better matching, now with plural forms and less distance
This commit is contained in:
@@ -1,417 +1,3 @@
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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(issuelist)) {
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curtags <- as.character(issuelist[[i]])
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curissue <- names(issuelist)[i]
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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(issues)
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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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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(issuelist)) {
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curtags <- as.character(issuelist[[i]])
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curissue <- names(issuelist)[i]
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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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#rm(tweets_curday,curacro, curchars, curdate,curfile,curid,curissue,curtag,curtags,curtext,d,date_end,date_start,i,id_folder,oldissue,oldtag,s,t,tags_found)
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View(issues)
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save(issues, "issues.RData")
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save(issues, file="issues.RData")
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readYN <- function(question) {
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n <- readline(prompt=question)
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n <- as.character(n)
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return(n)
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}
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checkIssue <- function(string, issuelist) {
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status <- any(str_detect(string, issuelist))
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return(status)
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}
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checkAllIssues <- function(string, issuelist) {
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status <- NULL
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for(i in 1:length(string)) {
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if(checkIssue(string[i], issuelist)) {
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status[i] <- TRUE
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}
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else {
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cat("Issue",string[i],"does not exist. Please try again.\n")
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status[i] <- FALSE
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}
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}
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return(status)
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}
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require(stringr)
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require(XML)
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require(stringr)
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require(XML)
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# FUNCTIONS ---------------------------------------------------------------
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readYN <- function(question) {
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n <- readline(prompt=question)
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n <- as.character(n)
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return(n)
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}
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checkIssue <- function(string, issuelist) {
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status <- any(str_detect(string, issuelist))
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return(status)
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}
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checkAllIssues <- function(string, issuelist) {
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status <- NULL
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for(i in 1:length(string)) {
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if(checkIssue(string[i], issuelist)) {
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status[i] <- TRUE
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}
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else {
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cat("Issue",string[i],"does not exist. Please try again.\n")
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status[i] <- FALSE
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}
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}
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return(status)
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}
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c_issues <- data.frame(date = drange)
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c_issuelist <- xmlToList("issues.xml")
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c_issueheads <- names(issuelist)
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c_issues[issueheads] <- 0
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source("issuecomp-codingsample-function.R")
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c_tweets <- tweets
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View(c_tweets)
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source("issuecomp-codingsample-function.R")
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smartPatternMatch("Höflich, aber klares Statement zu Menschenrechten. Der Bundespräsident macht das gut! #China #XiJinping URL ", "\\bMenschenrecht\\b", 13, FALSE)
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smartPatternMatch("Höflich, aber klares Statement zu Menschenrechten. Der Bundespräsident macht das gut! #China #XiJinping URL ", "\\bMenschenrechte\\b", 13, FALSE)
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smartPatternMatch <- function(string, pattern, chars, acronym) {
