second run; improving behaviour at different places

.Rhistory

@@ -1,220 +1,3 @@
-curchars <- curchars - 1
-} else {
-curhash <- FALSE
-}
-# Now expand the current tag by possible suffixes that may be plural forms
-# Only do if it isn't an acronym or specific hastag
-if(!curacro && !curhash) {
-for(e in 1:length(tagexpand)) {
-curtag[e] <- str_c(curtag[1], tagexpand[e])
-}
-}
-##############
-if(curchars <= 4 || curacro || curhash) {
-cat("distance 0\n")
-} else {
-cat("distance 1\n")
-}
-curtag <- "EURATOM"
-curchars <- nchar(curtag, type = "chars")
-# Check if tag is an acronym. If so, ignore.case will be deactivated in smartPatternMatch
-curacro <- checkAcronym(string = curtag)
-# Check if tag is some kind of specific hashtag. If so, do not handle as acronym, but don't expand it either
-if(str_detect(curtag, "^#")) {
-curacro <- FALSE
-curhash <- TRUE
-curtag <- str_replace(curtag, "#", "")
-curchars <- curchars - 1
-} else {
-curhash <- FALSE
-}
-# Now expand the current tag by possible suffixes that may be plural forms
-# Only do if it isn't an acronym or specific hastag
-if(!curacro && !curhash) {
-for(e in 1:length(tagexpand)) {
-curtag[e] <- str_c(curtag[1], tagexpand[e])
-}
-}
-##############
-if(curchars <= 4 || curacro || curhash) {
-cat("distance 0\n")
-} else {
-cat("distance 1\n")
-}
-curtag <- "Energiewende"
-curchars <- nchar(curtag, type = "chars")
-# Check if tag is an acronym. If so, ignore.case will be deactivated in smartPatternMatch
-curacro <- checkAcronym(string = curtag)
-# Check if tag is some kind of specific hashtag. If so, do not handle as acronym, but don't expand it either
-if(str_detect(curtag, "^#")) {
-curacro <- FALSE
-curhash <- TRUE
-curtag <- str_replace(curtag, "#", "")
-curchars <- curchars - 1
-} else {
-curhash <- FALSE
-}
-# Now expand the current tag by possible suffixes that may be plural forms
-# Only do if it isn't an acronym or specific hastag
-if(!curacro && !curhash) {
-for(e in 1:length(tagexpand)) {
-curtag[e] <- str_c(curtag[1], tagexpand[e])
-}
-}
-##############
-if(curchars <= 4 || curacro || curhash) {
-cat("distance 0\n")
-} else {
-cat("distance 1\n")
-}
-curtag <- "bnd"
-curchars <- nchar(curtag, type = "chars")
-# Check if tag is an acronym. If so, ignore.case will be deactivated in smartPatternMatch
-curacro <- checkAcronym(string = curtag)
-# Check if tag is some kind of specific hashtag. If so, do not handle as acronym, but don't expand it either
-if(str_detect(curtag, "^#")) {
-curacro <- FALSE
-curhash <- TRUE
-curtag <- str_replace(curtag, "#", "")
-curchars <- curchars - 1
-} else {
-curhash <- FALSE
-}
-# Now expand the current tag by possible suffixes that may be plural forms
-# Only do if it isn't an acronym or specific hastag
-if(!curacro && !curhash) {
-for(e in 1:length(tagexpand)) {
-curtag[e] <- str_c(curtag[1], tagexpand[e])
-}
-}
-##############
-if(curchars <= 4 || curacro || curhash) {
-cat("distance 0\n")
-} else {
-cat("distance 1\n")
-}
-curtag <- "#WM"
-curchars <- nchar(curtag, type = "chars")
-# Check if tag is an acronym. If so, ignore.case will be deactivated in smartPatternMatch
-curacro <- checkAcronym(string = curtag)
