Extrem(ist +) Fightin’ Words

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load libraries

### https://cran.r-project.org/web/packages/udpipe/vignettes/udpipe-usecase-postagging-lemmatisation.html
library(udpipe)
ud_model <- udpipe_download_model(language = "english")

library(tidyverse)
library(tidyr)
library(dplyr)
library(ggplot2)
library(ggrepel)
library(knitr)
library(tm)
library(quanteda)
library(lattice)
library(latticeExtra)
library(plotly)
library(pdp)
library(patchwork)

load the FW functions

### CODE DIRECTLY FROM: https://burtmonroe.github.io/TextAsDataCourse/Tutorials/TADA-FightinWords.nb.html#

fwgroups <- function(dtm, groups, pair = NULL, weights = rep(1,nrow(dtm)), k.prior = .1) {
  
  weights[is.na(weights)] <- 0
  
  weights <- weights/mean(weights)
  
  zero.doc <- rowSums(dtm)==0 | weights==0
  zero.term <- colSums(dtm[!zero.doc,])==0
  
  dtm.nz <- apply(dtm[!zero.doc,!zero.term],2,"*", weights[!zero.doc])
  
  g.prior <- tcrossprod(rowSums(dtm.nz),colSums(dtm.nz))/sum(dtm.nz)
  
  # 
  
  g.posterior <- as.matrix(dtm.nz + k.prior*g.prior)
  
  groups <- groups[!zero.doc]
  groups <- droplevels(groups)
  
  g.adtm <- as.matrix(aggregate(x=g.posterior,by=list(groups=groups),FUN=sum)[,-1])
  rownames(g.adtm) <- levels(groups)
  
  g.ladtm <- log(g.adtm)
  
  g.delta <- t(scale( t(scale(g.ladtm, center=T, scale=F)), center=T, scale=F))
  
  g.adtm_w <- -sweep(g.adtm,1,rowSums(g.adtm)) # terms not w spoken by k
  g.adtm_k <- -sweep(g.adtm,2,colSums(g.adtm)) # w spoken by groups other than k
  g.adtm_kw <- sum(g.adtm) - g.adtm_w - g.adtm_k - g.adtm # total terms not w or k 
  
  g.se <- sqrt(1/g.adtm + 1/g.adtm_w + 1/g.adtm_k + 1/g.adtm_kw)
  
  g.zeta <- g.delta/g.se
  
  g.counts <- as.matrix(aggregate(x=dtm.nz, by = list(groups=groups), FUN=sum)[,-1])
  
  if (!is.null(pair)) {
    pr.delta <- t(scale( t(scale(g.ladtm[pair,], center = T, scale =F)), center=T, scale=F))
    pr.adtm_w <- -sweep(g.adtm[pair,],1,rowSums(g.adtm[pair,]))
    pr.adtm_k <- -sweep(g.adtm[pair,],2,colSums(g.adtm[pair,])) # w spoken by groups other than k
    pr.adtm_kw <- sum(g.adtm[pair,]) - pr.adtm_w - pr.adtm_k - g.adtm[pair,] # total terms not w or k
    pr.se <- sqrt(1/g.adtm[pair,] + 1/pr.adtm_w + 1/pr.adtm_k + 1/pr.adtm_kw)
    pr.zeta <- pr.delta/pr.se
    
    return(list(zeta=pr.zeta[1,], delta=pr.delta[1,],se=pr.se[1,], counts = colSums(dtm.nz), acounts = colSums(g.adtm)))
  } else {
    return(list(zeta=g.zeta,delta=g.delta,se=g.se,counts=g.counts,acounts=g.adtm))
  }
}

############## FIGHTIN' WORDS PLOTTING FUNCTION

# helper function
makeTransparent<-function(someColor, alpha=100)
{
  newColor<-col2rgb(someColor)
  apply(newColor, 2, function(curcoldata){rgb(red=curcoldata[1], green=curcoldata[2],
                                              blue=curcoldata[3],alpha=alpha, maxColorValue=255)})
}

