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upi_repair.R
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upi_repair.R
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# 2h, 64GB
library(tmT)
library(tosca)
library(data.table)
library(lubridate)
library(rollinglda)
library(ldaPrototype)
library(ggplot2)
library(GGally)
library(ggthemes)
rootpath = "//media/TextMining/DoCMA/data"
setwd(rootpath)
####### data_prep:
message("#########\ndata_prep\n#########")
starttime = Sys.time()
setwd(file.path("//media/TextMining/DoCMA/data", "Working_Paper_Uncertainty"))
folder = readLines("folder.txt")
s.date = as.Date(readLines("sdate.txt"))
e.date = as.Date(folder)
sw = union(removeUmlauts(tm::stopwords("de")), readLines("stopwords_deutsch_lang.txt"))
setwd(folder)
tablist = list()
## save clean, wl, docs and vocab
for(type in c("_wirtschaft_unsicher", "_wirtschaft", "")){
obj = readRDS(file.path("..", paste0("obj_updated", type, ".rds")))
obj$text = obj$text[!duplicated(names(obj$text))]
obj$meta = obj$meta[!duplicated(obj$meta$id),]
obj = filterDate(obj, s.date = s.date, e.date = e.date)
obj = cleanTexts(obj, sw = sw)
saveRDS(obj, paste0("clean_new", type, ".rds"))
obj = mergeTextmeta(list(
readRDS(file.path("..", paste0("clean_updated", type, ".rds"))),
obj))
saveRDS(obj, file.path("..", paste0("clean_updated", type, ".rds")))
tab = cbind(plotScot(obj, type = "docs"), type = "docs")
colnames(tab) = c("date", paste0("n", type), "type")
tab2 = cbind(plotScot(obj, type = "words"), type = "words")
colnames(tab2) = c("date", paste0("n", type), "type")
tablist[[type]] = rbindlist(list(tab, tab2))
#wl = makeWordlist(obj$text)
#saveRDS(wl, paste0("wl", type, ".rds"))
#vocab = wl$words[wl$wordtable > 5]
#saveRDS(vocab, paste0("vocab", type, ".rds"))
#docs = LDAprep(obj$text, vocab)
#saveRDS(docs, paste0("docs", type, ".rds"))
}
tab = merge(merge(tablist[[3]], tablist[[2]]), tablist[[1]])
saveRDS(tab, file = file.path("..", "counts.rds"))
fwrite(tab, file = file.path("..", "counts.csv"))
gc()
difftime(Sys.time(), starttime, units = "hours")
rm(list = ls())
gc()
setwd("..")
####### roll_update:
message("#########\nroll_update\n#########")
folder = readLines("folder.txt")
s.date = as.Date(readLines("sdate.txt"))
e.date = as.Date(folder)
# overwrites the manually set date above; assumes monthly updates
chunks = seq.Date(from = s.date, to = e.date, by = "month")
# load
obj = readRDS("clean_updated_wirtschaft_unsicher.rds")
init = readRDS(file.path(as.character(s.date-1), "roll.rds"))
# modeling
setwd(folder)
obj = filterDate(obj, s.date = s.date, e.date = e.date)
obj = textmeta(meta = obj$meta, text = obj$text)
texts = obj$text
dates = obj$meta$date[match(names(texts), obj$meta$id)]
roll = updateRollingLDA(init, texts, dates, chunks, "3 quarter")
saveRDS(roll, "roll.rds")
rm(list = ls())
gc()
setwd("..")
