better pattern matching logic
This commit is contained in:
@@ -1,203 +1,3 @@
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dir.create(id_folder)
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issues <- data.frame(date = drange)
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issuelist <- xmlToList("issues.xml")
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issueheads <- names(issuelist)
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issues[issueheads] <- 0
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tweets$issue <- ""
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tweets$tags <- ""
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for(d in 1:nrow(issues)) {
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# Go through every day
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curdate <- issues$date[d]
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cat(as.character(curdate),"\n")
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# Put all tweets from specific day in a temporary DF
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tweets_curday <- tweets[tweets[, "created_at"] == curdate, ]
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for(t in 1:nrow(tweets_curday)){
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# Select tweet's text, make it lowercase and remove hashtag indicators (#)
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curtext <- as.character(tweets_curday$text[t])
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curtext <- str_replace_all(curtext, "#", "")
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curid <- as.character(tweets_curday$id_str[t])
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# Now test each single issue (not tag!)
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for(i in 1:length(issueheads)) {
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curissue <- issueheads[i]
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curtags <- as.character(issuelist[[curissue]])
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curfile <- str_c(id_folder,"/",curissue,".csv")
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# Now test all tags of a single issue
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for(s in 1:length(curtags)) {
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curtag <- curtags[s]
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curchars <- nchar(curtag, type = "chars")
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# Check if tag is an acronym. If so, ignore.case will be deactivated in smartPatternMatch
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if(curchars <= 4) {
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curacro <- checkAcronym(string = curtag, chars = curchars)
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} else {
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curacro <- FALSE
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}
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# Match current tweet with tag. If >= 5 letters allow 1 changed letter, if >=8 letters allow 2 (Levenshtein distance)
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tags_found <- smartPatternMatch(curtext, curtag, curchars, curacro)
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if(tags_found == 1) {
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# Raise number of findings on this day for this issue by 1
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issues[d,curissue] <- issues[d,curissue] + 1
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# Add issue and first matched tag of tweet to tweets-DF
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oldissue <- tweets[tweets[, "id_str"] == curid, "issue"]
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tweets[tweets[, "id_str"] == curid, "issue"] <- str_c(oldissue, curissue, ";")
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oldtag <- tweets[tweets[, "id_str"] == curid, "tags"]
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tweets[tweets[, "id_str"] == curid, "tags"] <- str_c(oldtag, curtag, ";")
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# Add information to file for function viewPatternMatching
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write(str_c(curdate,";\"",curid,"\";",curtag), curfile, append = TRUE)
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break
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}
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else {
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#cat("Nothing found\n")
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}
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} # /for curtags
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} # /for issuelist
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} # /for tweets_curday
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} # /for drange
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View(tweets)
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require(lubridate)
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require(XML)
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require(ggplot2)
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require(reshape2)
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require(stringr)
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smartPatternMatch("bla bla Matching bla bla", "matching", 8, FALSE)
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smartPatternMatch("bla bla Matching bla bla", "mating", 8, FALSE)
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source("issuecomp-functions.R")
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smartPatternMatch("bla bla Matching bla bla", "mating", 8, FALSE)
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test <- c("matching", "matccing", "matxxing")
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smartPatternMatch("bla bla Matching bla bla", "matching", 8, FALSE)
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smartPatternMatch("bla bla Matching bla bla", "matccing", 8, FALSE)
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smartPatternMatch <- function(string, pattern, chars, acronym) {
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patternrex <- str_c("\\b", pattern, "\\b")
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if(chars <= 4) { # 4 or less
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found <- agrep(patternrex, string, max.distance = list(all = 0), ignore.case = !acronym, fixed = FALSE)
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}
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else if(chars >= 8) { # 8 or more
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found <- agrep(patternrex, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE)
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# # Give longer words a chance by ignoring word boundaries \\b
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# if(convertLogical0(found) == 0) {
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# found <- grep(pattern, string, ignore.case = !acronym, fixed = FALSE)
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# }
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}
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else { # 5,6,7
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found <- agrep(patternrex, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE)
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}
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found <- convertLogical0(found)
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return(found)
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}
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smartPatternMatch("bla bla Matching bla bla", "matccing", 8, FALSE)
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smartPatternMatch("bla bla Matching bla bla", "matxxing", 8, FALSE)
