curious", "curiosity", "curiosly", "cuz", "deceive", "deceives", "deceiving", "decide", "decides", "deciding", "decided", "decides", "deciding", "decision", "decisions", "decisive", "deduction", "deductive", "deductions", "deductively", "define", "defined", "defines", "defining", "definite", "definitely", "definition", "definitive", "definitively", "depend", "depended", "depending", "depends", "desire", "desires", "desirable", "despite", "determination", "determine", "determined", "determines", "determining", "diagnose", "diagnoses", "diagnosed", "diagnosis", "didn't", "didnt", "differ", "differed", "difference", "differences", "different", "differential", "differentiation", "differentiated", "differently", "differing", "differs", "directly", "discern", "discerns", "discerning", "disclose", "disclosed", "disclosing", "discover", "discovers", "discovering", "disillusion", "disillusions", "disorient", "disorients", "disorienting", "dissimilar", "distinct", "distinctive", "distinguish", "distinguishes", "distinguishing", "distract", "distracted", "distraction", "doubt", "doubts", "doubting", "dreams", "dubious")
toksv2.1 <- tokens_keep(notpuncnostoptokssubworkcorpus, phrase(v2.1), window = 10, valuetype="fixed")
toksnov2.1 <- tokens_remove(notpuncnostoptokssubworkcorpus, phrase(v2.1), window = 10, valuetype="fixed")
dfmattoksv2.1 <- dfm(toksv2.1)
#head(toksnov2.1)
dfmattoksv2.1perpm <- dfm_group(dfmattoksv2.1, groups = "loc")
dfmattoksnov2.1 <- dfm(toksnov2.1)
dfmattoksnov2.1perpm <- dfm_group(dfmattoksnov2.1, groups = "loc")
tstatkeyv2.1 <- textstat_keyness(rbind(dfmattoksv2.1perpm, dfmattoksnov2.1perpm), seq_len(ndoc(dfmattoksv2.1perpm)))
tstatkeyv2.1subset <- tstatkeyv2.1[tstatkeyv2.1$n_target > 10, ]
head(tstatkeyv2.1subset, 1000)## feature chi2 p n_target n_reference
## 1 change 8474.863204 0.000000e+00 5254 0
## 2 become 8009.794870 0.000000e+00 4966 0
## 3 different 6458.385283 0.000000e+00 4005 0
## 4 believe 6135.595021 0.000000e+00 3805 0
## 5 always 5399.741064 0.000000e+00 3349 0
## 6 certain 4543.047388 0.000000e+00 2818 0
## 7 attention 4043.000007 0.000000e+00 2508 0
## 8 create 3822.033386 0.000000e+00 2371 0
## 9 based 3396.267224 0.000000e+00 2107 0
## 10 commitment 2994.738323 0.000000e+00 1858 0
## 11 changes 2972.163726 0.000000e+00 1844 0
## 12 aware 2651.297225 0.000000e+00 1645 0
## 13 created 2543.273211 0.000000e+00 1578 0
## 14 anything 2533.599576 0.000000e+00 1572 0
## 15 basis 2490.068541 0.000000e+00 1545 0
## 16 confidence 2432.027978 0.000000e+00 1509 0
## 17 consider 2399.783623 0.000000e+00 1489 0
## 18 committed 2373.988347 0.000000e+00 1473 0
## 19 doubt 2324.010524 0.000000e+00 1442 0
## 20 clear 2217.608431 0.000000e+00 1376 0
## 21 decision 2212.772047 0.000000e+00 1373 0
## 22 becomes 2211.159920 0.000000e+00 1372 0
## 23 almost 2188.590223 0.000000e+00 1358 0
## 24 cause 2178.917539 0.000000e+00 1352 0
## 25 decided 2112.821556 0.000000e+00 1311 0
## 26 changed 2043.502680 0.000000e+00 1268 0
## 27 control 1995.141461 0.000000e+00 1238 0
## 28 changing 1977.409176 0.000000e+00 1227 0
## 29 became 1775.912061 0.000000e+00 1102 0
## 30 complete 1704.987753 0.000000e+00 1058 0
## 31 decisions 1682.421220 0.000000e+00 1044 0
## 32 assure 1674.361778 0.000000e+00 1039 0
## 33 although 1663.078589 0.000000e+00 1032 0
## 34 confident 1540.577670 0.000000e+00 956 0
## 35 considered 1477.716970 0.000000e+00 917 0
## 36 accept 1469.657985 0.000000e+00 912 0
## 37 creating 1442.257570 0.000000e+00 895 0
## 38 despite 1371.339817 0.000000e+00 851 0
## 39 becoming 1326.211063 0.000000e+00 823 0
## 40 desire 1319.764144 0.000000e+00 819 0
## 41 affected 1279.471162 0.000000e+00 794 0
## 42 accepted 