####### analysis:
message("#########\nanalysis\n#########")
labels = c("Topic 1: Corporate Culture",
"Topic 2: EU Conflicts",
"Topic 3: Energy & Climate Change Mitigation",
"Topic 4: Companies & Markets",
"Topic 5: Geopolitics",
"Topic 6: Society",
"Topic 7: Financial Markets I",
"Topic 8: German Politics I",
"Topic 9: Miscellaneous",
"Topic 10: Financial Markets II",
"Topic 11: Leisure & Hospitality",
"Topic 12: Central banks",
"Topic 13: German Economy",
"Topic 14: German Politics II")
folder = readLines("folder.txt")
counts = readRDS("counts.rds")
obj = filterDate(readRDS("obj_updated_wirtschaft_unsicher.rds"), e.date = folder)
setwd(folder)
dir.create("analysis")
counts = counts[type == "words"]
pdf(file.path("analysis", "words.pdf"), width = 10, height = 7)
print(ggplot(counts) + aes(x = date, y = n/1e6) + geom_line() +
ylim(c(0, max(counts$n)/1e6)) +
xlab("monthly") + ylab("n (in Million)"))
print(ggplot(counts) + aes(x = date, y = n_wirtschaft/n) + geom_line() +
ylim(c(0, max(counts$n_wirtschaft/counts$n))) +
xlab("monthly"))
print(ggplot(counts) + aes(x = date, y = n_wirtschaft_unsicher/n_wirtschaft) + geom_line() +
ylim(c(0, max(counts$n_wirtschaft_unsicher/counts$n_wirtschaft))) +
xlab("monthly"))
print(ggplot(counts) + aes(x = date, y = n_wirtschaft_unsicher/n) + geom_line() +
ylim(c(0, max(counts$n_wirtschaft_unsicher/counts$n))) +
xlab("monthly"))
dev.off()
file = "roll.rds"
roll = readRDS(file)
K = getK(getLDA(roll))
## topTexts
dir.create(file.path("analysis", "topTexts"))
mat = topTexts(getLDA(roll), getNames(roll), 100, tnames = gsub(":", "", gsub(" ", "_", labels)))
showTexts(obj, mat, file = "analysis/topTexts/")
## topTexts per Month
dir.create(file.path("analysis", "topTextsPerMonth"))
for(m in as.character(seq.Date(as.Date("2001-01-01"), as.Date(folder), "month"))){
m = as.Date(m)
doc_sums = list(document_sums = getDocument_sums(getLDA(roll))[,getDates(roll) >= m & getDates(roll) < m + months(1)])
mat = topTexts(doc_sums, getNames(roll)[getDates(roll) >= m & getDates(roll) < m + months(1)], 10, tnames = gsub(":", "", gsub(" ", "_", labels)))
dir.create(file.path("analysis", "topTextsPerMonth", as.character(substr(m, 0, 7))))
showTexts(obj, mat, file = paste0("analysis/topTextsPerMonth/", as.character(substr(m, 0, 7)), "/"))
}
## topwords
topwords = topWords(getTopics(getLDA(roll)), 200)
prop = round(rowSums(getTopics(getLDA(roll))) / sum(getTopics(getLDA(roll))) * 100, 4)
out = rbind(prop, topwords)
colnames(out) = labels
row.names(out) = c("Proportion (%)", 1:200)
write.csv(out,
file = file.path("analysis", "topwords.csv"),
fileEncoding = "UTF-8")
## topics
tab = plotTopic(object = obj, ldaresult = getLDA(roll), ldaID = getNames(roll))
tabrel = plotTopic(object = obj, ldaresult = getLDA(roll), ldaID = getNames(roll), rel = TRUE)
colnames(tab) = colnames(tabrel) = c("date", labels)
write.csv(tab, file = file.path("analysis", "topics.csv"),
fileEncoding = "UTF-8", row.names = FALSE)
write.csv(tabrel, file = file.path("analysis", "topics_rel.csv"),
fileEncoding = "UTF-8", row.names = FALSE)
tabrel_wirtschaft = cbind(date = tabrel$date,
tabrel[, -1] *
counts[match(tabrel$date, date), n_wirtschaft_unsicher/n_wirtschaft])
write.csv(tabrel_wirtschaft, file = file.path("analysis", "topics_rel_to_wirtschaft.csv"),
fileEncoding = "UTF-8", row.names = FALSE)
tabrel_all = cbind(date = tabrel$date,
tabrel[, -1] *
counts[match(tabrel$date, date), n_wirtschaft_unsicher/n])