-# Check if tag is some kind of specific hashtag. If so, do not handle as acronym, but don't expand it either
-if(str_detect(curtag, "^#")) {
-curacro <- FALSE
-curhash <- TRUE
-curtag <- str_replace(curtag, "#", "")
-curchars <- curchars - 1
-} else {
-curhash <- FALSE
-}
-# Now expand the current tag by possible suffixes that may be plural forms
-# Only do if it isn't an acronym or specific hastag
-if(!curacro && !curhash) {
-for(e in 1:length(tagexpand)) {
-curtag[e] <- str_c(curtag[1], tagexpand[e])
-}
-}
-##############
-if(curchars <= 4 || curacro || curhash) {
-cat("distance 0\n")
-} else {
-cat("distance 1\n")
-}
-curtag
-curtag <- "Energiewende"
-curchars <- nchar(curtag, type = "chars")
-# Check if tag is an acronym. If so, ignore.case will be deactivated in smartPatternMatch
-curacro <- checkAcronym(string = curtag)
-# Check if tag is some kind of specific hashtag. If so, do not handle as acronym, but don't expand it either
-if(str_detect(curtag, "^#")) {
-curacro <- FALSE
-curhash <- TRUE
-curtag <- str_replace(curtag, "#", "")
-curchars <- curchars - 1
-} else {
-curhash <- FALSE
-}
-# Now expand the current tag by possible suffixes that may be plural forms
-# Only do if it isn't an acronym or specific hastag
-if(!curacro && !curhash) {
-for(e in 1:length(tagexpand)) {
-curtag[e] <- str_c(curtag[1], tagexpand[e])
-}
-}
-##############
-if(curchars <= 4 || curacro || curhash) {
-cat("distance 0\n")
-} else {
-cat("distance 1\n")
-}
-curtag <- "Energiewende"
-curchars <- nchar(curtag, type = "chars")
-# Check if tag is an acronym. If so, ignore.case will be deactivated in smartPatternMatch
-curacro <- checkAcronym(string = curtag)
-# Check if tag is some kind of specific hashtag. If so, do not handle as acronym, but don't expand it either
-if(str_detect(curtag, "^#")) {
-curacro <- FALSE
-curhash <- TRUE
-curtag <- str_replace(curtag, "#", "")
-curchars <- curchars - 1
-} else {
-curhash <- FALSE
-}
-# Now expand the current tag by possible suffixes that may be plural forms
-# Only do if it isn't an acronym or specific hastag
-if(!curacro && !curhash) {
-for(e in 1:length(tagexpand)) {
-curtag[e] <- str_c(curtag[1], tagexpand[e])
-}
-}
-# Set Levenshtein distance depending on char length, acronym and hashtag status
-if(curchars <= 4 || curacro || curhash) {
-curdistance <- 0
-} else {
-curdistance <- 1
-}
-curtag
-smartPatternMatch("Die Energiewende ist toll!", curtag, curdistance, curacro)
-smartPatternMatch("Die Energiewende ist toll!", curtag[1], curdistance, curacro)
-smartPatternMatch("Die Energiewende ist toll!", curtag[2], curdistance, curacro)
-smartPatternMatch("Die Energiewende ist toll!", sprintf("%s", curtag), curdistance, curacro)
-tags_found <- NULL
-# Match the tweet with each variation of tagexpand
-for(e in 1:length(curtag)) {
-tags_found[e] <- smartPatternMatch(curtext, curtag[e], curdistance, curacro)
-}
-curtext <- "Die Energiewende ist toll!"
-tags_found <- NULL
-# Match the tweet with each variation of tagexpand
-for(e in 1:length(curtag)) {
-tags_found[e] <- smartPatternMatch(curtext, curtag[e], curdistance, curacro)
-}
-tags_found
-curtag
-curtag <- "#WM2014"
-curtext <- "Ich freu mich auf wm2014 sehr"
-curchars <- nchar(curtag, type = "chars")