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pattern <- str_c("\\b", pattern, "\\b")
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if(chars <= 4) {
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found <- agrep(pattern, string, max.distance = list(all = 0), ignore.case = !acronym, fixed = FALSE)
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}
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else if(chars >= 8) {
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cat("bla")
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found <- agrep(pattern, string, max.distance = list(all = 2), ignore.case = !acronym, fixed = FALSE)
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}
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else {
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found <- agrep(pattern, 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("Höflich, aber klares Statement zu Menschenrechten. Der Bundespräsident macht das gut! #China #XiJinping URL ", "\\bMenschenrechte\\b", 13, FALSE)
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smartPatternMatch <- function(string, pattern, chars, acronym) {
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pattern <- str_c("\\b", pattern, "\\b")
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if(chars <= 4) {
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found <- agrep(pattern, string, max.distance = list(all = 0), ignore.case = !acronym, fixed = FALSE)
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}
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else if(chars >= 8) {
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found <- agrep(pattern, string, max.distance = list(all = 2), ignore.case = !acronym, fixed = FALSE)
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}
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else {
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found <- agrep(pattern, 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 <- function(string, pattern, chars, acronym) {
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pattern <- str_c("\\b", pattern, "\\b")
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if(chars <= 4) {
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found <- agrep(pattern, string, max.distance = list(all = 0), ignore.case = !acronym, fixed = FALSE)
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}
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else if(chars >= 8) {
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found <- agrep(pattern, string, max.distance = list(all = 3), ignore.case = !acronym, fixed = FALSE)
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}
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else {
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found <- agrep(pattern, 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("Höflich, aber klares Statement zu Menschenrechten. Der Bundespräsident macht das gut! #China #XiJinping URL ", "\\bMenschenrechte\\b", 13, FALSE)
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smartPatternMatch("Höflich, aber klares Statement zu Menschenrechten. Der Bundespräsident macht das gut! #China #XiJinping URL ", "\\bMenschenrecht\\b", 13, FALSE)
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smartPatternMatch("Höflich, aber klares Statement zu Menschenrechten. Der Bundespräsident macht das gut! #China #XiJinping URL ", "\\bMenschenracht\\b", 13, FALSE)
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smartPatternMatch("Höflich, aber klares Statement zu Menschenrechten. Der Bundespräsident macht das gut! #China #XiJinping URL ", "\\bMenschen-recht\\b", 13, FALSE)
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smartPatternMatch("Höflich, aber klares Statement zu Menschen-Rechten. Der Bundespräsident macht das gut! #China #XiJinping URL ", "\\bMenschenrecht\\b", 13, FALSE)
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smartPatternMatch("Höflich, aber klares Statement zu Menschen-Rechte. Der Bundespräsident macht das gut! #China #XiJinping URL ", "\\bMenschenrecht\\b", 13, FALSE)
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smartPatternMatch("Bla bla Tomate ", "\\Tomate\\b", 6, FALSE)
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smartPatternMatch("Bla bla Tomaten bla bla", "\\Tomate\\b", 6, FALSE)
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smartPatternMatch <- function(string, pattern, chars, acronym) {
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pattern <- str_c("\\b", pattern, "\\b")
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if(chars <= 4) {
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found <- agrep(pattern, string, max.distance = list(all = 0), ignore.case = !acronym, fixed = FALSE)
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}
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else if(chars >= 8) {
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found <- agrep(pattern, string, max.distance = list(all = 3), ignore.case = !acronym, fixed = FALSE)
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}
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else {
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found <- agrep(pattern, string, max.distance = list(all = 2), 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 Tomaten bla bla", "\\Tomate\\b", 6, FALSE)
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smartPatternMatch("Bla bla Menschen bla bla", "\\Menschen\\b", 8, FALSE)
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smartPatternMatch("Bla bla Menschen bla bla", "\\Menschen\\b", 7, FALSE)
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smartPatternMatch("Bla bla Menschen bla bla", "\\Mensch\\b", 7, FALSE)
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smartPatternMatch("Bla bla Menschen bla bla", "\\Mensch\\b", 8, FALSE)
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smartPatternMatch("Bla bla Nazis bla bla", "\\Nazis\\b", 8, FALSE)
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smartPatternMatch("Bla bla Nazis bla bla", "\\Nazis\\b", 5, FALSE)
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smartPatternMatch("Bla bla Nazis bla bla", "\\Nazi\\b", 4, FALSE)