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smartPatternMatch("bla bla Matching bla bla", sprintf(), 8, FALSE)
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sprintf("%s", test)
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smartPatternMatch("bla bla Matching bla bla", sprintf("%s", test), 8, FALSE)
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for(i in 1:length(test)) { smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE)}
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for(i in 1:length(test)) { cat(smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))}
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for(i in 1:length(test)) { tags_found[i] (smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))}
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for(i in 1:length(test)) { tags_found[i] <- (smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))}
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tags_found
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length(tags_found)
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any(tags_found)
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smartPatternMatch <- function(string, pattern, chars, acronym) {
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patternrex <- str_c("\\b", pattern, "\\b")
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if(chars <= 4) { # 4 or less
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found <- agrep(patternrex, string, max.distance = list(all = 0), ignore.case = !acronym, fixed = FALSE)
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}
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else if(chars >= 8) { # 8 or more
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found <- agrep(patternrex, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE)
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# # Give longer words a chance by ignoring word boundaries \\b
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# if(convertLogical0(found) == 0) {
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# found <- grep(pattern, string, ignore.case = !acronym, fixed = FALSE)
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# }
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}
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else { # 5,6,7
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found <- agrep(patternrex, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE)
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}
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found <- convertLogical0(found)
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if(found == 1) {
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found <- TRUE
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} else {
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found <- FALSE
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}
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return(found)
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}
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for(i in 1:length(test)) { tags_found[i] <- (smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))}
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any(tags_found)
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tags_found <- NULL
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rm(tags_found)
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for(i in 1:length(test)) { tags_found[i] <- (smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))}
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tags_found <- NULL
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for(i in 1:length(test)) { tags_found[i] <- (smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))}
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tags_found <- NULL
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for(i in 1:length(test)) { tags_found[i] <- (smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))}
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any(tags_found)
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curtag
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tagexpand <- c("s", "n", "en")
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curtag
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curtag[2] <- "bla"
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curtag
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curtag[2] <- NULL
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curtag[2] <- ""
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curtag
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rm(curtag[2])
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curtag <- "Tomate"
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for(e in 1:length(tagexpand)) {
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curtag[e] <- str_c(curtag[e], tagexpand[e])
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}
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curtag
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for(e in 1:length(tagexpand)) {
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curtag[e] <- str_c(curtag, tagexpand[e])
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}
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curtag <- "Tomate"
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for(e in 1:length(tagexpand)) {
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curtag[e] <- str_c(curtag, tagexpand[e])
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}
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curtag
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curtag <- "Tomate"
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for(e in 1:length(tagexpand)) {
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curtag[e] <- str_c(curtag[1], tagexpand[e])
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}
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curtag
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tagexpand <- c("", "s", "n", "en")
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for(e in 1:length(tagexpand)) {
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curtag[e] <- str_c(curtag[1], tagexpand[e])
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}
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curtag <- "Tomate"
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for(e in 1:length(tagexpand)) {
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curtag[e] <- str_c(curtag[1], tagexpand[e])
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}
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curtag
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smartPatternMatch <- function(string, pattern, chars, acronym) {
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patternrex <- str_c("\\b", pattern, "\\b")
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if(chars <= 4) { # 4 or less
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found <- agrep(patternrex, string, max.distance = list(all = 0), ignore.case = !acronym, fixed = FALSE)
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}
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else if(chars >= 8) { # 8 or more
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found <- agrep(patternrex, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE)
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# # Give longer words a chance by ignoring word boundaries \\b
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# if(convertLogical0(found) == 0) {
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# found <- grep(pattern, string, ignore.case = !acronym, fixed = FALSE)
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# }
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}
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else { # 5,6,7