-# Check if tag is an acronym. If so, ignore.case will be deactivated in smartPatternMatch
-curacro <- checkAcronym(string = curtag)
-# Check if tag is some kind of specific hashtag. If so, do not handle as acronym, but don't expand it either
-if(str_detect(curtag, "^#")) {
-curacro <- FALSE
-curhash <- TRUE
-curtag <- str_replace(curtag, "#", "")
-curchars <- curchars - 1
-} else {
-curhash <- FALSE
-}
-# Now expand the current tag by possible suffixes that may be plural forms
-# Only do if it isn't an acronym or specific hastag
-if(!curacro && !curhash) {
-for(e in 1:length(tagexpand)) {
-curtag[e] <- str_c(curtag[1], tagexpand[e])
-}
-}
-# Set Levenshtein distance depending on char length, acronym and hashtag status
-if(curchars <= 4 || curacro || curhash) {
-curdistance <- 0
-} else {
 curdistance <- 1
 }
 # Match current tweet with tag. If >= 5 letters allow 1 changed letter, if >=8 letters allow also 1 (Levenshtein distance)
@@ -510,3 +293,220 @@ for(i in 1:20) { cat(i,"\n")
 Sys.sleep(10)}
 list.dirs()
 list.files()
+rm(results)
+setwd("matched-ids/")
+results_files <- list.files()
+results_files
+results_files <- "all.csv"
+for(r in 1:length(results_files)) {
+if(r == 1) {
+results <- read.csv(results_files[r], sep=";", colClasses=c("character", "character", "character", "character"), header=F)
+} else {
+results_temp <- read.csv(results_files[r], sep=";", colClasses=c("character", "character", "character", "character"), header=F)
+results <- insertRow(results, results_temp)
+}
+}
+rm(r, results_temp, results_files)
+results <- results[!duplicated(results), ]
+names(results) <- c("date", "id_str", "issue", "tags")
+results <- results[order(results$id_str), ]
+row.names(results) <- NULL
+results[23381,]
+results[53381,]
+results[43253,]
+for(r in 53371:nrow(results)) {
+curdate <- as.character(results$date[r])
+curid <- as.character(results$id_str[r])
+curissue <- as.character(results$issue[r])
+curtag <- as.character(results$tags[r])
+cat("Sorting match", r, "of 53383 \n")
+# Update issue counter (date and issue)
+issues[issues[, "date"] == curdate, curissue] <- issues[issues[, "date"] == curdate, curissue] + 1
+# Update tweet dataframe (id, issue and tags)
+oldissue <- tweets[tweets[, "id_str"] == curid, "issue"]
+tweets[tweets[, "id_str"] == curid, "issue"] <- str_c(oldissue, curissue, ",")
+oldtag <- tweets[tweets[, "id_str"] == curid, "tags"]
+tweets[tweets[, "id_str"] == curid, "tags"] <- str_c(oldtag, curtag, ",")
+}
+issues[issueheads] <- 0
+View(issues)
+for(r in 1:nrow(results)) {
+curdate <- as.character(results$date[r])
+curid <- as.character(results$id_str[r])
+curissue <- as.character(results$issue[r])
+curtag <- as.character(results$tags[r])
+cat("Sorting match", r, "of 53383 \n")
+# Update issue counter (date and issue)
+issues[issues[, "date"] == curdate, curissue] <- issues[issues[, "date"] == curdate, curissue] + 1
+# Update tweet dataframe (id, issue and tags)
+oldissue <- tweets[tweets[, "id_str"] == curid, "issue"]
+tweets[tweets[, "id_str"] == curid, "issue"] <- str_c(oldissue, curissue, ",")
+oldtag <- tweets[tweets[, "id_str"] == curid, "tags"]
+tweets[tweets[, "id_str"] == curid, "tags"] <- str_c(oldtag, curtag, ",")
+}
