require(lubridate) require(XML) require(ggplot2) require(reshape2) require(stringr) require(foreach) require(doParallel) source("issuecomp-functions.R") setwd("~/Dokumente/Uni/Aktuell/BA-Arbeit/uni-ba-issuecomp") load(file = "tweets_untagged.RData") # Create date range date_start <- as.Date("2014-01-01") date_end <- as.Date("2014-12-31") drange <- as.integer(date_end - date_start) drange <- date_start + days(0:drange) # Import issues and prepare everything # Will only be filled after the large categorisation loop issues <- data.frame(date = drange) issuelist <- readLines("issues.xml") issuelist <- str_replace_all(string = issuelist, pattern = ".*", "") issuelist <- xmlToList(issuelist) issueheads <- names(issuelist) issues[issueheads] <- 0 tweets$issue <- "" tweets$tags <- "" # MATCH TWEETS ------------------------------------------------------------ # Create folder where all results will be saved (saver for backup and import) id_folder <- "matched-ids" unlink(id_folder, recursive = TRUE) dir.create(id_folder) # Tag expansion for plural, genetiv etc tagexpand <- c("", "s", "n", "en", "er", "e") # Parameters for parallelisation writeLines(c(""), "issuecomp-analysis.log") cl<-makeCluster(4) registerDoParallel(cl) # START CAT LOOP foreach(d = 1:nrow(issues), .packages = c("stringr"), .combine=rbind) %dopar% { # Go through every day curdate <- issues$date[d] cat(paste(as.character(curdate),"\n"), file="issuecomp-analysis.log", append=TRUE) # Put all tweets from specific day in a temporary DF tweets_curday <- tweets[tweets[, "created_at"] == curdate, ] for(t in 1:nrow(tweets_curday)){ # cat(paste("Starting tweet", t, "of",as.character(curdate),"\n"), file="issuecomp-analysis.log", append=TRUE) # Select tweet's text, make it lowercase and remove hashtag indicators (#) curtext <- as.character(tweets_curday$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 curacro <- checkAcronym(string = curtag) # Check if tag is some kind of specific hashtag. If so, do not handle as acronym, but don't expand it either if(str_detect(curtag, "^#")) { curacro <- FALSE # hashtags like #WM2014 are also written as #wm2014, so we need case-insensitivity curhash <- TRUE # But we need to mark it as hashtag, so it doesn't get extended or Levenshtein distance > 0 curtag <- str_replace(curtag, "#", "") curchars <- curchars - 1 } else { curhash <- FALSE } # Now expand the current tag by possible suffixes that may be plural forms # Only do if it isn't an acronym or specific hastag if(!curacro && !curhash) { for(e in 1:length(tagexpand)) { curtag[e] <- str_c(curtag[1], tagexpand[e]) } } # Set Levenshtein distance depending on char length, acronym and hashtag status if(curchars <= 6 || curacro || curhash) { # Distance = 1 if 7 chars or longer curdistance <- 0 } else { curdistance <- 1 } # Match current tweet with tag. # Allow 1 Levenshtein distance if tag is >= 5 letters and no hashtag or acronym # Make is case-sensitiv if tag is an acronym tags_found <- NULL # Match the tweet with each variation of tagexpand for(e in 1:length(curtag)) { tags_found[e] <- smartPatternMatch(curtext, curtag[e], curdistance, curacro) } tags_found <- 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,"\";",curissue,";",curtag), curfile, append = TRUE) # cat(paste("Match!\n"), file="issuecomp-analysis.log", append=TRUE) # data.frame(date=curdate, issue=curissue) break # next issue, no more tags from same issue } 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) stopCluster(cl) # IMPORT RESULTS ---------------------------------------------------------- # Import all files which have been generated at the categorisation run above. setwd("matched-ids/") results_files <- list.files() for(r in 1:length(results_files)) { if(r == 1) { results <- read.csv(results_files[r], sep=";", colClasses=c("character", "character", "character", "character"), header=F) } else { results_temp <- read.csv(results_files[r], sep=";", colClasses=c("character", "character", "character", "character"), header=F) results <- insertRow(results, results_temp) } } rm(r, results_temp, results_files) # Remove duplicates, sort chronologically results <- results[!duplicated(results), ] names(results) <- c("date", "id_str", "issue", "tags") results <- results[order(results$id_str), ] row.names(results) <- NULL # Now import all results in the dataframes "issues" and "tweets" # (which wasn't possible in the categorisation process because of parallelisation) # Reset issues counter # issues[issueheads] <- 0 for(r in 1:nrow(results)) { curdate <- as.character(results$date[r]) curid <- as.character(results$id_str[r]) curissue <- as.character(results$issue[r]) curtag <- as.character(results$tags[r]) cat("Sorting match", r, "of 62827 \n") # Update issue counter (date and issue) issues[issues[, "date"] == curdate, curissue] <- issues[issues[, "date"] == curdate, curissue] + 1 # Update tweet dataframe (id, issue and tags) oldissue <- tweets[tweets[, "id_str"] == curid, "issue"] tweets[tweets[, "id_str"] == curid, "issue"] <- str_c(oldissue, curissue, ",") oldtag <- tweets[tweets[, "id_str"] == curid, "tags"] tweets[tweets[, "id_str"] == curid, "tags"] <- str_c(oldtag, curtag, ",") } # SAVING ------------------------------------------------------------------ save(tweets, file="tweets_tagged.RData") write.csv(tweets, file="tweets.csv") save(issues, file="issues.RData")