write.csv(tabrel_all, file = file.path("analysis", "topics_rel_to_entire.csv"),
fileEncoding = "UTF-8", row.names = FALSE)
# delete topic 9 (Misc.) for upi calculation
upi = data.frame(date = tabrel_all$date,
upi = rowSums(tabrel_all[,-c(1,10)]),
sub_real_economy = rowSums(tabrel_all[, c(1,3,4,11,13)+1]),
sub_politics = rowSums(tabrel_all[, c(2,5,6,8,12,14)+1]),
sub_financial_markets = rowSums(tabrel_all[, c(7,10)+1]))
write.csv(upi, file = file.path("analysis", "upi.csv"),
fileEncoding = "UTF-8", row.names = FALSE)
upi_tmp = upi
upi = unname(upi)
sub = rbind(cbind(a = upi[, c(1,3)], b = "Real Economy"),
cbind(a = upi[, c(1,4)], b = "Politics"),
cbind(a = upi[, c(1,5)], b = "Financial Markets"))
colnames(sub) = c("date", "upi", "Subindex")
upi = upi_tmp
pdf(file.path("analysis", "upi.pdf"), width = 10, height = 7)
print(ggplot(upi) + aes(x = date, y = upi) + geom_line() + xlab("") + ylab("UPI (Share)"))
print(ggplot(sub) +
aes(x = date, y = upi, group = Subindex, color = Subindex) +
geom_line() +
xlab("") + ylab("Share") + theme(legend.position = "top"))
dev.off()
## topics cosine
# quarter
quarters = lubridate::floor_date(getDates(roll), "quarter")
xquarter = sort(unique(quarters))
nquarter = length(xquarter)
topicsq = lapply(xquarter, function(x){
tmp = table(factor(unlist(getAssignments(getLDA(roll))[quarters == x])+1, levels = 1:getK(getLDA(roll))),
factor(unlist(lapply(getDocs(roll)[quarters == x], function(y) y[1,]))+1,
levels = seq_len(length(getVocab(roll)))))
tmp = matrix(as.integer(tmp), nrow = getK(getLDA(roll)))
colnames(tmp) = getVocab(roll)
tmp
})
topics = lapply(1:getK(getLDA(roll)), function(k){
tmp = sapply(topicsq, function(x) x[k,])
colnames(tmp) = paste0("Q", 1:nquarter)
tmp
})
sims = lapply(1:getK(getLDA(roll)), function(k){
cosineTopics(topics[[k]], pm.backend = "socket", ncpus = 4)
})
valq = sapply(sims, function(x) c(NA, x$sims[cbind(2:nquarter,2:nquarter-1)]))
valq_first = sapply(sims, function(x) x$sims[,1])
valq_last = sapply(sims, function(x) x$sims[nquarter,])
saveRDS(sims, file.path("analysis", "sim_quarterly.rds"))
# month
months = lubridate::floor_date(getDates(roll), "month")
xmonth = sort(unique(months))
nmonth = length(xmonth)
topicsm = lapply(xmonth, function(x){
tmp = table(factor(unlist(getAssignments(getLDA(roll))[months == x])+1, levels = 1:getK(getLDA(roll))),
factor(unlist(lapply(getDocs(roll)[months == x], function(y) y[1,]))+1,
levels = seq_len(length(getVocab(roll)))))
tmp = matrix(as.integer(tmp), nrow = getK(getLDA(roll)))
colnames(tmp) = getVocab(roll)
tmp
})
topics = lapply(1:getK(getLDA(roll)), function(k){
tmp = sapply(topicsm, function(x) x[k,])
colnames(tmp) = paste0("Q", 1:nmonth)
tmp
})
sims = lapply(1:getK(getLDA(roll)), function(k){
cosineTopics(topics[[k]], pm.backend = "socket", ncpus = 4)
})
valm = sapply(sims, function(x) c(NA, x$sims[cbind(2:nmonth,2:nmonth-1)]))
valm_first = sapply(sims, function(x) x$sims[,1])
valm_last = sapply(sims, function(x) x$sims[nmonth,])
saveRDS(sims, file.path("analysis", "sim_monthly.rds"))
xmin = min(xmonth)
cosine_quarterly = ggmatrix(lapply(1:14, function(i){
ggplot() + geom_line(aes(x = xmonth, y = valm[,i]), col = "darkgrey") + ylim(c(0,1)) +
geom_line(aes(x = xquarter, y = valq_first[,i], col = "green")) +
geom_line(aes(x = xquarter, y = valq_last[,i], col = "red")) +
geom_line(aes(x = xquarter, y = valq[,i])) +
annotate("text", x = xmin, y = 0.05, label = labels[i], hjust = 0, vjust = 0)
}), nrow = 7, ncol = 2, ylab = "Cosine Similarity")