fw.ggplot.groups <- function(fw.ch, groups.use = as.factor(rownames(fw.ch$zeta)), max.words = 50, max.countrank = 400, colorpalette=rep("black",length(groups.use)), sizescale=2, title="Comparison of Terms by Groups", subtitle = "", caption = "Group-specific terms are ordered by Fightin' Words statistic (Monroe, et al. 2008)") {
  if (is.null(dim(fw.ch$zeta))) {## two-group fw object consists of vectors, not matrices
    zetarankmat <- cbind(rank(-fw.ch$zeta),rank(fw.ch$zeta))
    colnames(zetarankmat) <- groups.use
    countrank <- rank(-(fw.ch$counts))
  } else {
    zetarankmat <- apply(-fw.ch$zeta[groups.use,],1,rank)
    countrank <- rank(-colSums(fw.ch$counts))
  }
  wideplotmat <- as_tibble(cbind(zetarankmat,countrank=countrank))
  wideplotmat$term=names(countrank)
  #rankplot <- gather(wideplotmat, party, zetarank, 1:ncol(zetarankmat))
  rankplot <- gather(wideplotmat, groups.use, zetarank, 1:ncol(zetarankmat))
  rankplot$plotsize <- sizescale*(50/(rankplot$zetarank))^(1/4)
  rankplot <- rankplot[rankplot$zetarank < max.words + 1 & rankplot$countrank<max.countrank+1,]
  rankplot$groups.use <- factor(rankplot$groups.use,levels=groups.use)
  
  p <- ggplot(rankplot, aes((nrow(rankplot)-countrank)^1, -(zetarank^1), colour=groups.use)) + 
    geom_point(show.legend=F,size=sizescale/2) + 
    theme_classic() +
    theme(axis.ticks=element_blank(), axis.text=element_blank() ) +
    ylim(-max.words,40) +
    facet_grid(groups.use ~ .) +
    geom_text_repel(aes(label = term), size = rankplot$plotsize, point.padding=.05,
                    box.padding = unit(0.20, "lines"), show.legend=F, max.overlaps = Inf) +
    scale_colour_manual(values = alpha(colorpalette, .7)) + 
#    labs(x="Terms used more frequently overall →", y="Terms used more frequently by group →",  title=title, subtitle=subtitle , caption = caption) 
    labs(x=paste("Terms used more frequently overall -->"), y=paste("Terms used more frequently by group -->"),  title=title, subtitle=subtitle , caption = caption) 
  
}

options(ggrepel.max.overlaps = Inf)

fw.keys <- function(fw.ch,n.keys=10) {
  n.groups <- nrow(fw.ch$zeta)
  keys <- matrix("",n.keys,n.groups)
  colnames(keys) <- rownames(fw.ch$zeta)
  
  for (g in 1:n.groups) {
    keys[,g] <- names(sort(fw.ch$zeta[g,],dec=T)[1:n.keys])
  }
  keys
}

Compare Associated Press 1994-2010: Before and After “extremist” and other query terms

Query search: (activi* | ahbash | akromiya | anjem | ansharut | anticapital* | antidemocr* | antiestablish* | antifa | antigovern* | antimilitar* | antimonarch* | antipatri* | antireli* | antisem* | antisocia* | antisyst* | apost* | atharis | athei* | atheists | außerparlamentari* | authoritar* | bagau* | bigots | bplf | bukhari* | capitulatio* | conspirato* | counterj* | cybercalip* | damigo | dawro* | demon* | deradicaliza* | deviatio* | diqqi | dissid* | djamaat | dotbus* | ecofas* | espou* | ethnonationa* | extrem* | facists | fadaia* | fanat* | fasci* | fetö | fightdem* | freedo* | fundamental* | fuqra | gafatar | gamerga* | gemidzii | ghuluww | globali* | gramsc* | gülen* | hacktiv* | haquna | hardline | harkatul | hatemon* | heimwe* | hezbol* | hinduph* | hindutva | hizbut | hojja* | ideolog* | incitem* | inciters | insurr* | intacti* | islam4uk | islam* | jaljalat | jbakc | jihadi* | jmjb | jrtn | judai* | juhayman | jundu* | kadiza* | kahanism | kahanist | karram* | kaysa* | khalis* | khatmia | khawarij | khomein* | koutla | leftist | leftists | leftwing | liberatio* | madkha* | madkhal* | maimonid* | manosp* | mauras* | mcln | militan* | millatu | monarc* | mudja* | muhaji* | mujahid* | murab* | muttahi* | najjadah | nationali* | neofas* | neona* | opantish | oppositi* | paleolibertar* | paramili* | parliamenta* | pegida | populist | principa* | profe* | prowar | putinist | qadari | quranism | quranist | qutbism | qutbist | qutbists | racis* | radica* | reactioni* | reformis* | reichsbürgerbewe* | rightist | rightw* | rofiq | russoph* | sabireen | sadda* | salaf* | sayaff | scriptura* | secula* | separationi* | sharia4hol* | sikrikim | split* | squadism | strasse* | subver* | suidlan* | sukarn* | suprema* | supremac* | sympathi* | table* | tabliq | takfir | takfir* | takfi* |terror* | theoc* | titoite | triba* | trots* | trotsk* | ukrainoph* | ultraconserva* | ultralib* | ultranationa* | ultrar* | uscmo | wahabbi | wahab* | wahha* | xenoph* | yulde* | zinovie*)