1264.965799 0.000000e+00 785 0
## 43 allow 1242.402016 0.000000e+00 771 0
## 44 difference 1216.615009 0.000000e+00 755 0
## 45 anyone 1195.663201 0.000000e+00 742 0
## 46 completely 1169.876528 0.000000e+00 726 0
## 47 cant 1165.041547 0.000000e+00 723 0
## 48 dreams 1155.371605 0.000000e+00 717 0
## 49 decide 1102.187386 0.000000e+00 684 0
## 50 differences 1090.905986 0.000000e+00 677 0
## 51 awareness 1079.624621 0.000000e+00 670 0
## 52 answer 1073.178142 0.000000e+00 666 0
## 53 determined 1068.343291 0.000000e+00 663 0
## 54 determination 1053.838776 0.000000e+00 654 0
## 55 clearly 1013.548763 0.000000e+00 629 0
## 56 completed 1002.267640 0.000000e+00 622 0
## 57 belief 995.821299 0.000000e+00 618 0
## 58 allowed 992.598134 0.000000e+00 616 0
## 59 directly 974.870773 0.000000e+00 605 0
## 60 correct 965.201340 0.000000e+00 599 0
## 61 complex 961.978202 0.000000e+00 597 0
## 62 consideration 955.531934 0.000000e+00 593 0
## 63 chance 939.416313 0.000000e+00 583 0
## 64 creative 934.581641 0.000000e+00 580 0
## 65 creation 881.400675 0.000000e+00 547 0
## 66 anybody 878.177612 0.000000e+00 545 0
## 67 actually 860.450813 0.000000e+00 534 0
## 68 consequences 844.335617 0.000000e+00 524 0
## 69 anywhere 833.055023 0.000000e+00 517 0
## 70 conscious 831.443512 0.000000e+00 516 0
## 71 climate 786.333674 0.000000e+00 805 180
## 72 convinced 765.372197 0.000000e+00 475 0
## 73 appreciate 762.149237 0.000000e+00 473 0
## 74 depends 758.926280 0.000000e+00 471 0
## 75 conclusion 723.473941 0.000000e+00 449 0
## 76 alternative 676.741844 0.000000e+00 420 0
## 77 creativity 667.073210 0.000000e+00 414 0
## 78 believed 662.238903 0.000000e+00 411 0
## 79 affect 652.570308 0.000000e+00 405 0
## 80 besides 633.233195 0.000000e+00 393 0
## 81 definitely 623.564678 0.000000e+00 387 0
## 82 causes 612.284773 0.000000e+00 380 0
## 83 choice 581.668067 0.000000e+00 361 0
## 84 absolutely 578.445271 0.000000e+00 359 0
## 85 depend 573.611083 0.000000e+00 356 0
## 86 considering 551.051622 0.000000e+00 342 0
## 87 caused 538.160564 0.000000e+00 334 0
## 88 didnt 523.658179 0.000000e+00 325 0
## 89 choose 512.378587 0.000000e+00 318 0
## 90 analysis 507.544487 0.000000e+00 315 0
## 91 accountability 468.871919 0.000000e+00 291 0
## 92 concentrate 449.535790 0.000000e+00 279 0
## 93 assurance 438.256430 0.000000e+00 272 0
## 94 answers 433.422429 0.000000e+00 269 0
## 95 conclude 420.531792 0.000000e+00 261 0
## 96 creates 386.694088 0.000000e+00 240 0
## 97 appear 375.414924 0.000000e+00 233 0
## 98 acceptance 375.414924 0.000000e+00 233 0
## 99 defined 344.800227 0.000000e+00 214 0
## 100 desirable 331.909906 0.000000e+00 206 0
## 101 complicated 331.909906 0.000000e+00 206 0
## 102 determine 323.853479 0.000000e+00 201 0
## 103 commitments 299.684306 0.000000e+00 186 0
## 104 commit 299.684306 0.000000e+00 186 0
## 105 appears 293.239220 0.000000e+00 182 0
## 106 doubts 293.239220 0.000000e+00 182 0
## 107 affects 285.182879 0.000000e+00 177 0
## 108 admit 283.571613 0.000000e+00 176 0
## 109 definite 267.458993 0.000000e+00 166 0
## 110 differently 265.847735 0.000000e+00 165 0
## 111 beliefs 265.847735 0.000000e+00 165 0
## 112 accepting 256.180201 0.000000e+00 159 0
## 113 define 256.180201 0.000000e+00 159 0
## 114 choices 249.735193 0.000000e+00 155 0
## 115 affecting 246.512694 0.000000e+00 153 0
## 116 atmosphere 246.268487 0.000000e+00 573 326
## 117 believes 244.901445 0.000000e+00 152 0
## 118 assume 238.456457 0.000000e+00 148 0
## 119 definition 238.456457 0.000000e+00 148 0
## 120 discover 235.233968 0.000000e+00 146 0
## 121 decisive 233.622724 0.000000e+00 145 0
## 122 situation 229.137144 0.000000e+00 1586 1490
## 123 