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found <- agrep(patternrex, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE)
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}
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found <- convertLogical0(found)
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if(found == 1) {
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found <- TRUE
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} else {
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found <- FALSE
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}
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return(found)
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}
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# MATCH TWEETS ------------------------------------------------------------
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id_folder <- "matched-ids"
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unlink(id_folder, recursive = TRUE)
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dir.create(id_folder)
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issues <- data.frame(date = drange)
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issuelist <- xmlToList("issues.xml")
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issueheads <- names(issuelist)
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issues[issueheads] <- 0
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tweets$issue <- ""
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tweets$tags <- ""
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tagexpand <- c("", "s", "n", "en")
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for(d in 1:nrow(issues)) {
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# Go through every day
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curdate <- issues$date[d]
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@@ -510,3 +310,203 @@ main = "Seats of parties in the parliament")
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pie(acc_parties$twitter, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"), clockwise = T,
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main = "Percentage of parties' MdBs of all Twitter accounts")
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rm(acc_parties)
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require(lubridate)
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require(XML)
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require(ggplot2)
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require(reshape2)
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require(stringr)
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source("issuecomp-functions.R")
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curchars
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curchars <- 7
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curchars >= 5 && curchars <= 7
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curchars <- 10
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curchars >= 5 && curchars <= 7
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curchars <- 4
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curchars >= 5 && curchars <= 7
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if(curchars <= 4) {
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curdistance <- 0
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}
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else if {curchars >= 5} {
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curdistance <- 1
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}
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if(curchars <= 4) {
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curdistance <- 0
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} else if {curchars >= 5} {
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curdistance <- 1
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}
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if(curchars <= 4) {
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curdistance <- 0
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} else {
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curdistance <- 1
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}
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curdistance
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source("issuecomp-functions.R")
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smartPatternMatch("bla bla Tomate bla", "tomaten", 0, F)
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smartPatternMatch("bla bla Tomate bla", "tomaten", 1, F)
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smartPatternMatch("bla bla Tomate bla", "tomatens", 1, F)
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smartPatternMatch("bla bla Tomate bla", "tomatens", 2, F)
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rm(list=ls())
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require(lubridate)
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require(XML)
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require(ggplot2)
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require(reshape2)
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require(stringr)
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source("issuecomp-functions.R")
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load(file = "tweets_untagged.RData")
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date_start <- as.Date("2014-01-01")
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date_end <- as.Date("2014-12-31")
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drange <- as.integer(date_end - date_start)
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drange <- date_start + days(0:drange)
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# MATCH TWEETS ------------------------------------------------------------
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id_folder <- "matched-ids"
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unlink(id_folder, recursive = TRUE)
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dir.create(id_folder)
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issues <- data.frame(date = drange)
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issuelist <- xmlToList("issues.xml")
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issueheads <- names(issuelist)
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issues[issueheads] <- 0
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tweets$issue <- ""
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tweets$tags <- ""
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tagexpand <- c("", "s", "n", "en")
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for(d in 1:nrow(issues)) {
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# Go through every day
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curdate <- issues$date[d]
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cat(as.character(curdate),"\n")
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# Put all tweets from specific day in a temporary DF
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tweets_curday <- tweets[tweets[, "created_at"] == curdate, ]
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for(t in 1:nrow(tweets_curday)){
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# Select tweet's text, make it lowercase and remove hashtag indicators (#)
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curtext <- as.character(tweets_curday$text[t])
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curtext <- str_replace_all(curtext, "#", "")
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curid <- as.character(tweets_curday$id_str[t])
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# Now test each single issue (not tag!)