Load and clean the data

• to string & lower text
• pivot to long format
• apply text_cleaner to one column “context.text”

text_cleaner<-function(corpus){
  tempcorpus<-Corpus(VectorSource(corpus))
  tempcorpus<-tm_map(tempcorpus,
                    removePunctuation)
  tempcorpus<-tm_map(tempcorpus,
                    stripWhitespace)
  tempcorpus<-tm_map(tempcorpus,
                    removeNumbers)
  tempcorpus<-tm_map(tempcorpus,
                     removeWords, stopwords("english"))
  tempcorpus<-tm_map(tempcorpus, 
                    stemDocument)
  return(tempcorpus)
}

Calculate FW.

e <- dfm(extremecorpus$content)
message(dim(e))
head(e)

Document-feature matrix of: 6 documents, 15,213 features (100.0% sparse).
       features
docs    us struggl uptick american plot attack past month last week
  text1  1       1      1        1    1      0    0     0    0    0
  text2  0       0      0        0    0      1    1     1    1    1
  text3  0       0      0        0    0      0    0     0    0    0
  text4  0       0      0        0    0      0    0     0    0    0
  text5  0       0      0        0    0      0    0     0    0    0
  text6  0       0      0        0    0      0    0     0    0    0
[ reached max_nfeat ... 15,203 more features ]

#############################################

e <- dfm_select(e, pattern = stopwords("english"), selection = "remove")
e <- dfm_select(e, min_nchar = 2)
e <- dfm_trim(e, min_termfreq = 4, min_docfreq = .05, verbose=TRUE)

#dim(e)
# sparsity(e)

#############################################

extrem_dtm <- convert(e, to='data.frame')
extrem_dtm <- extrem_dtm[-c(1)]
w <- which( sapply(extrem_dtm, class ) == 'character' )

#############################################

fw.extrem <- fwgroups(extrem_dtm, groups=extrem_NYT.dfm.long$Context)

rm(extrem_dtm)

Get and show the top words per group by zeta.

fwkeys.extrem <- fw.keys(fw.extrem, n.keys=20)
cols <- rev(colnames(fwkeys.extrem))
fwkeys.extrem <- fwkeys.extrem[,cols]
kable(fwkeys.extrem)

Context.before	Context.after
islamic	group
palestinian	hussein
war	attack
iraqi	parti
suspect	elect
crack	leader
jemaah	jihad
crackdown	milit
main	movement
alleg	said
support	guerrilla
evid	network
albanian	regim
prodemocraci	armi
sept	organ
megawati	politician
muslim	outsid
prevent	univers
kosovo	bomb
battl	rocket

NA

Plot: Before in Blue, After in Red

p.fw.extrem <- fw.ggplot.groups(fw.extrem,sizescale=4,max.words=200,
                                max.countrank=400,colorpalette=c("red","blue"),
                                title = 'Comparison of Terms Before and After Query Word')
p.fw.extrem

Calculate by query item/search term

extrem_NYT.dfm.long$Query.item <- as.factor(extrem_NYT.dfm.long$Query.item)

top_n <-as.data.frame(sort(table(extrem_NYT.dfm.long$Query.item), decreasing = TRUE)[1:5]) 
message(dim(top_n))
colnames(top_n) <- c('term', 'Freq')
message(top_n)

extrem_dtm_topn <- convert(e, to='data.frame')
extrem_dtm_topn$Number.of.hit <- extrem_NYT.dfm.long$Number.of.hit

topn_terms <- extrem_NYT.dfm.long %>%
      filter(Query.item %in% top_n$term)

extrem_dtm_topn_keep <- extrem_dtm_topn %>% 
    filter(Number.of.hit %in% topn_terms$Number.of.hit)

r <- sum(length(extrem_dtm_topn_keep))

extrem_dtm_topn_keep <- extrem_dtm_topn_keep[-c(1, r)]

fw.query_item <- fwgroups(extrem_dtm_topn_keep,groups = topn_terms$Query.item)
fwkeys.query_item <- fw.keys(fw.query_item, n.keys=15)
kable(fwkeys.query_item)