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smartPatternMatch("Bla bla Nazi bla bla", "\\Nazis\\b", 5, FALSE)
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source("issuecomp-codingsample-function.R")
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smartPatternMatch("Der kleine Flüchtlingsjunge war", "\\bFlüchtling\\b", 9, FALSE)
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str_detect("Der kleine Flüchtlingsjunge war", pattern = "\\bFlüchtling\\b")
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str_detect("Der kleine Flüchtlingsjunge war", pattern = "Flüchtling")
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str_detect("Der kleine Flücht lingsjunge war", pattern = "Flüchtling")
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smartPatternMatch <- function(string, pattern, chars, acronym) {
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pattern <- str_c("\\b", pattern, "\\b")
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if(chars <= 4) { # 4 or less
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found <- agrep(pattern, 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(pattern, string, max.distance = list(all = 3), ignore.case = !acronym, fixed = FALSE)
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cat(found)
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}
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else { # 5,6,7
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found <- agrep(pattern, string, max.distance = list(all = 2), 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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str_detect("Der kleine Flücht lingsjunge war", pattern = "Flüchtling")
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smartPatternMatch("Der kleine Flüchtlingsjunge war", "\\bFlüchtling\\b", 9, FALSE)
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smartPatternMatch("Der kleine Flüchtlingsjunge war", "\\bFlüchtling\\b", 9, FALSE)
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smartPatternMatch <- function(string, pattern, chars, acronym) {
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pattern <- str_c("\\b", pattern, "\\b")
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if(chars <= 4) { # 4 or less
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found <- agrep(pattern, 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(pattern, string, max.distance = list(all = 3), ignore.case = !acronym, fixed = FALSE)
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cat("it's",found)
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}
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else { # 5,6,7
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found <- agrep(pattern, string, max.distance = list(all = 2), 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("Der kleine Flüchtlingsjunge war", "\\bFlüchtling\\b", 9, FALSE)
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str_detect("Der kleine Flücht lingsjunge war", pattern = "Flüchtling")
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str_detect("Der kleine Flüchtlingsjunge war", pattern = "Flüchtling")
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smartPatternMatch("Der kleine Flüchtlingsjunge war", "\\bFlüchtling\\b", 9, FALSE)
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smartPatternMatch("Der kleine Flüchtlinge war", "\\bFlüchtling\\b", 9, FALSE)
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grep("Flüchtling","Der kleine Flüchtlingsjunge war", ignore.case = TRUE, fixed=FALSE)
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grep("\\bFlüchtling\\b","Der kleine Flüchtlingsjunge war", ignore.case = TRUE, fixed=FALSE)
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grep("\\bFlüchtling\\b","Der kleine Flüchtlingsjunge war", ignore.case = TRUE, fixed=TRUE)
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grep("\\bFlüchtling\\b","Der kleine Flüchtlingsjunge war", ignore.case = TRUE, fixed=FALSE)
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grep("Flüchtling","Der kleine Flüchtlingsjunge war", ignore.case = TRUE, fixed=FALSE)
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grep("Flüchtling","Der kleine Flücht-lingsjunge war", ignore.case = TRUE, fixed=FALSE)
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grep("Flüchtling","Der kleine Flüchtlingsjunge war", ignore.case = TRUE, fixed=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 = 3), ignore.case = !acronym, fixed = FALSE)
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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 = 2), 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("Der kleine Flüchtlingsjunge war", "Flüchtling", 9, FALSE)
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smartPatternMatch("Der kleine Flüchtlingsjunge war", "Flüchtling", 9, FALSE)
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smartPatternMatch("Der kleine Flüchtlingsjunge war", "Flüchtling", 7, FALSE)
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c_errors <- read.csv("issuecomp-codingsample-error.csv", header = F, sep=",", colClasses="character")
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names(c_errors) <- c("str_id", "code", "issue", "tags", "text")
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for(r in 1:nrow(c_errors)) {
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c_errcode <- as.character(c_errors$code[r])
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c_errissue <- as.character(c_errors$issue[r])
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c_errtags <- as.character(c_errors$tags[r])
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c_errtext <- as.character(c_errors$text[r])
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c_errid <- as.character(c_errors$str_id[r])
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cat("===============\n\n[TWEET]: ",c_errtext,"\n[ISSUES]: ", c_errtags, "\n", sep="")
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source("issuecomp-codingsample-function2.R")
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}
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View(c_errors)
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viewMatchingTweets(date = "2014-05-10", issue = "agrar.204", id_folder)
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viewMatchingTweets(date = "2014-05-10", issue = "agrar.402", id_folder)
