better pattern matching logic

This commit is contained in:
2015-01-21 13:17:24 +01:00
parent a8987936c4
commit 54d1cd79aa
3 changed files with 228 additions and 215 deletions
+200 -200
View File
@@ -1,203 +1,3 @@
dir.create(id_folder)
issues <- data.frame(date = drange)
issuelist <- xmlToList("issues.xml")
issueheads <- names(issuelist)
issues[issueheads] <- 0
tweets$issue <- ""
tweets$tags <- ""
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
}
# 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) {
# 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(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)
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]
@@ -510,3 +310,203 @@ main = "Seats of parties in the parliament")
pie(acc_parties$twitter, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"), clockwise = T,
main = "Percentage of parties' MdBs of all Twitter accounts")
rm(acc_parties)
require(lubridate)
require(XML)
require(ggplot2)
require(reshape2)
require(stringr)
source("issuecomp-functions.R")
curchars
curchars <- 7
curchars >= 5 && curchars <= 7
curchars <- 10
curchars >= 5 && curchars <= 7
curchars <- 4
curchars >= 5 && curchars <= 7
if(curchars <= 4) {
curdistance <- 0
}
else if {curchars >= 5} {
curdistance <- 1
}
if(curchars <= 4) {
curdistance <- 0
} else if {curchars >= 5} {
curdistance <- 1
}
if(curchars <= 4) {
curdistance <- 0
} else {
curdistance <- 1
}
curdistance
source("issuecomp-functions.R")
smartPatternMatch("bla bla Tomate bla", "tomaten", 0, F)
smartPatternMatch("bla bla Tomate bla", "tomaten", 1, F)
smartPatternMatch("bla bla Tomate bla", "tomatens", 1, F)
smartPatternMatch("bla bla Tomate bla", "tomatens", 2, F)
rm(list=ls())
require(lubridate)
require(XML)
require(ggplot2)
require(reshape2)
require(stringr)
source("issuecomp-functions.R")
load(file = "tweets_untagged.RData")
date_start <- as.Date("2014-01-01")
date_end <- as.Date("2014-12-31")
drange <- as.integer(date_end - date_start)
drange <- date_start + days(0:drange)
# MATCH TWEETS ------------------------------------------------------------
id_folder <- "matched-ids"
unlink(id_folder, recursive = TRUE)
dir.create(id_folder)
issues <- data.frame(date = drange)
issuelist <- xmlToList("issues.xml")
issueheads <- names(issuelist)
issues[issueheads] <- 0
tweets$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])
}
}
# 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], 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,"\";",curtag), curfile, append = TRUE)
break
}
else {
#cat("Nothing found\n")
}
} # /for curtags
} # /for issuelist
} # /for tweets_curday
} # /for drange
# 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])
}
}
# 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], 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,"\";",curtag), curfile, append = TRUE)
break
}
else {
#cat("Nothing found\n")
}
} # /for curtags
} # /for issuelist
} # /for tweets_curday
} # /for drange
View(issues)
+9 -1
View File
@@ -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
View File
@@ -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