Islamic	militants	opposition	Saddam	terrorist
milit	islamic	parti	hussein	attack
hama	palestinian	democrat	iraqi	organ
jihad	kill	parliament	iraq	sept
group	israeli	leader	presid	unite
movement	suspect	elect	regim	state
extremist	gaza	vote	saddam	activ
front	israel	opposit	baghdad	alqaida
republ	attack	lawmak	un	act
insurg	kashmir	polit	kuwait	network
radic	taliban	main	war	group
law	arafat	candid	captur	suspect
fundamentalist	forc	coalit	oust	laden
revolut	pakistani	conserv	weapon	bin
somalia	fire	allianc	bush	connect
hardlin	pakistan	protest	us	financ

rm(r)
rm(extrem_dtm_topn)

########################################################

p.fw.query_item <- fw.ggplot.groups(fw.query_item,sizescale=3.2,max.words=150,max.countrank=400,
                                    colorpalette=c("darkgreen","darkgreen","darkgreen","darkgreen","darkgreen"),
                                    title = 'Comparison of Terms by Overall Top Terms')
p.fw.query_item

extrem_dtm_topn <- convert(e, to='data.frame')
extrem_dtm_topn$Number.of.hit <- extrem_NYT.dfm.long$Number.of.hit
extrem_dtm_topn$Context <- extrem_NYT.dfm.long$Context

topn_terms <- extrem_NYT.dfm.long %>%
      filter(Query.item %in% top_n$term)

extrem_dtm_topn_keep <- extrem_dtm_topn %>% 
    filter(Number.of.hit %in% topn_terms$Number.of.hit)

extrem_dtm_topn_keep_before <- extrem_dtm_topn_keep[grep("before",extrem_dtm_topn_keep$Context),]
extrem_dtm_topn_keep_after <- extrem_dtm_topn_keep[grep("after", extrem_dtm_topn_keep$Context),]

rr <-dim(topn_terms)[1]
r <- sum(length(extrem_dtm_topn_keep_before))
topn_terms_before <- topn_terms[seq(1,rr,2),]
topn_terms_after <- topn_terms[seq(2,rr,2),]

extrem_dtm_topn_keep_before <- extrem_dtm_topn_keep_before[-c(1, r-1, r)]
extrem_dtm_topn_keep_after <- extrem_dtm_topn_keep_after[-c(1, r-1, r)]

rm(rr)
rm(r)
rm(extrem_dtm_topn)

#############################################
fw.query_item_before <- fwgroups(extrem_dtm_topn_keep_before,groups = topn_terms_before$Query.item)
fwkeys.query_item_before <- fw.keys(fw.query_item_before, n.keys=15)
kable(fwkeys.query_item_before, caption = "Top 15 Words for Query Term: BEFORE")

Top 15 Words for Query Term: BEFORE

Islamic	militants	opposition	Saddam	terrorist
milit	islamic	main	iraqi	sept
strict	palestinian	elect	presid	alqaida
radic	kill	polit	iraq	unite
somalia	suspect	parliament	war	bin
hama	taliban	opposit	saddam	laden
organiz	attack	parti	oust	involv
secular	israeli	minist	baghdad	suspect
hardlin	muslim	vote	fall	link
iran	gaza	democrat	bush	state
turkey	armi	prime	hussein	consid
extremist	troop	strong	captur	connect
malaysia	israel	percent	regim	appar
suprem	pakistan	despit	usled	osama
gaza	forc	poll	former	charg
fundamentalist	arafat	voic	death	list

p.fw.query_item_before <- fw.ggplot.groups(fw.query_item_before,sizescale=2,max.words=150,max.countrank=400,
                                           colorpalette = c('blue','blue','blue', 'blue','blue'),
                                           title = 'Comparison of Terms by Overall Top Terms: BEFORE')
p.fw.query_item_before