concentrated 223.955277 0.000000e+00 139 0
## 124 consequence 223.955277 0.000000e+00 139 0
## 125 causing 223.955277 0.000000e+00 139 0
## 126 category 222.344038 0.000000e+00 138 0
## 127 appreciated 220.732800 0.000000e+00 137 0
## 128 acknowledge 212.676621 0.000000e+00 132 0
## 129 apart 204.620460 0.000000e+00 127 0
## 130 differ 201.398001 0.000000e+00 125 0
## 131 convince 191.730640 0.000000e+00 119 0
## 132 allowing 190.119416 0.000000e+00 118 0
## 133 altogether 186.896969 0.000000e+00 116 0
## 134 appeared 185.285747 0.000000e+00 115 0
## 135 concluded 185.285747 0.000000e+00 115 0
## 136 confusion 183.674526 0.000000e+00 114 0
## 137 distinct 183.674526 0.000000e+00 114 0
## 138 distinctive 182.063305 0.000000e+00 113 0
## 139 complexity 180.452085 0.000000e+00 112 0
## 140 admitted 178.840866 0.000000e+00 111 0
## 141 absolute 178.840866 0.000000e+00 111 0
## 142 categories 175.618430 0.000000e+00 109 0
## 143 can 173.106716 0.000000e+00 10478 14262
## 144 conclusions 170.784780 0.000000e+00 106 0
## 145 else 167.089978 0.000000e+00 672 517
## 146 anyhow 165.951138 0.000000e+00 103 0
## 147 consciously 162.728713 0.000000e+00 101 0
## 148 desires 157.895081 0.000000e+00 98 0
## 149 adjust 157.895081 0.000000e+00 98 0
## 150 conditions 154.436268 0.000000e+00 783 662
## 151 things 152.479487 0.000000e+00 2385 2723
## 152 environment 148.684718 0.000000e+00 1367 1390
## 153 defining 143.394224 0.000000e+00 89 0
## 154 compelling 141.783022 0.000000e+00 88 0
## 155 chances 133.727019 0.000000e+00 83 0
## 156 policy 131.290151 0.000000e+00 2031 2314
## 157 accurate 130.504623 0.000000e+00 81 0
## 158 curiosity 130.504623 0.000000e+00 81 0
## 159 problems 127.276293 0.000000e+00 2760 3330
## 160 depending 119.226259 0.000000e+00 74 0
## 161 apparently 114.392686 0.000000e+00 71 0
## 162 allows 114.392686 0.000000e+00 71 0
## 163 deciding 112.781496 0.000000e+00 70 0
## 164 firm 112.598233 0.000000e+00 338 225
## 165 barely 111.170307 0.000000e+00 69 0
## 166 believing 107.947931 0.000000e+00 67 0
## 167 compel 106.336744 0.000000e+00 66 0
## 168 considers 106.336744 0.000000e+00 66 0
## 169 assumes 104.725558 0.000000e+00 65 0
## 170 curious 104.725558 0.000000e+00 65 0
## 171 government 104.124127 0.000000e+00 6642 9080
## 172 circumstances 103.383961 0.000000e+00 424 329
## 173 paid 103.105655 0.000000e+00 356 255
## 174 difficult 102.826076 0.000000e+00 1075 1128
## 175 adversely 101.812693 0.000000e+00 81 9
## 176 determining 101.503188 0.000000e+00 63 0
## 177 apparent 101.503188 0.000000e+00 63 0
## 178 defines 101.503188 0.000000e+00 63 0
## 179 concluding 99.892004 0.000000e+00 62 0
## 180 compliance 98.280820 0.000000e+00 61 0
## 181 alternatives 96.669638 0.000000e+00 60 0
## 182 accepts 96.669638 0.000000e+00 60 0
## 183 confused 95.058456 0.000000e+00 59 0
## 184 aggravated 93.447275 0.000000e+00 58 0
## 185 committing 91.836094 0.000000e+00 57 0
## 186 distinguish 90.224915 0.000000e+00 56 0
## 187 concentrating 88.613736 0.000000e+00 55 0
## 188 attributes 87.002558 0.000000e+00 54 0
## 189 draw 86.739082 0.000000e+00 344 263
## 190 attitude 84.411102 0.000000e+00 435 370
## 191 enterprise 83.611072 0.000000e+00 421 355
## 192 firmly 83.332387 0.000000e+00 236 152
## 193 society 82.521854 0.000000e+00 2498 3158
## 194 decides 80.557852 0.000000e+00 50 0
## 195 quite 80.189083 0.000000e+00 661 653
## 196 approach 78.458117 0.000000e+00 1023 1127
## 197 final 77.377210 0.000000e+00 219 141
## 198 clarify 75.724330 0.000000e+00 47 0
## 199 attitudes 75.288536 0.000000e+00 197 121
## 200 correction 74.113157 0.000000e+00 46 0
## 201 self 72.148753 0.000000e+00 902 985
## 202 future 70.283876 0.000000e+00 