» DHWI: R Code Day Four Matthew L. Jockers

###############################################################
# mjockers unl edu
# The Day in Code--DHWI Text Analysis with R. 
# Day 4
###############################################################
# Today we pick up where we left of yesterday. . . 

# Review how mapply and xtabs work from the end of the day yesterday.

###############################################################
# Here is the important code from yesterday again. . . 
inputDir<-"data/XMLAuthorCorpus"
files<-dir(path=inputDir, pattern=".*xml")
library(XML)
source("code/corpusFunctions.r")
book.list.freqs<-list()
for(i in 1:length(files)){
  doc<-xmlTreeParse(file.path(inputDir, files[i]), useInternalNodes=TRUE)
  worddata<-getWordLists(doc)
  book.list.freqs[[files[i]]]<-worddata
}
freqs.list<-mapply(data.frame, ID=seq_along(book.list.freqs), book.list.freqs, SIMPLIFY=FALSE, MoreArgs=list(stringsAsFactors=FALSE))
freqs.df<-do.call(rbind, freqs.list)
result<-xtabs(Freq ~ ID+Var1, data=freqs.df)
final.m<-apply(result, 2, as.numeric)

freqs.list <- mapply(data.frame, ID=seq_along(book.list.freqs), book.list.freqs, SIMPLIFY=FALSE, MoreArgs=list(stringsAsFactors=FALSE))
freqs.df <- do.call(rbind,freqs.list)
result <- xtabs(Freq ~ ID+Var1, data= freqs.df)

# End of yesterday's code
###############################################################
# now continue with clustering. . . 

smaller.m <- final.m[,apply(final.m,2,mean)>=.25]
dm<-dist(smaller.m) # Creates a distance object
cluster <- hclust(dm) # Performs a cluster analysis on the distance object
cluster$labels<-names(book.list.freqs) #get the book file names to use as lables.
plot(cluster) # plots the results as a dendrogram for our inspection.
# OR in one line
plot(hclust(dist( final.m[,apply(final.m,2,mean)>=2.5] )), labels<-names(book.list.freqs))

##############################################################
# Classification
# A Small Authorship Classification Experiment
##############################################################
# Don't forget to clear workspace and then reset setwd()

library(XML)
source("code/corpusFunctions.r")
inputDir<-"data/XMLAuthorCorpus"
files<-dir(path=inputDir, pattern=".*xml")
book.list.freqs<-list()
for(i in 1:length(files)){
  doc<-xmlTreeParse(file.path(inputDir, files[i]), useInternalNodes=TRUE)
  chunkdata<- getWordSegmentLists(doc,10)
  book.list.freqs[[files[i]]]<- chunkdata
}
freqs.list <- lapply(book.list.freqs, my.mapply)
freqs.df <- do.call(rbind,freqs.list)
dim(freqs.df)
head(freqs.df)

#eg
bookids<-gsub("\\..*", "", "anonymous.xml.1")
bookids<-gsub("\\..*", "", rownames(freqs.df))
book.chunk.ids<-paste(bookids, freqs.df$ID, sep="_")
freqs.df$ID<-book.chunk.ids
result <- xtabs(Freq ~ ID+Var1, data=freqs.df)
final<-as.data.frame.matrix(result) #convert to data.frame

# Need to create metadata columns from text-id column
metacols<-do.call(rbind, strsplit(rownames(final), "_"))
colnames(metacols)<-c("sampletext", "samplechunk")
author<-gsub("\\d.*", "", metacols[,"sampletext"])
final.df<-cbind(author, metacols, final)

#Winnow the data
freq.means<-colMeans(final.df[,4:ncol(final.df)])
keepers<-which(freq.means >=.005)
smaller.df<-final.df[, names(keepers)]
smaller.df<-cbind(author, metacols, smaller.df)

# Run SVM classification
library(e1071)
anon<-which(smaller.df$author == "anonymous")
train <- smaller.df[-anon,4:ncol(smaller.df)]
class <- smaller.df[-anon,"author"]
model <- svm(train, class)
pred <- predict(model, train) #same as pred <- fitted(model)
cm<-table(pred, class)
testdata <- smaller.df[anon,4:ncol(smaller.df)]
final.result<-predict (model, testdata)
as.data.frame(final.result)

###############################################################################
#                               Topic Modeling
###############################################################################
# Two options outlined below.  Choose one of the two option and then 
# skip down to section of code where the lda model gets run
###############################################################################