#############################################
fw.query_item_after <- fwgroups(extrem_dtm_topn_keep_after,groups = topn_terms_after$Query.item)
fwkeys.query_item_after <- fw.keys(fw.query_item_after, n.keys=15)
kable(fwkeys.query_item_after, caption = "Top 15 Words for Query Term: AFTER")

Top 15 Words for Query Term: AFTER

Islamic	militants	opposition	Saddam	terrorist
milit	israeli	parti	hussein	attack
group	israel	leader	regim	organ
jihad	kashmir	democrat	iraq	unite
hama	gaza	vote	iraqi	state
movement	palestinian	parliament	un	activ
law	kill	lawmak	weapon	group
insurg	attack	protest	saddam	act
extremist	region	candid	son	network
front	southern	politician	captur	bomb
revolut	fire	coalit	kuwait	suspect
republ	rocket	elect	mass	threat
islamic	pakistani	social	baghdad	financ
court	fight	conserv	us	sept
fundamentalist	border	win	resolut	link
radic	area	opposit	palac	cell

p.fw.query_item_after <- fw.ggplot.groups(fw.query_item_after,sizescale=2,max.words=150,max.countrank=400,
                                           colorpalette = c('red', 'red','red','red','red'),
                                          title = 'Comparison of Terms by Overall Top Terms: AFTER')
p.fw.query_item_after

NA
NA

Calculate Parts of speech by before and after

Calculate FW and keys

ud_model <- udpipe_load_model(ud_model$file_model)

txt <-as.character(extrem_NYT.dfm.long$context.text)

x_udp <- udpipe_annotate(ud_model, x = txt, doc_id = seq_along(txt))
x <- as.data.frame(x_udp)

x$doc_id <-as.integer(x$doc_id)

x_odd.before <- x[x$doc_id %% 2 == 1,]
x_even.after <-x[x$doc_id %% 2 == 0, ]

A few barchart functions

## UNIVERSAL PoS
UPOS_barchart <- function(df1, df2){
  stats1 <- txt_freq(df1$upos)
  stats1$key <- factor(stats1$key, levels = rev(stats1$key))
  
  stats2 <- txt_freq(df2$upos)
  stats2$key <- factor(stats2$key, levels = rev(stats2$key))
  
  c(barchart(key ~ freq, data = stats1, col = "cadetblue", 
        main = "UPOS (Universal Parts of Speech)\n frequency of occurrence: BEFORE vs AFTER", 
         xlab = "Freq"), 
    barchart(key ~ freq, data = stats2, col =  'skyblue',
         xlab = "Freq"))
}



## NOUNS
NOUNS_barchart <- function(df1, df2){
  
  stats1 <- subset(df1, upos %in% c("NOUN")) 
  stats1 <- txt_freq(stats1$token)
  stats1$key <- factor(stats1$key, levels = rev(stats1$key))
  
  stats2 <- subset(df2, upos %in% c("NOUN")) 
  stats2 <- txt_freq(stats2$token)
  stats2$key <- factor(stats2$key, levels = rev(stats2$key))
  
  c(barchart(key ~ freq, data = head(stats1, 20), col = "cadetblue", 
           main = "Most occurring nouns: BEFORE vs AFTER", xlab = "Freq"),
      barchart(key ~ freq, data = head(stats2, 20), col = "skyblue", 
            xlab = "Freq"))
}

## ADJECTIVES
ADJ_barchart <- function(df1, df2){
  
  stats1 <- subset(df1, upos %in% c("ADJ")) 
  stats1 <- txt_freq(stats1$token)
  stats1$key <- factor(stats1$key, levels = rev(stats1$key))
  
  stats2 <- subset(df2, upos %in% c("ADJ")) 
  stats2 <- txt_freq(stats2$token)
  stats2$key <- factor(stats2$key, levels = rev(stats2$key))
  
  c(barchart(key ~ freq, data = head(stats1, 20), col = "cadetblue", 
           main = "Most occurring adjectives: BEFORE vs AFTER", xlab = "Freq"),
      barchart(key ~ freq, data = head(stats2, 20), col = "skyblue", 
         xlab = "Freq"))
}