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viewMatchingTweets(date = "2014-01-10", issue = "agrar.402", id_folder)
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viewMatchingTweets(date = "2014-01-20", issue = "agrar.402", id_folder)
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viewMatchingTweets(date = "2014-01-10", issue = "agrar.403", id_folder)
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viewMatchingTweets(date = "2014-04-10", issue = "agrar.403", id_folder)
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viewMatchingTweets(date = "2014-05-10", issue = "agrar.403", id_folder)
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viewMatchingTweets(date = "2014-02-11", issue = "agrar.403", id_folder)
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viewMatchingTweets(date = "2014-08-01", issue = "agrar.403", id_folder)
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issuelist <- xmlToList("issues.xml")
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issuelist
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issuelist[[1]]
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xmlTreeParse(file = "issues.xml")
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View(issues)
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issuelist
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issueheads
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issuelist[[1]]
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issuelist2 <- xmlTreeParse(file = "issues.xml")
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issuelist2[[1]]
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issuelist2[[2]]
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issuelist2[[1,2]]
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issuelist2[1
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issuelist2[1]
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issuelist2$doc$file
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issuelist2$doc$version
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xmlParse("issues.xml")
|
||||
issuelist2 <- xmlParse("issues.xml")
|
||||
issuelist2[1]
|
||||
issuelist2[2]
|
||||
issuelist2
|
||||
issuelist
|
||||
issuelist$edu.606
|
||||
issuelist$edu.606[1]
|
||||
issuelist$edu.606[2]
|
||||
issuelist$edu.606[3]
|
||||
issueheads
|
||||
issuelist$macro.100
|
||||
length(issuelist$macro.100)
|
||||
length(issuelist$macro.101)
|
||||
length(issuelist$macro.103)
|
||||
length(issuelist$macro.105)
|
||||
issuelist$macro.105
|
||||
issuelist$macro.105[2]
|
||||
issueheads
|
||||
as.character(issuelist[[1]])
|
||||
as.character(issuelist[[2]])
|
||||
test <- issueheads[1]
|
||||
test
|
||||
as.character(issuelist$test)
|
||||
as.character(issuelist$macro.100)
|
||||
as.character(issuelist[test])
|
||||
as.character(issuelist[test,1])
|
||||
as.character(issuelist[1,test])
|
||||
as.character(issuelist[test])
|
||||
issuelist[test]
|
||||
issuelist[test]
|
||||
length(issuelist[test])
|
||||
length(issuelist$macro.100)
|
||||
issuelist$macro.100
|
||||
test
|
||||
issuelist[test]
|
||||
issuelist[,test]
|
||||
issuelist[,as.character(test)]
|
||||
issuelist[[test]]
|
||||
issuelist[,test]
|
||||
issuelist[test]
|
||||
issuelist[[test]]
|
||||
length(issuelist[[test]])
|
||||
issuelist[[test]]
|
||||
issuelist[[test]][1]
|
||||
as.character(issuelist[[test]][1])
|
||||
as.character(issuelist[[test]])
|
||||
issueheads
|
||||
issueheads[2]
|
||||
as.character(issuelist[[i]])
|
||||
as.character(issuelist[[1]])
|
||||
as.character(issuelist[[test]])
|
||||
i <- 1
|
||||
curissue <- issueheads[i]
|
||||
curtags <- as.character(issuelist[[curissue]])
|
||||
curfile <- str_c(id_folder,"/",curissue,".csv")
|
||||
curissue
|
||||
curtags
|
||||
curfile
|
||||
curtags[2]
|
||||
# MATCH TWEETS ------------------------------------------------------------
|
||||
id_folder <- "matched-ids"
|
||||
unlink(id_folder, recursive = TRUE)
|
||||
dir.create(id_folder)
|
||||
issues <- data.frame(date = drange)
|
||||
issuelist <- xmlToList("issues.xml")
|
||||
@@ -466,10 +52,142 @@ else {
|
||||
} # /for issuelist
|
||||
} # /for tweets_curday
|
||||
} # /for drange
|
||||
smartPatternMatch(string = "er ist pädophil ", pattern = "pädophilie", chars = 10, acronym = FALSE)
|
||||
smartPatternMatch(string = "er ist pädophiler ", pattern = "pädophilie", chars = 10, acronym = FALSE)
|
||||
smartPatternMatch(string = "er ist pädophiler ", pattern = "Pädophilie", chars = 10, acronym = FALSE)
|
||||
smartPatternMatch(string = "er ist pädophiles ", pattern = "Pädophilie", chars = 10, acronym = FALSE)
|
||||
View(tweets)
|
||||
require(lubridate)
|
||||
require(XML)
|
||||
require(ggplot2)
|
||||
require(reshape2)
|
||||
require(stringr)
|
||||
smartPatternMatch("bla bla Matching bla bla", "matching", 8, FALSE)
|
||||
smartPatternMatch("bla bla Matching bla bla", "mating", 8, FALSE)
|
||||
source("issuecomp-functions.R")
|
||||
smartPatternMatch("bla bla Matching bla bla", "mating", 8, FALSE)
|
||||
test <- c("matching", "matccing", "matxxing")
|
||||
smartPatternMatch("bla bla Matching bla bla", "matching", 8, FALSE)
|
||||
smartPatternMatch("bla bla Matching bla bla", "matccing", 8, FALSE)
|
||||
smartPatternMatch <- function(string, pattern, chars, 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 <- convertLogical0(found)
|
||||
return(found)
|
||||
}
|
||||
smartPatternMatch("bla bla Matching bla bla", "matccing", 8, FALSE)
|
||||
smartPatternMatch("bla bla Matching bla bla", "matxxing", 8, FALSE)
|
||||
smartPatternMatch("bla bla Matching bla bla", sprintf(), 8, FALSE)
|
||||
sprintf("%s", test)
|
||||
smartPatternMatch("bla bla Matching bla bla", sprintf("%s", test), 8, FALSE)
|
||||
for(i in 1:length(test)) { smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE)}
|
||||
for(i in 1:length(test)) { cat(smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))}
|
||||
for(i in 1:length(test)) { tags_found[i] (smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))}
|
||||
for(i in 1:length(test)) { tags_found[i] <- (smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))}
|
||||
tags_found
|
||||
length(tags_found)
|
||||
any(tags_found)
|
||||
smartPatternMatch <- function(string, pattern, chars, 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 <- convertLogical0(found)