2193 2783
## 203 reality 70.281005 0.000000e+00 423 380
## 204 may 70.156787 0.000000e+00 3327 4425
## 205 processes 68.112346 1.110223e-16 537 524
## 206 questions 67.921112 2.220446e-16 468 439
## 207 question 67.020791 2.220446e-16 1649 2027
## 208 confidently 66.057306 4.440892e-16 41 0
## 209 pay 65.779541 5.551115e-16 728 774
## 210 discovering 64.446138 9.992007e-16 40 0
## 211 comprehend 64.446138 9.992007e-16 40 0
## 212 differentiated 64.446138 9.992007e-16 40 0
## 213 taken 64.215697 1.110223e-15 2145 2745
## 214 necessary 63.476994 1.665335e-15 1075 1245
## 215 broad 63.225161 1.887379e-15 305 253
## 216 acutely 62.461975 2.664535e-15 45 3
## 217 fundamental 62.237886 2.997602e-15 380 343
## 218 complications 61.223803 5.107026e-15 38 0
## 219 creations 61.223803 5.107026e-15 38 0
## 220 fully 61.190116 5.218048e-15 638 669
## 221 thing 60.977366 5.773160e-15 1401 1705
## 222 conducive 60.832867 6.217249e-15 141 80
## 223 contemplate 59.612637 1.154632e-14 37 0
## 224 convincing 59.612637 1.154632e-14 37 0
## 225 political 59.233463 1.398881e-14 2067 2659
## 226 opinion 58.292593 2.253753e-14 410 387
## 227 matter 58.241254 2.320366e-14 1503 1860
## 228 view 56.392306 5.939693e-14 1129 1345
## 229 differing 56.390307 5.939693e-14 35 0
## 230 arbitrary 56.390307 5.939693e-14 35 0
## 231 fact 56.245838 6.394885e-14 1580 1978
## 232 abled 55.852463 7.815970e-14 56 12
## 233 social 55.468360 9.492407e-14 2461 3251
## 234 must 55.270613 1.050271e-13 6099 8681
## 235 issues 55.269333 1.051381e-13 1218 1473
## 236 world 54.877217 1.283418e-13 7451 10739
## 237 ultimate 54.755903 1.364464e-13 163 108
## 238 structure 54.197230 1.812994e-13 400 383
## 239 peaceful 53.474483 2.620126e-13 564 593
## 240 corrections 53.167980 3.061995e-13 33 0
## 241 destiny 52.691062 3.903544e-13 275 235
## 242 mistakes 52.475043 4.357625e-13 172 120
## 243 ability 52.198830 5.015988e-13 391 376
## 244 make 52.152178 5.135892e-13 3495 4797
## 245 entirely 51.831058 6.048495e-13 284 247
## 246 appearing 51.556818 6.955547e-13 32 0
## 247 apprehensive 51.556818 6.955547e-13 32 0
## 248 commits 51.556818 6.955547e-13 32 0
## 249 static 51.498331 7.165379e-13 108 57
## 250 proposals 50.290186 1.326161e-12 275 239
## 251 factors 50.047844 1.500466e-12 289 256
## 252 contingent 49.945656 1.580625e-12 31 0
## 253 increasingly 49.239195 2.265743e-12 302 273
## 254 bring 48.388438 3.496203e-12 1387 1741
## 255 parts 48.375723 3.518963e-12 745 848
## 256 depended 48.334495 3.593792e-12 30 0
## 257 therefore 47.997067 4.268585e-12 2302 3065
## 258 wrong 47.815040 4.683809e-12 604 661
## 259 faith 47.139396 6.611378e-12 670 751
## 260 nevertheless 46.889301 7.511214e-12 265 233
## 261 accurately 46.723335 8.175016e-12 29 0
## 262 perception 46.271176 1.029676e-11 149 103
## 263 indirectly 45.602631 1.448464e-11 49 12
## 264 try 45.525766 1.506439e-11 879 1041
## 265 sincerely 45.161520 1.814349e-11 308 288
## 266 attribute 45.112175 1.860645e-11 28 0
## 267 right 44.668742 2.333533e-11 1669 2164
## 268 differed 43.501017 4.237388e-11 27 0
## 269 clarified 43.501017 4.237388e-11 27 0
## 270 nature 43.260268 4.792167e-11 755 879
## 271 pattern 43.103535 5.191836e-11 182 143
## 272 issue 42.698146 6.387280e-11 814 962
## 273 possible 41.923268 9.492629e-11 1495 1928
## 274 coherent 41.889859 9.656220e-11 26 0
## 275 completes 41.889859 9.656220e-11 26 0
## 276 succeed 41.801628 1.010193e-10 309 296
## 277 consensus 41.280002 1.319116e-10 325 317
## 278 accession 41.152638 1.407932e-10 43 10
## 279 suicide 40.646086 1.824523e-10 55 19
## 280 confuse 40.278701 2.201965e-10 25 0
## 281 aggravate 40.278701 2.201965e-10 25 0
## 282 determines 40.278701 2.201965e-10 