## Using RAKE to find keywords
RAKE_KW_barchart <- function(df1,df2){
  
  stats1 <- keywords_rake(x = df1, term = "lemma", group = "doc_id", 
                         relevant = df1$upos %in% c("NOUN", "ADJ"))
  stats1$key <- factor(stats1$keyword, levels = rev(stats1$keyword))
  
  stats2 <- keywords_rake(x = df2, term = "lemma", group = "doc_id", 
                         relevant = df2$upos %in% c("NOUN", "ADJ"))
  stats2$key <- factor(stats2$keyword, levels = rev(stats2$keyword))
  
  
  c(barchart(key ~ rake, data = head(subset(stats1, freq > 3), 20), col = "cadetblue", 
           main = "Keywords identified by RAKE: BEFORE vs AFTER", 
           xlab = "Rake"),
    barchart(key ~ rake, data = head(subset(stats2, freq > 3), 20), col = "skyblue", 
           xlab = "Rake"))
}

## Using Pointwise Mutual Information Collocations
PWI_barchart <- function(df1, df2){
  
  df1$word <- tolower(df1$token)
  stats1 <- keywords_collocation(x = df1, term = "word", group = "doc_id")
  stats1$key <- factor(stats1$keyword, levels = rev(stats1$keyword))
  
  df2$word <- tolower(df2$token)
  stats2 <- keywords_collocation(x = df2, term = "word", group = "doc_id")
  stats2$key <- factor(stats2$keyword, levels = rev(stats2$keyword))
  
  c(barchart(key ~ pmi, data = head(subset(stats1, freq > 3), 20), col = "cadetblue", 
           main = "Keywords identified by PMI Collocation: BEFORE vs AFTER", 
           xlab = "PMI (Pointwise Mutual Information)"),
      barchart(key ~ pmi, data = head(subset(stats2, freq > 3), 20), col = "skyblue", 
           xlab = "PMI (Pointwise Mutual Information)"))
}

## Using a sequence of POS tags (noun phrases / verb phrases)
POS_barchart <- function(df1, df2){
  
  df1$phrase_tag <- as_phrasemachine(df1$upos, type = "upos")
  stats1 <- keywords_phrases(x = df1$phrase_tag, term = tolower(df1$token), 
                            pattern = "(A|N)*N(P+D*(A|N)*N)*", 
                            is_regex = TRUE, detailed = FALSE)
  stats1 <- subset(stats1, ngram > 1 & freq > 3)
  stats1$key <- factor(stats1$keyword, levels = rev(stats1$keyword))
  
  df2$phrase_tag <- as_phrasemachine(df2$upos, type = "upos")
  stats2 <- keywords_phrases(x = df2$phrase_tag, term = tolower(df2$token), 
                            pattern = "(A|N)*N(P+D*(A|N)*N)*", 
                            is_regex = TRUE, detailed = FALSE)
  stats2 <- subset(stats2, ngram > 1 & freq > 3)
  stats2$key <- factor(stats2$keyword, levels = rev(stats2$keyword))
  
  c(barchart(key ~ freq, data = head(stats1, 20), col = "cadetblue", 
           main = "Keywords - simple noun phrases: BEFORE vs AFTER", xlab = "Frequency"),
      barchart(key ~ freq, data = head(stats2, 20), col = "skyblue", 
               xlab = "Frequency"))
}

Bar Charts from Functions Above

UPOS_barchart(x_odd.before, x_even.after)

NOUNS_barchart(x_odd.before, x_even.after)

ADJ_barchart(x_odd.before, x_even.after)

RAKE_KW_barchart(x_odd.before, x_even.after)

PWI_barchart(x_odd.before, x_even.after)

POS_barchart(x_odd.before, x_even.after)

Cooccurences

Cooccurences (part 2)

Corrs <- function(df){
  df$id <- unique_identifier(df, fields = c("sentence_id", "doc_id"))
  dtm <- subset(df, upos %in% c("NOUN", "ADJ"))
  dtm <- document_term_frequencies(dtm, document = "id", term = "lemma")
  dtm <- document_term_matrix(dtm)
  dtm <- dtm_remove_lowfreq(dtm, minfreq = 5)
  termcorrelations <- dtm_cor(dtm)
  y <- as_cooccurrence(termcorrelations)
  y <- subset(y, term1 < term2 & abs(cooc) > 0.2)
  y <- y[order(abs(y$cooc), decreasing = TRUE), ]
  print(y[1:25,])
}

Corrs(x_odd.before)

Corrs(x_even.after)

NA

rm(list=ls())

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