|
||||
if(found == 1) {
|
||||
found <- TRUE
|
||||
} else {
|
||||
found <- FALSE
|
||||
}
|
||||
return(found)
|
||||
}
|
||||
for(i in 1:length(test)) { tags_found[i] <- (smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))}
|
||||
any(tags_found)
|
||||
tags_found <- NULL
|
||||
rm(tags_found)
|
||||
for(i in 1:length(test)) { tags_found[i] <- (smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))}
|
||||
tags_found <- NULL
|
||||
for(i in 1:length(test)) { tags_found[i] <- (smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))}
|
||||
tags_found <- NULL
|
||||
for(i in 1:length(test)) { tags_found[i] <- (smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))}
|
||||
any(tags_found)
|
||||
curtag
|
||||
tagexpand <- c("s", "n", "en")
|
||||
curtag
|
||||
curtag[2] <- "bla"
|
||||
curtag
|
||||
curtag[2] <- NULL
|
||||
curtag[2] <- ""
|
||||
curtag
|
||||
rm(curtag[2])
|
||||
curtag <- "Tomate"
|
||||
for(e in 1:length(tagexpand)) {
|
||||
curtag[e] <- str_c(curtag[e], tagexpand[e])
|
||||
}
|
||||
curtag
|
||||
for(e in 1:length(tagexpand)) {
|
||||
curtag[e] <- str_c(curtag, tagexpand[e])
|
||||
}
|
||||
curtag <- "Tomate"
|
||||
for(e in 1:length(tagexpand)) {
|
||||
curtag[e] <- str_c(curtag, tagexpand[e])
|
||||
}
|
||||
curtag
|
||||
curtag <- "Tomate"
|
||||
for(e in 1:length(tagexpand)) {
|
||||
curtag[e] <- str_c(curtag[1], tagexpand[e])
|
||||
}
|
||||
curtag
|
||||
tagexpand <- c("", "s", "n", "en")
|
||||
for(e in 1:length(tagexpand)) {
|
||||
curtag[e] <- str_c(curtag[1], tagexpand[e])
|
||||
}
|
||||
curtag <- "Tomate"
|
||||
for(e in 1:length(tagexpand)) {
|
||||
curtag[e] <- str_c(curtag[1], tagexpand[e])
|
||||
}
|
||||
curtag
|
||||
smartPatternMatch <- function(string, pattern, chars, 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 <- convertLogical0(found)
|
||||
if(found == 1) {
|
||||
found <- TRUE
|
||||
} else {
|
||||
found <- FALSE
|
||||
}
|
||||
return(found)
|
||||
}
|
||||
# MATCH TWEETS ------------------------------------------------------------
|
||||
id_folder <- "matched-ids"
|
||||
unlink(id_folder, recursive = TRUE)
|
||||
dir.create(id_folder)
|
||||
@@ -479,34 +197,316 @@ issueheads <- names(issuelist)
|
||||
issues[issueheads] <- 0
|
||||
tweets$issue <- ""
|
||||
tweets$tags <- ""
|
||||
issueheads
|
||||
issuelist <- xmlToList("issues.xml")
|
||||
issuelist
|
||||
issueheads
|
||||
View(issues)
|
||||
issuelist$text
|
||||
issuelist$macro.100
|
||||
issuelist$macro.101
|
||||
issuelist$text
|
||||
issuelist$text <- NULL
|
||||
issueheads <- names(issuelist)
|
||||
issueheads
|
||||
issuelist
|
||||
issuelist$text <- ""
|
||||
issuelist
|
||||
issuelist$text <- NA
|
||||
issuelist
|
||||
issuelist$text
|
||||
issuelist$text[1]
|
||||
issuelist$text[2]
|
||||
issuelist$text[6]
|
||||
issuelist$text[10]
|
||||
issues <- data.frame(date = drange)
|
||||
issuelist <- xmlToList("issues.xml")
|
||||
tagexpand <- c("", "s", "n", "en")
|
||||
for(d in 1:nrow(issues)) {
|
||||
# Go through every day
|
||||
curdate <- issues$date[d]
|
||||
cat(as.character(curdate),"\n")
|
||||
# Put all tweets from specific day in a temporary DF
|
||||
tweets_curday <- tweets[tweets[, "created_at"] == curdate, ]
|
||||
for(t in 1:nrow(tweets_curday)){
|
||||
# Select tweet's text, make it lowercase and remove hashtag indicators (#)
|
||||
curtext <- as.character(tweets_curday$text[t])
|
||||
curtext <- str_replace_all(curtext, "#", "")
|
||||
curid <- as.character(tweets_curday$id_str[t])
|
||||
# Now test each single issue (not tag!)
|
||||
for(i in 1:length(issueheads)) {
|
||||
curissue <- issueheads[i]
|
||||
curtags <- as.character(issuelist[[curissue]])
|
||||
curfile <- str_c(id_folder,"/",curissue,".csv")
|
||||
# Now test all tags of a single issue
|
||||
for(s in 1:length(curtags)) {
|
||||
curtag <- curtags[s]
|
||||
curchars <- nchar(curtag, type = "chars")
|
||||
# Check if tag is an acronym. If so, ignore.case will be deactivated in smartPatternMatch
|
||||
if(curchars <= 4) {
|
||||
curacro <- checkAcronym(string = curtag, chars = curchars)
|
||||
} else {
|
||||
curacro <- FALSE
|
||||
}
|
||||
# Now expand the current tag by possible suffixes that may be plural forms
|
||||
if(!curacro) {
|
||||
for(e in 1:length(tagexpand)) {
|
||||
curtag[e] <- str_c(curtag[1], tagexpand[e])
|
||||
}
|
||||
}
|
||||
# Match current tweet with tag. If >= 5 letters allow 1 changed letter, if >=8 letters allow also 1 (Levenshtein distance)
|
||||
tags_found <- NULL
|
||||
tags_found <- smartPatternMatch(curtext, curtag, curchars, curacro)
|
||||
if(tags_found == 1) {
|
||||
# Raise number of findings on this day for this issue by 1
|
||||
issues[d,curissue] <- issues[d,curissue] + 1
|
||||
# Add issue and first matched tag of tweet to tweets-DF
|
||||
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, ";")
|
||||
# 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
|
||||
#rm(tweets_curday,curacro, curchars, curdate,curfile,curid,curissue,curtag,curtags,curtext,d,date_end,date_start,i,id_folder,oldissue,oldtag,s,t,tags_found)
|
||||
warnings()
|
||||
tags_found <- NULL
|
||||
for(e in 1:length(curtag)) {
|
||||
tags_found[e] <- smartPatternMatch(curtext, curtag[e], curchars, curacro)
|
||||
}
|
||||
tags_found
|
||||
curtext
|
||||
curtag
|
||||
any(tags_found)
|
||||
# MATCH TWEETS ------------------------------------------------------------
|
||||
id_folder <- "matched-ids"
|
||||
unlink(id_folder, recursive = TRUE)
|
||||
dir.create(id_folder)
|
||||
issues <- data.frame(date = drange)
|
||||
issuelist <- xmlToList("issues.xml")
|
||||
issueheads <- names(issuelist)
|
||||
issues[issueheads] <- 0
|
||||
tweets$issue <- ""
|
||||
tweets$tags <- ""
|
||||
tagexpand <- c("", "s", "n", "en")
|
||||
for(d in 1:nrow(issues)) {
|
||||
# Go through every day
|
||||
curdate <- issues$date[d]
|
||||
cat(as.character(curdate),"\n")
|
||||
# Put all tweets from specific day in a temporary DF
|
||||
tweets_curday <- tweets[tweets[, "created_at"] == curdate, ]
|
||||
for(t in 1:nrow(tweets_curday)){
|
||||
# Select tweet's text, make it lowercase and remove hashtag indicators (#)
|
||||
curtext <- as.character(tweets_curday$text[t])
|
||||
curtext <- str_replace_all(curtext, "#", "")
|
||||
curid <- as.character(tweets_curday$id_str[t])