25 0
## 283 harm 40.123320 2.384257e-10 127 87
## 284 confess 38.667544 5.024955e-10 24 0
## 285 chooses 38.667544 5.024955e-10 24 0
## 286 rules 38.471403 5.556188e-10 267 251
## 287 inevitable 38.247836 6.230615e-10 149 113
## 288 thinking 38.228890 6.291403e-10 887 1081
## 289 totally 37.380521 9.718799e-10 214 189
## 290 situations 37.124315 1.108330e-09 169 137
## 291 dubious 37.056388 1.147620e-09 23 0
## 292 correlation 37.056388 1.147620e-09 23 0
## 293 merits 37.053471 1.149338e-09 62 27
## 294 matters 36.701778 1.376539e-09 607 700
## 295 process 36.700610 1.377363e-09 1570 2066
## 296 adapt 36.689262 1.385404e-09 103 66
## 297 perfectly 36.667530 1.400935e-09 94 57
## 298 problem 36.627389 1.430082e-09 1591 2097
## 299 trust 36.331025 1.664923e-09 458 501
## 300 might 36.182894 1.796405e-09 1026 1286
## 301 stated 36.120449 1.854901e-09 191 164
## 302 something 36.035385 1.937666e-09 1834 2456
## 303 say 35.731230 2.265038e-09 2709 3755
## 304 compels 35.445233 2.623236e-09 22 0
## 305 bet 35.445233 2.623236e-09 22 0
## 306 analytical 35.445233 2.623236e-09 22 0
## 307 closure 35.445233 2.623236e-09 22 0
## 308 fate 34.239998 4.871730e-09 111 77
## 309 damage 33.854351 5.939611e-09 148 118
## 310 abnormal 33.834079 6.001826e-09 21 0
## 311 diagnosis 33.834079 6.001826e-09 21 0
## 312 rather 33.800760 6.105500e-09 717 862
## 313 basic 33.584084 6.824889e-09 999 1260
## 314 drawn 33.179204 8.404457e-09 211 193
## 315 independent 33.088158 8.807351e-09 453 504
## 316 upon 32.902317 9.690769e-09 1010 1279
## 317 criteria 32.516496 1.181851e-08 50 20
## 318 logical 32.476990 1.206122e-08 66 34
## 319 never 32.453568 1.220746e-08 1109 1423
## 320 distracted 32.222925 1.374593e-08 20 0
## 321 discern 32.222925 1.374593e-08 20 0
## 322 extinct 32.222925 1.374593e-08 20 0
## 323 qualitative 31.910094 1.614759e-08 74 42
## 324 sometimes 31.896844 1.625811e-08 942 1187
## 325 opportunities 31.755108 1.748887e-08 1057 1352
## 326 views 31.546250 1.947461e-08 415 458
## 327 scenario 31.233177 2.288219e-08 134 106
## 328 policies 30.954189 2.641912e-08 860 1075
## 329 principles 30.830418 2.815893e-08 490 561
## 330 confusing 30.611772 3.151757e-08 19 0
## 331 anytime 30.611772 3.151757e-08 19 0
## 332 agree 30.597494 3.175036e-08 360 388
## 333 realities 30.362246 3.584369e-08 132 105
## 334 positive 30.354507 3.598698e-08 431 483
## 335 seems 30.352294 3.602806e-08 345 369
## 336 system 30.065090 4.177848e-08 2194 3032
## 337 economic 30.042466 4.226876e-08 3762 5396
## 338 democratic 29.769431 4.866046e-08 718 880
## 339 conviction 29.748346 4.919259e-08 175 156
## 340 aspects 29.447761 5.744436e-08 390 431
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## 777 rejected 8.347964 3.861197e-03 60 57
## 778 stable 8.346360 3.864607e-03 175 210
## 779 precisely 8.315574 3.930659e-03 86 90
## 780 calmly 8.314566 3.932840e-03 15 7
## 781 interplay 8.314566 3.932840e-03 15 7
## 782 robots 8.314566 3.932840e-03 15 7
## 783 pact 8.293122 3.979552e-03 45 39
## 784 resolute 8.277668 4.013565e-03 30 22
## 785 imbalances 8.263182 4.045715e-03 75 76
## 786 mandate 8.213930 4.157001e-03 95 102
## 787 impartial 8.213838 4.157212e-03 29 21
## 788 contained 8.211171 4.163327e-03 63 61
## 789 argue 8.188266 4.216220e-03 44 38
## 790 separate 8.180790 4.233632e-03 201 247
## 791 ignores 8.174004 4.249500e-03 16 8
## 792 reciprocal 8.174004 4.249500e-03 16 8
## 793 spoil 8.174004 4.249500e-03 16 8
## 794 evolves 8.174004 4.249500e-03 16 8
## 795 cautious 8.154349 4.295801e-03 28 20
## 796 mans 8.154349 4.295801e-03 28 20
## 797 speedily 8.150483 4.304969e-03 49 44
## 798 ecosystem 8.150483 4.304969e-03 49 44
## 799 collective 8.139536 