+require(lubridate)
+require(XML)
+require(ggplot2)
+require(reshape2)
+require(stringr)
+require(foreach)
+require(doParallel)
+for(r in 1:nrow(results)) {
+curdate <- as.character(results$date[r])
+curid <- as.character(results$id_str[r])
+curissue <- as.character(results$issue[r])
+curtag <- as.character(results$tags[r])
+cat("Sorting match", r, "of 53383 \n")
+# Update issue counter (date and issue)
+issues[issues[, "date"] == curdate, curissue] <- issues[issues[, "date"] == curdate, curissue] + 1
+# Update tweet dataframe (id, issue and tags)
+oldissue <- tweets[tweets[, "id_str"] == curid, "issue"]
+tweets[tweets[, "id_str"] == curid, "issue"] <- str_c(oldissue, curissue, ",")
+oldtag <- tweets[tweets[, "id_str"] == curid, "tags"]
+tweets[tweets[, "id_str"] == curid, "tags"] <- str_c(oldtag, curtag, ",")
+}
+results[119,]
+results[120,]
+load(file = "tweets_untagged.RData")
+setwd("~/Dokumente/Uni/Aktuell/BA-Arbeit/uni-ba-issuecomp")
+results_files <- "matched-ids/all.csv"
+load(file = "tweets_untagged.RData")
+View(issues)
+issues <- data.frame(date = drange)
+issuelist <- readLines("issues.xml")
+issuelist <- str_replace_all(string = issuelist, pattern = ".*<!-- .+ -->", "")
+issuelist <- xmlToList(issuelist)
+issueheads <- names(issuelist)
+issues[issueheads] <- 0
+tweets$issue <- ""
+tweets$tags <- ""
+View(results)
+rm(r, results_temp, results_files)
+results <- results[!duplicated(results), ]
+names(results) <- c("date", "id_str", "issue", "tags")
+results <- results[order(results$id_str), ]
+row.names(results) <- NULL
+for(r in 1:nrow(results)) {
+curdate <- as.character(results$date[r])
+curid <- as.character(results$id_str[r])
+curissue <- as.character(results$issue[r])
+curtag <- as.character(results$tags[r])
+cat("Sorting match", r, "of 53383 \n")
+# Update issue counter (date and issue)
+issues[issues[, "date"] == curdate, curissue] <- issues[issues[, "date"] == curdate, curissue] + 1
+# Update tweet dataframe (id, issue and tags)
+oldissue <- tweets[tweets[, "id_str"] == curid, "issue"]
+tweets[tweets[, "id_str"] == curid, "issue"] <- str_c(oldissue, curissue, ",")
+oldtag <- tweets[tweets[, "id_str"] == curid, "tags"]
+tweets[tweets[, "id_str"] == curid, "tags"] <- str_c(oldtag, curtag, ",")
+}
+curdate
+curissue
+issues[issues[, "date"] == curdate, curissue]
+issueheads
+issuelist <- readLines("issues-v2.xml")
+issues <- data.frame(date = drange)
+issuelist <- readLines("issues-v2.xml")
+issuelist <- str_replace_all(string = issuelist, pattern = ".*<!-- .+ -->", "")
+issuelist <- xmlToList(issuelist)
+issueheads <- names(issuelist)
+issues[issueheads] <- 0
+tweets$issue <- ""
+tweets$tags <- ""
+for(r in 1:nrow(results)) {
+curdate <- as.character(results$date[r])
+curid <- as.character(results$id_str[r])
+curissue <- as.character(results$issue[r])
+curtag <- as.character(results$tags[r])
+cat("Sorting match", r, "of 53383 \n")
+# Update issue counter (date and issue)
+issues[issues[, "date"] == curdate, curissue] <- issues[issues[, "date"] == curdate, curissue] + 1
+# Update tweet dataframe (id, issue and tags)
+oldissue <- tweets[tweets[, "id_str"] == curid, "issue"]
+tweets[tweets[, "id_str"] == curid, "issue"] <- str_c(oldissue, curissue, ",")