|
||||
# Now test each single issue (not tag!)
|
||||
for(i in 1:length(issueheads)) {
|
||||
curissue <- issueheads[i]
|
||||
curtags <- as.character(issuelist[[curissue]])
|
||||
curfile <- str_c(id_folder,"/",curissue,".csv")
|
||||
# Now test all tags of a single issue
|
||||
for(s in 1:length(curtags)) {
|
||||
curtag <- curtags[s]
|
||||
curchars <- nchar(curtag, type = "chars")
|
||||
# Check if tag is an acronym. If so, ignore.case will be deactivated in smartPatternMatch
|
||||
if(curchars <= 4) {
|
||||
curacro <- checkAcronym(string = curtag, chars = curchars)
|
||||
} else {
|
||||
curacro <- FALSE
|
||||
}
|
||||
# Now expand the current tag by possible suffixes that may be plural forms
|
||||
if(!curacro) {
|
||||
for(e in 1:length(tagexpand)) {
|
||||
curtag[e] <- str_c(curtag[1], tagexpand[e])
|
||||
}
|
||||
}
|
||||
# Match current tweet with tag. If >= 5 letters allow 1 changed letter, if >=8 letters allow also 1 (Levenshtein distance)
|
||||
tags_found <- NULL
|
||||
for(e in 1:length(curtag)) {
|
||||
tags_found[e] <- smartPatternMatch(curtext, curtag[e], curchars, curacro)
|
||||
}
|
||||
tags_found <- any(tags_found)
|
||||
if(tags_found == TRUE) {
|
||||
# Raise number of findings on this day for this issue by 1
|
||||
issues[d,curissue] <- issues[d,curissue] + 1
|
||||
# Add issue and first matched tag of tweet to tweets-DF
|
||||
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, ";")
|
||||
# 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
|
||||
#rm(tweets_curday,curacro, curchars, curdate,curfile,curid,curissue,curtag,curtags,curtext,d,date_end,date_start,i,id_folder,oldissue,oldtag,s,t,tags_found)
|
||||
curtag
|
||||
curtag <- curtag[1]
|
||||
curtag
|
||||
# MATCH TWEETS ------------------------------------------------------------
|
||||
id_folder <- "matched-ids"
|
||||
unlink(id_folder, recursive = TRUE)
|
||||
dir.create(id_folder)
|
||||
issues <- data.frame(date = drange)
|
||||
issuelist <- xmlToList("issues.xml")
|
||||
issueheads <- names(issuelist)
|
||||
issues[issueheads] <- 0
|
||||
tweets$issue <- ""
|
||||
tweets$tags <- ""
|
||||
tagexpand <- c("", "s", "n", "en")
|
||||
for(d in 1:nrow(issues)) {
|
||||
# Go through every day
|
||||
curdate <- issues$date[d]
|
||||
cat(as.character(curdate),"\n")
|
||||
# Put all tweets from specific day in a temporary DF
|
||||
tweets_curday <- tweets[tweets[, "created_at"] == curdate, ]
|
||||
for(t in 1:nrow(tweets_curday)){
|
||||
# Select tweet's text, make it lowercase and remove hashtag indicators (#)
|
||||
curtext <- as.character(tweets_curday$text[t])
|
||||
curtext <- str_replace_all(curtext, "#", "")
|
||||
curid <- as.character(tweets_curday$id_str[t])
|
||||
# Now test each single issue (not tag!)
|
||||
for(i in 1:length(issueheads)) {
|
||||
curissue <- issueheads[i]
|
||||
curtags <- as.character(issuelist[[curissue]])
|
||||
curfile <- str_c(id_folder,"/",curissue,".csv")
|
||||
# Now test all tags of a single issue
|
||||
for(s in 1:length(curtags)) {
|
||||
curtag <- curtags[s]
|
||||
curchars <- nchar(curtag, type = "chars")
|
||||
# Check if tag is an acronym. If so, ignore.case will be deactivated in smartPatternMatch
|
||||
if(curchars <= 4) {
|
||||
curacro <- checkAcronym(string = curtag, chars = curchars)
|
||||
} else {
|
||||
curacro <- FALSE
|
||||
}
|
||||
# Now expand the current tag by possible suffixes that may be plural forms
|
||||
if(!curacro) {
|
||||
for(e in 1:length(tagexpand)) {
|
||||
curtag[e] <- str_c(curtag[1], tagexpand[e])
|
||||
}
|
||||
}
|
||||
# Match current tweet with tag. If >= 5 letters allow 1 changed letter, if >=8 letters allow also 1 (Levenshtein distance)
|
||||
tags_found <- NULL
|
||||
for(e in 1:length(curtag)) {
|
||||
tags_found[e] <- smartPatternMatch(curtext, curtag[e], curchars, curacro)
|
||||
}
|
||||
tags_found <- any(tags_found)
|
||||
curtag <- curtag[1]
|
||||
if(tags_found == TRUE) {
|
||||
# Raise number of findings on this day for this issue by 1
|
||||
issues[d,curissue] <- issues[d,curissue] + 1
|
||||
# Add issue and first matched tag of tweet to tweets-DF
|
||||
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, ";")
|
||||
# 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(tweets)
|
||||
require(jsonlite)
|
||||