4.331037e-03 306 397
## 800 bureaucracy 8.134806 4.342349e-03 81 84
## 801 obvious 8.131329 4.350684e-03 198 243
## 802 trends 8.129331 4.355482e-03 130 149
## 803 emissions 8.119014 4.380338e-03 58 55
## 804 sustained 8.116582 4.386219e-03 246 311
## 805 productive 8.112433 4.396269e-03 283 364
## 806 suggestions 8.103816 4.417218e-03 319 416
## 807 individuals 8.092935 4.443815e-03 269 344
## 808 iraq 8.083963 4.465867e-03 43 37
## 809 timing 8.072900 4.493215e-03 17 9
## 810 cordiality 8.072900 4.493215e-03 17 9
## 811 stems 8.072900 4.493215e-03 17 9
## 812 though 8.069631 4.501327e-03 581 802
## 813 possession 8.058415 4.529279e-03 53 49
## 814 human 8.054808 4.538305e-03 1867 2763
## 815 devoting 8.050760 4.548457e-03 26 18
## 816 convictions 8.050760 4.548457e-03 26 18
## 817 conform 8.050760 4.548457e-03 26 18
## 818 treaty 8.047973 4.555460e-03 159 189
## 819 controversial 8.008125 4.656792e-03 25 17
## 820 people 7.990754 4.701683e-03 12287 19162
## 821 reviewing 7.972818 4.748495e-03 24 16
## 822 interference 7.963916 4.771907e-03 134 155
## 823 dogmatic 7.957498 4.788857e-03 19 11
## 824 insignificant 7.957498 4.788857e-03 19 11
## 825 determinant 7.957498 4.788857e-03 19 11
## 826 wealth 7.952651 4.801699e-03 348 459
## 827 sorted 7.932350 4.855869e-03 20 12
## 828 achievable 7.932350 4.855869e-03 20 12
## 829 unproductive 7.929074 4.864669e-03 22 14
## 830 objectively 7.923797 4.878877e-03 21 13
## 831 inf 7.923797 4.878877e-03 21 13
## 832 essentially 7.850463 5.080768e-03 116 131
## 833 antagonisms 7.791939 5.247986e-03 11 4
## 834 orthodox 7.791939 5.247986e-03 11 4
## 835 minimised 7.791939 5.247986e-03 11 4
## 836 painfully 7.791939 5.247986e-03 11 4
## 837 achieve 7.780229 5.282109e-03 821 1165
## 838 instability 7.775993 5.294511e-03 55 52
## 839 prevailing 7.766133 5.323488e-03 67 67
## 840 suit 7.749862 5.371663e-03 59 57
## 841 segments 7.748221 5.376549e-03 63 62
## 842 investor 7.731184 5.427516e-03 74 76
## 848 sick 7.718886 5.464611e-03 103 114
## 849 indivisible 7.713591 5.480661e-03 45 40
## 850 vague 7.679584 5.584905e-03 32 25
## 851 warming 7.627110 5.749737e-03 80 84
## 852 swept 7.605175 5.820103e-03 44 39
## 853 exist 7.598828 5.840624e-03 233 295
## 854 submit 7.574268 5.920741e-03 133 155
## 855 sweeping 7.572404 5.926869e-03 38 32
## 856 priorities 7.563857 5.955040e-03 187 230
## 857 merely 7.504388 6.154886e-03 596 829
## 858 phraseology 7.501264 6.165572e-03 12 5
## 859 diffused 7.501264 6.165572e-03 12 5
## 860 adopts 7.501264 6.165572e-03 12 5
## 861 reciprocity 7.501264 6.165572e-03 12 5
## 862 umpire 7.501264 6.165572e-03 12 5
## 863 jayapur 7.501264 6.165572e-03 12 5
## 864 assert 7.496879 6.180601e-03 48 44
## 865 keep 7.493406 6.192531e-03 897 1283
## 866 npt 7.472493 6.264876e-03 37 31
## 867 remains 7.465685 6.288612e-03 391 525
## 868 synonymous 7.438840 6.383102e-03 29 22
## 869 type 7.423686 6.437080e-03 297 388
## 870 adhere 7.389481 6.560642e-03 42 37
## 871 simplicity 7.385053 6.576812e-03 47 43
## 872 doctor 7.367472 6.641423e-03 131 153
## 873 slight 7.364415 6.652722e-03 28 21
## 874 life 7.332873 6.770476e-03 2937 4437
## 875 regimented 7.296186 6.910115e-03 13 6
## 876 agenda 7.294413 6.916936e-03 302 396
## 877 coercion 7.293522 6.920369e-03 27 20
## 878 dynamics 7.293522 6.920369e-03 27 20
## 879 assess 7.288904 6.938180e-03 55 53
## 880 assets 7.287990 6.941709e-03 105 118
## 881 fair 7.278136 6.979892e-03 304 399
## 882 reform 7.277146 6.983740e-03 367 491
## 883 proposition 7.273413 6.998268e-03 46 42
## 884 require 7.246525 7.103835e-03 308 405
## 895 entangled 7.226630 7.182992e-03 26 19
## 896 interfere 7.218501 7.215596e-03 83 89
## 897 conditioned 7.205592 