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for(i in 1:length(issueheads)) {
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curissue <- issueheads[i]
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curtags <- as.character(issuelist[[curissue]])
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curfile <- str_c(id_folder,"/",curissue,".csv")
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# Now test all tags of a single issue
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for(s in 1:length(curtags)) {
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curtag <- curtags[s]
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curchars <- nchar(curtag, type = "chars")
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# Check if tag is an acronym. If so, ignore.case will be deactivated in smartPatternMatch
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if(curchars <= 4) {
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curacro <- checkAcronym(string = curtag, chars = curchars)
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} else {
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curacro <- FALSE
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}
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# Now expand the current tag by possible suffixes that may be plural forms
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if(!curacro) {
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for(e in 1:length(tagexpand)) {
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curtag[e] <- str_c(curtag[1], tagexpand[e])
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}
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}
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# Set Levenshtein distance depending on char length
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if(curchars <= 4) {
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curdistance <- 0
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} else {
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curdistance <- 1
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}
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# Match current tweet with tag. If >= 5 letters allow 1 changed letter, if >=8 letters allow also 1 (Levenshtein distance)
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tags_found <- NULL
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# Match the tweet with each variation of tagexpand
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for(e in 1:length(curtag)) {
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tags_found[e] <- smartPatternMatch(curtext, curtag[e], curdistance, curacro)
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}
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tags_found <- any(tags_found)
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curtag <- curtag[1]
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if(tags_found == TRUE) {
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# Raise number of findings on this day for this issue by 1
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issues[d,curissue] <- issues[d,curissue] + 1
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# Add issue and first matched tag of tweet to tweets-DF
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oldissue <- tweets[tweets[, "id_str"] == curid, "issue"]
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tweets[tweets[, "id_str"] == curid, "issue"] <- str_c(oldissue, curissue, ";")
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oldtag <- tweets[tweets[, "id_str"] == curid, "tags"]
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tweets[tweets[, "id_str"] == curid, "tags"] <- str_c(oldtag, curtag, ";")
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# Add information to file for function viewPatternMatching
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write(str_c(curdate,";\"",curid,"\";",curtag), curfile, append = TRUE)
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break
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}
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else {
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#cat("Nothing found\n")
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}
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} # /for curtags
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} # /for issuelist
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} # /for tweets_curday
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} # /for drange
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# MATCH TWEETS ------------------------------------------------------------
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id_folder <- "matched-ids"
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unlink(id_folder, recursive = TRUE)
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dir.create(id_folder)
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issues <- data.frame(date = drange)
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issuelist <- xmlToList("issues.xml")
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issueheads <- names(issuelist)
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issues[issueheads] <- 0
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tweets$issue <- ""
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tweets$tags <- ""
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tagexpand <- c("", "s", "n", "en")
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for(d in 1:nrow(issues)) {
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# Go through every day
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curdate <- issues$date[d]
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cat(as.character(curdate),"\n")
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# Put all tweets from specific day in a temporary DF
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tweets_curday <- tweets[tweets[, "created_at"] == curdate, ]
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for(t in 1:nrow(tweets_curday)){
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# Select tweet's text, make it lowercase and remove hashtag indicators (#)
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curtext <- as.character(tweets_curday$text[t])
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curtext <- str_replace_all(curtext, "#", "")
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curid <- as.character(tweets_curday$id_str[t])