require(stringr)
|
||||
require(devtools)
|
||||
require(RTwitterAPI)
|
||||
acc_df <- read.csv("MdB-twitter.csv")
|
||||
delrow <- NULL
|
||||
for(r in 1:nrow(acc_df)) {
|
||||
acc <- as.character(acc_df$twitter_acc[r])
|
||||
if(!nzchar(acc)) {
|
||||
delrow <- c(delrow, r)
|
||||
}
|
||||
}
|
||||
acc_df <- acc_df[-delrow, ]
|
||||
rm(delrow, r, acc)
|
||||
acc_df$row.names <- NULL
|
||||
row.names(acc_df) <- NULL
|
||||
View(acc_df)
|
||||
acc_df(acc_df$party == "linke")
|
||||
acc_df[acc_df$party == "linke"]
|
||||
acc_df[, acc_df$party == "linke"]
|
||||
acc_df[acc_df$party == "linke", ]
|
||||
length(acc_df[acc_df$party == "linke", ])
|
||||
nrow(acc_df[acc_df$party == "linke", ])
|
||||
nrow(acc_df[acc_df$party == "linke", ]) / 280
|
||||
nrow(acc_df[acc_df$party == "gruene", ]) / 280
|
||||
nrow(acc_df[acc_df$party == "cducsu", ]) / 280
|
||||
nrow(acc_df[acc_df$party == "spd", ]) / 280
|
||||
test <- c("linke", "gruene")
|
||||
nrow(acc_df[acc_df$party == sprintf("%s", test), ]) / 280
|
||||
test
|
||||
nrow(acc_df[acc_df$party == sprintf("%s", test), ]) / 280
|
||||
acc_parties <- c("cducsu", "spd", "linke", "gruene")
|
||||
acc_parties <- data.frame(party = c("cducsu", "spd", "linke", "gruene"))
|
||||
View(acc_parties)
|
||||
acc_parties$btw13 <- c(41.5, 25.7, 8.6, 8.4)
|
||||
View(acc_parties)
|
||||
acc_parties$twitter <- 0
|
||||
View(acc_parties)
|
||||
for(p in 1:length(acc_parties)) {
|
||||
acc_parties$twitter[p] <- as.numeric(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280)
|
||||
}
|
||||
View(acc_parties)
|
||||
as.numeric(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280 * 100)
|
||||
round(14.64282, digits = 1)
|
||||
round(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280), digits=1)
|
||||
nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280)
|
||||
nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280
|
||||
nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280 * 100
|
||||
round(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280 * 100)
|
||||
round(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280 * 100, digits=1)
|
||||
for(p in 1:length(acc_parties)) {
|
||||
acc_parties$twitter[p] <- round(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280 * 100, digits=1)
|
||||
}
|
||||
View(acc_parties)
|
||||
View(acc_parties)
|
||||
acc_parties$twitter <- 0
|
||||
for(p in 1:length(acc_parties)) {
|
||||
acc_parties$twitter[p] <- round(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280 * 100, digits=1)
|
||||
}
|
||||
View(acc_parties)
|
||||
nrow(acc_df[acc_df$party == "gruene", ]) / 280
|
||||
as.character(acc_parties$party[4])
|
||||
acc_parties <- data.frame(party = c("cducsu", "spd", "linke", "gruene"))
|
||||
acc_parties$btw13 <- c(41.5, 25.7, 8.6, 8.4)
|
||||
acc_parties$twitter <- 0
|
||||
for(p in 1:length(acc_parties)) {
|
||||
acc_parties$twitter[p] <- round(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280 * 100)
|
||||
}
|
||||
View(acc_parties)
|
||||
round(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280 * 100)
|
||||
p
|
||||
acc_parties
|
||||
acc_parties <- data.frame(party = c("cducsu", "spd", "linke", "gruene"))
|
||||
acc_parties$btw13 <- c(41.5, 25.7, 8.6, 8.4)
|
||||
acc_parties$twitter <- 0
|
||||
for(p in 1:nrow(acc_parties)) {
|
||||
acc_parties$twitter[p] <- round(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280 * 100)
|
||||
}
|
||||
View(acc_parties)
|
||||
acc_parties <- data.frame(party = c("cducsu", "spd", "linke", "gruene"))
|
||||
acc_parties$btw13 <- c(49.3, 30.6, 10.1, 10.0)
|
||||
acc_parties$twitter <- 0
|
||||
for(p in 1:nrow(acc_parties)) {
|
||||
acc_parties$twitter[p] <- round(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280 * 100)
|
||||
}
|
||||
View(acc_parties)
|
||||
pie(acc_parties$btw13)
|
||||
pie(acc_parties$btw13, col=c("black", "red", "purple", "green"))
|
||||
pie(acc_parties$btw13, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"))
|
||||
pie(acc_parties$twitter, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"))
|
||||
pie(acc_parties$twitter, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"), clockwise = T)
|
||||
pie(acc_btw13$twitter, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"), clockwise = T)
|
||||