7.267677e-03 50 47
## 898 commonwealth 7.196262 7.305558e-03 200 250
## 899 societies 7.186896 7.343791e-03 316 417
## 900 automatically 7.182019 7.363778e-03 89 97
## 901 discontent 7.178954 7.376369e-03 34 28
## 902 normal 7.178853 7.376784e-03 195 243
## 903 verdict 7.175508 7.390550e-03 40 35
## 904 worse 7.167797 7.422388e-03 98 109
## 905 systemic 7.164298 7.436878e-03 25 18
## 906 liking 7.152249 7.487005e-03 14 7
## 907 ceasefire 7.152249 7.487005e-03 14 7
## 908 pressurised 7.152249 7.487005e-03 14 7
## 909 lies 7.108573 7.671614e-03 304 400
## 910 attached 7.105189 7.686111e-03 82 88
## 911 dissatisfaction 7.091744 7.743985e-03 49 46
## 915 bamboo 7.070640 7.835727e-03 61 61
## 916 disturbance 7.056122 7.899482e-03 23 16
## 917 compromising 7.056122 7.899482e-03 23 16
## 918 advertising 7.056122 7.899482e-03 23 16
## 919 irreversible 7.056122 7.899482e-03 23 16
## 920 overwhelm 7.053503 7.911039e-03 15 8
## 921 jurists 7.053503 7.911039e-03 15 8
## 922 intrinsically 7.053503 7.911039e-03 15 8
## 923 country 7.029756 8.016625e-03 8912 13852
## 924 developments 7.023161 8.046206e-03 283 370
## 925 identical 7.012059 8.096250e-03 22 15
## 926 effect 7.002657 8.138881e-03 307 405
## 927 varying 6.989493 8.198959e-03 32 26
## 928 caution 6.989493 8.198959e-03 32 26
## 929 marches 6.989103 8.200746e-03 16 9
## 930 selfie 6.989103 8.200746e-03 16 9
## 931 reaffirmed 6.976208 8.260043e-03 21 14
## 932 critically 6.963548 8.318689e-03 38 33
## 933 quick 6.953462 8.365721e-03 99 111
## 934 dictate 6.951415 8.375296e-03 17 10
## 935 tentative 6.951415 8.375296e-03 17 10
## 936 greenhouse 6.951415 8.375296e-03 17 10
## 937 terminology 6.950062 8.381633e-03 20 13
## 938 bold 6.950029 8.381786e-03 90 99
## 939 weaken 6.948148 8.390605e-03 96 107
## 940 insecurity 6.939745 8.430111e-03 43 39
## 941 implied 6.935498 8.450152e-03 19 12
## 942 deforestation 6.935498 8.450152e-03 19 12
## 943 grit 6.935498 8.450152e-03 19 12
## 944 glaciers 6.934914 8.452914e-03 18 11
## 945 pollution 6.924757 8.501055e-03 124 145
## 946 religious 6.921095 8.518482e-03 362 486
## 947 drift 6.897032 8.633899e-03 31 25
## 948 permanent 6.867145 8.779481e-03 118 137
## 949 undergoing 6.858432 8.822391e-03 37 32
## 950 mental 6.841116 8.908316e-03 115 133
## 951 apprehension 6.828992 8.968987e-03 42 38
## 952 complexities 6.806308 9.083640e-03 30 24
## 953 reactions 6.753961 9.353979e-03 36 31
## 954 evolving 6.751505 9.366864e-03 125 147
## 955 pursuing 6.746045 9.395572e-03 100 113
## 956 progress 6.728721 9.487259e-03 1742 2590
## 957 whenever 6.718672 9.540866e-03 357 480
## 958 questioning 6.717522 9.547020e-03 29 23
## 959 herbal 6.717522 9.547020e-03 29 23
## 960 realistic 6.706408 9.606706e-03 54 53
## 961 maintain 6.692311 9.682958e-03 252 327
## 962 making 6.667166 9.820520e-03 1207 1766
## 963 understanding 6.657801 9.872268e-03 637 898
## 967 attempt 6.640201 9.970267e-03 267 349
## 968 limit 6.635822 9.994805e-03 137 164
## 969 demanded 6.608301 1.015045e-02 40 36
## 970 disruption 6.608301 1.015045e-02 40 36
## 971 appraisal 6.608301 1.015045e-02 40 36
## 972 sections 6.603123 1.018001e-02 636 897
## 973 reason 6.563565 1.040877e-02 653 923
## 974 longer 6.561120 1.042307e-02 342 459
## 975 innate 6.546732 1.050770e-02 27 21
## 976 affirm 6.546732 1.050770e-02 27 21
## 977 probity 6.546732 1.050770e-02 27 21
## 978 judges 6.522450 1.065212e-02 80 87
## 979 necessarily 6.517042 1.068456e-02 161 198
## 986 socially 6.501793 1.077658e-02 109 126
## 987 order 6.494638 1.082003e-02 981 1422
## 988 agrarian 6.491065 1.084180e-02 56 56
## 989 need 6.488151 1.085959e-02 3457 5268
## 990 periods 6.482285 1.089549e-02 70 74
## 991 ingenuity 6.465318 