+oldtag <- tweets[tweets[, "id_str"] == curid, "tags"]
+tweets[tweets[, "id_str"] == curid, "tags"] <- str_c(oldtag, curtag, ",")
+}
+results[33170,]
+results[33171,]
+results$date[33170]
+results$date[33170] <- "2014-08-21"
+for(r in 33170:nrow(results)) {
+curdate <- as.character(results$date[r])
+curid <- as.character(results$id_str[r])
+curissue <- as.character(results$issue[r])
+curtag <- as.character(results$tags[r])
+cat("Sorting match", r, "of 53383 \n")
+# Update issue counter (date and issue)
+issues[issues[, "date"] == curdate, curissue] <- issues[issues[, "date"] == curdate, curissue] + 1
+# Update tweet dataframe (id, issue and tags)
+oldissue <- tweets[tweets[, "id_str"] == curid, "issue"]
+tweets[tweets[, "id_str"] == curid, "issue"] <- str_c(oldissue, curissue, ",")
+oldtag <- tweets[tweets[, "id_str"] == curid, "tags"]
+tweets[tweets[, "id_str"] == curid, "tags"] <- str_c(oldtag, curtag, ",")
+}
+save(tweets, file="tweets_tagged.RData")
+write.csv(tweets, file="tweets.csv")
+save(issues, file="issues.RData")
+require(stringr)
+require(reshape2)
+require(ggplot2)
+require(vars)
+drop_s <- which(str_detect(names(issues), "^s"))
+drop_i <- which(str_detect(names(issues), "^i"))
+issues_i <- issues[,-drop_s]
+issues_s <- issues[,-drop_i]
+issues_i$total <- rowSums(issues_i[2:ncol(issues_i)])
+issues_i$entropy <- 0
+for(r in 1:nrow(issues_i)) {
+curtotal <- as.numeric(issues_i$total[r])
+curp <- 0
+for(c in 2:ncol(issues_i)) {
+curcount <- as.numeric(issues_i[r,c])
+curp[c] <- curcount / curtotal
+}
+curp <- curp [2:length(curp)-2]
+curdrop <- which(curp==0)
+curp <- curp[-curdrop]
+issues_i$entropy[r] <- sum(-1 * curp * log(curp))
+}
+issues_s$total <- rowSums(issues_s[2:ncol(issues_s)])
+issues_s$entropy <- 0
+for(r in 1:nrow(issues_s)) {
+curtotal <- as.numeric(issues_s$total[r])
+curp <- 0
+for(c in 2:ncol(issues_s)) {
+curcount <- as.numeric(issues_s[r,c])
+curp[c] <- curcount / curtotal
+}
+curp <- curp [2:length(curp)-2]
+curdrop <- which(curp==0)
+curp <- curp[-curdrop]
+issues_s$entropy[r] <- sum(-1 * curp * log(curp))
+}
+stats_total <- data.frame(date=drange)
+stats_total$tpd <- 0
+stats_total$ipd <- issues_i$total
+stats_total$spd <- issues_s$total
+# Total number of tweets per day over time
+for(r in 1:length(drange)) {
+stats_total$tpd[r] <- length(tweets[tweets[, "created_at"] == drange[r], "id_str"])
+}
+g1 <- ggplot(data = stats_melt, aes(x=date,y=value,colour=variable, group=variable)) +
+geom_line()+
+geom_smooth(size=1,formula = y ~ x, method="loess", se=FALSE, color=1)
+g1
+stats_entropy <- data.frame(date=drange)
+stats_entropy$entropy <- issues_i$entropy
+stats_entropy <- melt(stats_entropy, id="date")
+g1 <- ggplot(data = stats_entropy, aes(x=date,y=value,colour=variable, group=variable)) +
+geom_line() +
+geom_smooth(size=1,formula = y ~ x, method="loess", se=FALSE, color=1)
+g1
+test <- VAR(issues[,2:32], p=1, type="none")
+View(issues_i)
+View(issues_s)
+View(issues)
+test <- VAR(issues[,2:44], p=1, type="none")
+VAR(issues_s[,2:23], p=1, type=c("const", "trend", "both", "none"), season=NULL, exogen = issues_i[2:22])
+plot(irf(test, impulse = names(issues_s[2:23]), response = names(issues_i[2:22])))