pie(acc_parties$btw13, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"), clockwise = T)
|
||||
acc_parties <- data.frame(party = c("cducsu", "spd", "linke", "gruene"))
|
||||
acc_parties$btw13 <- c(49.3, 30.6, 10.1, 10.0) # seats of party / 631 seats
|
||||
acc_parties$twitter <- 0
|
||||
for(p in 1:nrow(acc_parties)) {
|
||||
acc_parties$twitter[p] <- round(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280 * 100)
|
||||
}
|
||||
pie(acc_parties$btw13, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"), clockwise = T)
|
||||
pie(acc_parties$twitter, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"), clockwise = T)
|
||||
pie(acc_parties$btw13, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"), clockwise = T,
|
||||
main = "Seats of parties in the parliament")
|
||||
pie(acc_parties$twitter, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"), clockwise = T,
|
||||
main = "Percentage of parties' MdBs of all Twitter accounts")
|
||||
rm(acc_parties)
|
||||
|
||||
+34
-3
@@ -29,6 +29,8 @@ issues[issueheads] <- 0
|
||||
tweets$issue <- ""
|
||||
tweets$tags <- ""
|
||||
|
||||
tagexpand <- c("", "s", "n", "en")
|
||||
|
||||
for(d in 1:nrow(issues)) {
|
||||
# Go through every day
|
||||
curdate <- issues$date[d]
|
||||
@@ -61,10 +63,23 @@ for(d in 1:nrow(issues)) {
|
||||
} else {
|
||||
curacro <- FALSE
|
||||
}
|
||||
|
||||
# Now expand the current tag by possible suffixes that may be plural forms
|
||||
if(!curacro) {
|
||||
for(e in 1:length(tagexpand)) {
|
||||
curtag[e] <- str_c(curtag[1], tagexpand[e])
|
||||
}
|
||||
}
|
||||
|
||||
# Match current tweet with tag. If >= 5 letters allow 1 changed letter, if >=8 letters allow 2 (Levenshtein distance)
|
||||
tags_found <- smartPatternMatch(curtext, curtag, curchars, curacro)
|
||||
if(tags_found == 1) {
|
||||
# Match current tweet with tag. If >= 5 letters allow 1 changed letter, if >=8 letters allow also 1 (Levenshtein distance)
|
||||
tags_found <- NULL
|
||||
for(e in 1:length(curtag)) {
|
||||
tags_found[e] <- smartPatternMatch(curtext, curtag[e], curchars, curacro)
|
||||
}
|
||||
tags_found <- any(tags_found)
|
||||
curtag <- curtag[1]
|
||||
|
||||
if(tags_found == TRUE) {
|
||||
# Raise number of findings on this day for this issue by 1
|
||||
issues[d,curissue] <- issues[d,curissue] + 1
|
||||
|
||||
@@ -117,6 +132,22 @@ g1
|
||||
|
||||
rm(g1, r)
|
||||
|
||||
|
||||
# Show party percentage of twitter users
|
||||
acc_parties <- data.frame(party = c("cducsu", "spd", "linke", "gruene"))
|
||||
acc_parties$btw13 <- c(49.3, 30.6, 10.1, 10.0) # seats of party / 631 seats
|
||||
acc_parties$twitter <- 0
|
||||
for(p in 1:nrow(acc_parties)) {
|
||||
acc_parties$twitter[p] <- round(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280 * 100)
|
||||
}
|
||||
pie(acc_parties$btw13, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"), clockwise = T,
|
||||
main = "Seats of parties in the parliament")
|
||||
pie(acc_parties$twitter, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"), clockwise = T,
|
||||
main = "Percentage of parties' MdBs of all Twitter accounts")
|
||||
|
||||
rm(acc_parties, p)
|
||||
|
||||
|
||||
# VISUALS -----------------------------------------------------------------
|
||||
|
||||
|
||||
|
||||
+11
-5
@@ -33,16 +33,22 @@ smartPatternMatch <- function(string, pattern, chars, acronym) {
|
||||
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 = 2), 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)
|
||||
}
|
||||
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 <- convertLogical0(found)
|
||||
if(found == 1) {
|
||||
found <- TRUE
|
||||
} else {
|
||||
found <- FALSE
|
||||
}
|
||||
|
||||
return(found)
|
||||
}
|
||||
|
||||
|
||||
+1
-2
@@ -24,7 +24,7 @@
|
||||
<macro.105>
|
||||
<tag>Staatsverschuldung</tag>
|
||||
<tag>Schuldenquote</tag>
|
||||
<tag>Haushaltskürzungen</tag>
|
||||
<tag>Haushaltskürzung</tag>
|
||||
<tag>Staatsdefizit</tag>
|
||||
<tag>Finanzpolitik</tag>
|
||||
<tag>Haushaltspolitik</tag>
|
||||
@@ -95,7 +95,6 @@
|
||||
</civil.208>
|
||||
<civil.209>
|
||||
<tag>Extremismus</tag>
|
||||
<tag>Spione</tag>
|
||||
<tag>Spion</tag>
|
||||
<tag>linksradikal</tag>
|
||||
<tag>rechtsradikal</tag>
|
||||
|
||||
Reference in New Issue
Block a user