1.100000e-02 26 20
## 992 unleash 6.465318 1.100000e-02 26 20
## 993 subjects 6.465064 1.100157e-02 148 180
## 994 ends 6.456763 1.105308e-02 130 155
## 995 everybody 6.439995 1.115789e-02 260 340
## 996 command 6.396292 1.143590e-02 88 98
## 997 doctrines 6.388847 1.148396e-02 38 34
## 998 governed 6.375150 1.157293e-02 55 55
## 999 theories 6.359251 1.167708e-02 51 50
## 1000 hence 6.356506 1.169516e-02 402 549
## 1001 reconcile 6.343834 1.177900e-02 32 27
## 1002 shadow 6.336788 1.182588e-02 72 77
## 1003 naturally 6.318160 1.195076e-02 254 332
## 1004 external 6.317459 1.195548e-02 300 399
## 1005 tobacco 6.312346 1.199001e-02 24 18
## 1006 managing 6.298235 1.208582e-02 78 85
## 1007 expert 6.288949 1.214930e-02 81 89
## 1008 disapproval 6.279072 1.221720e-02 11 5
## 1009 crime 6.277126 1.223063e-02 141 171
## 1010 pressure 6.270656 1.227537e-02 240 312
## 1011 difficulty 6.264919 1.231518e-02 215 276
## 1012 minds 6.248724 1.242828e-02 472 654
## 1022 spirits 6.241775 1.247714e-02 23 17
## 1023 passionately 6.241775 1.247714e-02 23 17
## 1024 pretend 6.241775 1.247714e-02 23 17
## 1025 exporter 6.241775 1.247714e-02 23 17
## 1026 polity 6.232050 1.254584e-02 138 167
## 1027 requiring 6.175976 1.294965e-02 22 16
## 1028 treating 6.175976 1.294965e-02 22 16
## 1029 helplessness 6.175976 1.294965e-02 22 16
## 1030 underlined 6.175976 1.294965e-02 22 16
## 1031 forces 6.172453 1.297546e-02 934 1354
## 1032 sovereignty 6.139220 1.322157e-02 100 115
## 1033 squabbles 6.130495 1.328697e-02 12 6
## 1034 diverting 6.130495 1.328697e-02 12 6
## 1035 meanings 6.130495 1.328697e-02 12 6
## 1036 realistically 6.130495 1.328697e-02 12 6
## 1037 neighbourly 6.130495 1.328697e-02 12 6
## 1038 icrier 6.130495 1.328697e-02 12 6
## 1039 layers 6.115746 1.339829e-02 21 15
## 1040 fertility 6.115746 1.339829e-02 21 15
## 1041 wing 6.074405 1.371545e-02 76 83
## 1042 solely 6.071947 1.373455e-02 40 37
## 1043 sensible 6.062066 1.381160e-02 20 14
## 1044 dislocation 6.062066 1.381160e-02 20 14
## 1045 reflect 6.050092 1.390557e-02 194 247
## 1046 favour 6.044763 1.394760e-02 168 210
## 1047 doubtful 6.034815 1.402642e-02 13 7
## 1048 conspicuous 6.034815 1.402642e-02 13 7
## 1049 privacy 6.034815 1.402642e-02 13 7
## 1050 tb 6.023869 1.411367e-02 44 42
## 1051 receptive 6.016166 1.417541e-02 19 13
## 1052 reckon 6.016166 1.417541e-02 19 13
## 1053 adversities 5.979600 1.447228e-02 18 12
## 1054 objectivity 5.979600 1.447228e-02 18 12
## 1055 transcend 5.979600 1.447228e-02 18 12
## 1056 abiding 5.978222 1.448359e-02 90 102
## 1057 resigned 5.977607 1.448865e-02 14 8
## 1058 piecemeal 5.977607 1.448865e-02 14 8
## 1059 hurdles 5.958191 1.464907e-02 84 94
## 1060 doubted 5.954371 1.468084e-02 17 11
## 1061 inhibited 5.954371 1.468084e-02 17 11
## 1062 resolutely 5.954371 1.468084e-02 17 11
## 1063 shape 5.953081 1.469158e-02 236 308
## 1064 divyang 5.950502 1.471309e-02 28 23
## 1065 confirm 5.949252 1.472353e-02 15 9
## 1066 pros 5.949252 1.472353e-02 15 9
## 1067 doctrinaire 5.943092 1.477508e-02 16 10bon<-c()
bon2<-tstatkeyv2.1subset
for(j in 1:length(v2.1)){
bon <- c(bon,which(tstatkeyv2.1subset[,1]==v2.1[j]))
}
bon<-sort(bon, decreasing=TRUE)
bon2<-bon2[-c(bon),]
#Method: Below is an example of the the calculation of the hypergeometric probability distribution of single features, which was used to identify overrepresented and underrepresented features among Prime Ministers (see the discussion section of the article)
plotspecif <- specificities.distribution.plot(7799, 22957, 2199993, 8029517)
#plotspecif <- specificities.distribution.plot(x, F, t, T)
#x: observed number of A words
#F: total number of A
#t: size of part
#T: size of corpus