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# Now test each single issue (not tag!)
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for(i in 1:length(issueheads)) {
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curissue <- issueheads[i]
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curtags <- as.character(issuelist[[curissue]])
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curfile <- str_c(id_folder,"/",curissue,".csv")
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# Now test all tags of a single issue
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for(s in 1:length(curtags)) {
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curtag <- curtags[s]
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curchars <- nchar(curtag, type = "chars")
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# Check if tag is an acronym. If so, ignore.case will be deactivated in smartPatternMatch
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if(curchars <= 4) {
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curacro <- checkAcronym(string = curtag, chars = curchars)
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} else {
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curacro <- FALSE
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}
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# Now expand the current tag by possible suffixes that may be plural forms
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if(!curacro) {
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for(e in 1:length(tagexpand)) {
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curtag[e] <- str_c(curtag[1], tagexpand[e])
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}
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}
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# Set Levenshtein distance depending on char length
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if(curchars <= 4) {
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curdistance <- 0
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} else {
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curdistance <- 1
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}
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# Match current tweet with tag. If >= 5 letters allow 1 changed letter, if >=8 letters allow also 1 (Levenshtein distance)
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tags_found <- NULL
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# Match the tweet with each variation of tagexpand
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for(e in 1:length(curtag)) {
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tags_found[e] <- smartPatternMatch(curtext, curtag[e], curdistance, curacro)
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}
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tags_found <- any(tags_found)
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curtag <- curtag[1]
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if(tags_found == TRUE) {
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# Raise number of findings on this day for this issue by 1
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issues[d,curissue] <- issues[d,curissue] + 1
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# Add issue and first matched tag of tweet to tweets-DF
|
||||
oldissue <- tweets[tweets[, "id_str"] == curid, "issue"]
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tweets[tweets[, "id_str"] == curid, "issue"] <- str_c(oldissue, curissue, ";")
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oldtag <- tweets[tweets[, "id_str"] == curid, "tags"]
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tweets[tweets[, "id_str"] == curid, "tags"] <- str_c(oldtag, curtag, ";")
|
||||
# Add information to file for function viewPatternMatching
|
||||
write(str_c(curdate,";\"",curid,"\";",curtag), curfile, append = TRUE)
|
||||
break
|
||||
}
|
||||
else {
|
||||
#cat("Nothing found\n")
|
||||
}
|
||||
} # /for curtags
|
||||
} # /for issuelist
|
||||
} # /for tweets_curday
|
||||
} # /for drange
|
||||
View(issues)
|
||||
|
||||
@@ -70,11 +70,19 @@ for(d in 1:nrow(issues)) {
|
||||
curtag[e] <- str_c(curtag[1], tagexpand[e])
|
||||
}
|
||||
}
|
||||
|
||||
# Set Levenshtein distance depending on char length
|
||||
if(curchars <= 4) {
|
||||
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)
|
||||
tags_found <- NULL
|
||||
# Match the tweet with each variation of tagexpand
|
||||
for(e in 1:length(curtag)) {
|
||||
tags_found[e] <- smartPatternMatch(curtext, curtag[e], curchars, curacro)
|
||||
tags_found[e] <- smartPatternMatch(curtext, curtag[e], curdistance, curacro)
|
||||
}
|
||||
tags_found <- any(tags_found)
|
||||
curtag <- curtag[1]
|
||||
|
||||
+19
-14
@@ -26,22 +26,27 @@ convertLogical0 <- function(var) {
|
||||
return(var)
|
||||
}
|
||||
|
||||
smartPatternMatch <- function(string, pattern, chars, acronym) {
|
||||
smartPatternMatch <- function(string, pattern, dist, acronym) {
|
||||
patternrex <- str_c("\\b", pattern, "\\b")
|
||||
|
||||
if(chars <= 4) { # 4 or less
|
||||
found <- agrep(patternrex, string, max.distance = list(all = 0), ignore.case = !acronym, fixed = FALSE)
|
||||
}
|
||||
else if(chars >= 8) { # 8 or more
|
||||
found <- agrep(patternrex, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE)
|
||||
# # Give longer words a chance by ignoring word boundaries \\b
|
||||
# if(convertLogical0(found) == 0) {
|
||||
# found <- grep(pattern, string, ignore.case = !acronym, fixed = FALSE)
|
||||
# }
|
||||
}
|
||||
else { # 5,6,7
|
||||
found <- agrep(patternrex, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE)
|
||||
}
|
||||
found <- agrep(patternrex, string, max.distance = list(all = dist), ignore.case = !acronym, fixed = FALSE)
|
||||
|
||||
# if(chars <= 4) { # 4 or less
|
||||
# found <- agrep(patternrex, string, max.distance = list(all = 0), ignore.case = !acronym, fixed = FALSE)
|
||||
# }
|
||||
# else if(chars >= 8) { # 8 or more
|
||||
# found <- agrep(patternrex, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE)
|
||||
# # # Give longer words a chance by ignoring word boundaries \\b
|
||||
# # if(convertLogical0(found) == 0) {
|
||||
# # found <- grep(pattern, string, ignore.case = !acronym, fixed = FALSE)
|
||||
# # }
|
||||
# }
|
||||
# else { # 5,6,7
|
||||
# found <- agrep(patternrex, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE)
|
||||
# }
|
||||
#
|
||||
|
||||
# Convert 0/1 to F/T
|
||||
found <- convertLogical0(found)
|
||||
if(found == 1) {
|
||||
found <- TRUE
|
||||
|
||||
Reference in New Issue
Block a user