issuecomp-2-analysis.R

@@ -21,7 +21,7 @@ drange <- date_start + days(0:drange)
 # Import issues and prepare everything
 # Will only be filled after the large categorisation loop
 issues <- data.frame(date = drange)
-issuelist <- readLines("issues.xml")
+issuelist <- readLines("issues-v2.xml")
 issuelist <- str_replace_all(string = issuelist, pattern = ".*<!-- .+ -->", "")
 issuelist <- xmlToList(issuelist)
 issueheads <- names(issuelist)
@@ -66,7 +66,8 @@ foreach(d = 1:nrow(issues), .packages = c("stringr"), .combine=rbind) %dopar% {
     for(i in 1:length(issueheads)) {
       curissue <- issueheads[i]
       curtags <- as.character(issuelist[[curissue]])  
-      curfile <- str_c(id_folder,"/",curissue,".csv")
+#       curfile <- str_c(id_folder,"/",curissue,".csv")
+      curfile <- str_c(id_folder,"/",curdate,".csv")  # Possible solution to avoid buggy files when using many processes
       
       # Now test all tags of a single issue
       for(s in 1:length(curtags)) {
@@ -144,8 +145,9 @@ stopCluster(cl)
 # IMPORT RESULTS ----------------------------------------------------------
 
 # Import all files which have been generated at the categorisation run above.
-setwd("matched-ids/")
-results_files <- list.files()
+#setwd("matched-ids/")
+#results_files <- list.files()
+results_files <- "matched-ids/all.csv"
 for(r in 1:length(results_files)) {
   if(r == 1) {
     results <- read.csv(results_files[r], sep=";", colClasses=c("character", "character", "character", "character"), header=F)
@@ -166,15 +168,15 @@ row.names(results) <- NULL
 # (which wasn't possible in the categorisation process because of parallelisation)
 
 # Reset issues counter
-# issues[issueheads] <- 0
+#issues[issueheads] <- 0
 
-for(r in 1:nrow(results)) {
+for(r in 33170:nrow(results)) {
   curdate <- as.character(results$date[r])
   curid <- as.character(results$id_str[r])
   curissue <- as.character(results$issue[r])
   curtag <- as.character(results$tags[r])
   
-  cat("Sorting match", r, "of 62827 \n")
+  cat("Sorting match", r, "of 53383 \n")
   
   # Update issue counter (date and issue)
   issues[issues[, "date"] == curdate, curissue] <- issues[issues[, "date"] == curdate, curissue] + 1

issuecomp-3-calc.R

@@ -72,19 +72,19 @@ stats_entropy <- melt(stats_entropy, id="date")
 g1 <- ggplot(data = stats_entropy, aes(x=date,y=value,colour=variable, group=variable)) + 
   geom_line() + 
   geom_smooth(size=1,formula = y ~ x, method="loess", se=FALSE, color=1)
-# g1
+g1
 
 
 
 # VAR ---------------------------------------------------------------------
 
-test <- VAR(issues[,2:32], p=1, type=c("const", "trend", "both", "none"), season=NULL, exogen = NULL, lag.max = NULL, ic = c("AIC", "HQ", "SC", "FPE"))
-test <- VAR(issues_i[,2:22], p=1, type="none", exogen = issues_s[,2:3])
-test <- VAR(issues_s[,2:11], p=1, type="none")
-test <- VAR(issues[,2:32], p=1, type="none")
-VAR(issues_s[,2:11], p=1, type=c("const", "trend", "both", "none"), season=NULL, exogen = issues_i[2:22])
+# test <- VAR(issues[,2:32], p=1, type=c("const", "trend", "both", "none"), season=NULL, exogen = NULL, lag.max = NULL, ic = c("AIC", "HQ", "SC", "FPE"))
+# test <- VAR(issues_i[,2:22], p=1, type="none", exogen = issues_s[,2:3])
+# test <- VAR(issues_s[,2:11], p=1, type="none")
+test <- VAR(issues[,2:44], p=1, type="none")
+# VAR(issues_s[,2:23], p=1, type=c("const", "trend", "both", "none"), season=NULL, exogen = issues_i[2:22])
 
-plot(irf(test, impulse = names(issues_s[2:11]), response = names(issues_i[2:22])))
+plot(irf(test, impulse = names(issues_s[2:23]), response = names(issues_i[2:22])))
 
 capture.output(print(summary(test), prmsd=TRUE, digits=1), file="out.txt")