cosine_monthly = ggmatrix(lapply(1:14, function(i){
ggplot() + geom_line(aes(x = xquarter, y = valq[,i]), col = "darkgrey") + ylim(c(0,1)) +
geom_line(aes(x = xmonth, y = valm_first[,i], col = "green")) +
geom_line(aes(x = xmonth, y = valm_last[,i], col = "red")) +
geom_line(aes(x = xmonth, y = valm[,i])) +
annotate("text", x = xmin, y = 0.05, label = labels[i], hjust = 0, vjust = 0)
}), nrow = 7, ncol = 2, ylab = "Cosine Similarity")
pdf(file.path("analysis", "topics_cosine_quarter.pdf"), width = 8, height = 10)
print(cosine_quarterly)
dev.off()
tiff(file.path("analysis", "topics_cosine_quarter.tiff"),
width = 1600, height = 2000, pointsize = 200, compression = "lzw", res = 200,
type = "cairo")
print(cosine_quarterly)
dev.off()
pdf(file.path("analysis", "topics_cosine_month.pdf"), width = 8, height = 10)
print(cosine_monthly)
dev.off()
tiff(file.path("analysis", "topics_cosine_month.tiff"),
width = 1600, height = 2000, pointsize = 200, compression = "lzw", res = 200,
type = "cairo")
print(cosine_monthly)
dev.off()
## topWordsPerMonth
topwordsm = lapply(topicsm, topWords, numWords = 50)
topwordsm = lapply(1:K, function(k) sapply(seq_along(topwordsm), function(t) topwordsm[[t]][,k]))
dir.create(file.path("analysis", "topWordsPerMonth"))
for(i in 1:K){
out = rbind(round(tabrel[, i+1] * 100, 4),
round(tabrel_wirtschaft[, i+1] * 100, 4),
round(tabrel_all[, i+1] * 1000, 4),
topwordsm[[i]])
colnames(out) = as.character(tabrel$date)
row.names(out) = c("Share (in %) on \"wirtschaft\"&\"unsicher\"-corpus",
"Share (in %) on \"wirtschaft\"-corpus",
"Share (in Promille) on entire corpus",
1:50)
write.csv(out,
file = file.path("analysis", "topWordsPerMonth",
paste0(gsub(":", "", gsub(" ", "_", colnames(tabrel)[i+1])), ".csv")),
fileEncoding = "UTF-8")
}
## covid and fear
vocab = getVocab(roll)
## covid
covid = union(union(vocab[grepl("corona", vocab)], vocab[grepl("covid", vocab)]),
vocab[grepl("pandemie", vocab)])
covid_ids = match(covid, vocab)
a = (unname(unlist(lapply(getDocs(roll), function(x) x[1,])))+1) %in% covid_ids
b = (unlist(getAssignments(getLDA(roll)))+1)[a]
d = floor_date(rep(getDates(roll),
lengths(getAssignments(getLDA(roll))))[a], "month")
o = table(b, d)
o = o[, colnames(o) >= c("2020-01-01")]
tab = plotTopic(obj, getLDA(roll), getNames(roll), unit = "month")
out = t(o)
tmp = out
colnames(tmp) = labels
write.csv(tmp, file.path("analysis", "corona.csv"))
out = out / tab[match(rownames(out), as.character(tab$date)), 2:15]
colnames(out) = rownames(o)
rownames(out) = colnames(o)
colnames(out) = labels
write.csv(out, file.path("analysis", "corona_rel_to_topic.csv"))
writeLines(covid, file.path("analysis", "corona.txt"))
## fear
fear = setdiff(vocab[grepl("angst", vocab)],
c("abstiegsangst", "gangster", "terrorangst", "kriegsangst"))
fear_ids = match(fear, vocab)
a = (unname(unlist(lapply(getDocs(roll), function(x) x[1,])))+1) %in% fear_ids
b = (unlist(getAssignments(getLDA(roll)))+1)[a]
d = floor_date(rep(getDates(roll),
lengths(getAssignments(getLDA(roll))))[a], "quarter")
o = table(b, d)
tab = plotTopic(obj, getLDA(roll), getNames(roll), unit = "month")
out = t(o)
tmp = out
colnames(tmp) = labels
write.csv(tmp, file.path("analysis", "fear.csv"))
out = out / tab[match(rownames(out), as.character(tab$date)), 2:15]
colnames(out) = rownames(o)
rownames(out) = colnames(o)
colnames(out) = labels
write.csv(out, file.path("analysis", "fear_rel_to_topic.csv"))
writeLines(fear, file.path("analysis", "fear.txt"))
}else{
message("Keine neuen Daten!")
}