# OPTION ONE: Using a stop List
# Returns topics with lots of character names

inputDir<-"data/XMLAuthorCorpus"
files<-dir(path=inputDir, pattern=".*xml")
chunk.size<-1000 #number of words per chunk
stoplist<-read.csv("data/stoplist.csv", stringsAsFactors=F)
library(XML)

topic.df<-NULL
for(i in 1:length(files)){
  doc<-xmlTreeParse(file.path(inputDir, files[i]), useInternalNodes=TRUE)
  paras<-getNodeSet(doc, "/TEI/text/body//p")
  words<-paste(sapply(paras,xmlValue), collapse=" ")
  words.lower <-tolower(words)
  words.lower<-gsub("[^[:alnum:][:space:]']", " ", words.lower)#replace all punc except apostrope and replace with space character
  words.list<-strsplit(words.lower, "\\s+") #split on one or more spaces
  words.unlist<-unlist(words.list)
  word.vector<-words.unlist[-which(words.unlist %in% stoplist[,1])]
  num.chunks<-length(word.vector)/chunk.size
  max<-length(word.vector)/num.chunks
  x <- seq_along(word.vector)
  chunks <- split(word.vector, ceiling(x/max))
  #deal with small chunks at the end
  if(length(chunks[[length(chunks)]]) <= 500){
    chunks[[length(chunks)-1]]<-c(chunks[[length(chunks)-1]], chunks[[length(chunks)]])
    chunks[[length(chunks)]]<-NULL
  }
  chunks<-lapply(chunks, paste, collapse=" ")
  chunks.df<-do.call(rbind, chunks)
  chunks.df<-cbind(files[i],chunks.df)
  topic.df<-rbind(topic.df, chunks.df)
}

# Now skip to "Running the model" below


###############################################################################
# OPTION TWO: Requires POS tagging.  
# With this corpus total tagging time = ~2.5 hours
# Here is the code I used to tag the files
# library(openNLP)
# for(i in 1:length(files)){
#  doc<-xmlTreeParse(file.path(inputDir, files[i]), useInternalNodes=TRUE)
#  paras<-getNodeSet(doc, "/TEI/text/body//p")
#  words<-paste(sapply(paras,xmlValue), collapse=" ")
#  tagged_text <- tagPOS(words) 
#  write(tagged_text, paste("data/taggedCorpus/",files[i], ".txt", sep=""))
# }

inputDir<-"data/taggedCorpus"
files<-dir(path=inputDir, pattern=".*xml")
chunk.size<-1000 #number of words per chunk
source("code/corpusFunctions.r")
topic.df<-NULL
for(i in 1:length(files)){
  tagged_text<-scan(file.path(inputDir, files[i]), what="character", sep="\n")
  tagged_words <- SplitText(tagged_text)
  tagged_words <- c(SelectTaggedWords(tagged_words,"/NN$")) # SELECT POS TAG TYPES TO KEEP 
  tagged_words_less <- RemoveTags(tagged_words)
  word.vector <- RemoveNonChar(tagged_words_less)
  num.chunks<-length(word.vector)/chunk.size
  max<-length(word.vector)/num.chunks
  x <- seq_along(word.vector)
  chunks <- split(word.vector, ceiling(x/max))
  #deal with small chunks at the end
  if(length(chunks[[length(chunks)]]) <= 500){
    chunks[[length(chunks)-1]]<-c(chunks[[length(chunks)-1]], chunks[[length(chunks)]])
    chunks[[length(chunks)]]<-NULL
  }
  chunks<-lapply(chunks, paste, collapse=" ")
  chunks.df<-do.call(rbind, chunks)
  chunks.df<-cbind(files[i],chunks.df)
  topic.df<-rbind(topic.df, chunks.df)
}
# Now skip to "Running the model" below

##########################################################################################
#                     RUNNING THE TOPIC MODEL
##########################################################################################

library(lda)
doclines<-lexicalize(topic.df[,2]) 
set.seed(8675309)
K <- 40
num.iterations<-250
result <- lda.collapsed.gibbs.sampler(doclines$documents, K, doclines$vocab, num.iterations, 0.1, 0.1, compute.log.likelihood=TRUE)
top.words <- top.topic.words(result$topics, 25, by.score=TRUE)
topic.proportions <- t(result$document_sums) / colSums(result$document_sums)

# DONE. . . Naw, let's make some purdy clouds. . . .

##########################################################################################
# MAKE SOME WORD CLOUDS OF THE TOPIC MODEL
##########################################################################################
library(wordcloud)
result.dir<-"topicClouds/"
for(i in 1:nrow(result$topics)){
  cloud.data<-sort(result$topics[i,], decreasing=T)[1:50]
  pdf(paste(result.dir, i,".pdf", sep=""), paper="special")  
  print(wordcloud(names(cloud.data), freq=cloud.data, scale=c(3,.10), min.freq=1, rot.per=0, random.order=F))
  dev.off()
}

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

###############################################################

# mjockers unl edu

# The Day in Code--DHWI Text Analysis with R.

# Day 4

###############################################################

# Today we pick up where we left of yesterday. . .

# Review how mapply and xtabs work from the end of the day yesterday.

###############################################################

# Here is the important code from yesterday again. . .

inputDir<-"data/XMLAuthorCorpus"

files<-dir(path=inputDir, pattern=".*xml")

library(XML)

source("code/corpusFunctions.r")

book.list.freqs<-list()

for(i in 1:length(files)){

doc<-xmlTreeParse(file.path(inputDir, files[i]), useInternalNodes=TRUE)

worddata<-getWordLists(doc)

book.list.freqs[[files[i]]]<-worddata

}

freqs.list<-mapply(data.frame, ID=seq_along(book.list.freqs), book.list.freqs, SIMPLIFY=FALSE, MoreArgs=list(stringsAsFactors=FALSE))

freqs.df<-do.call(rbind, freqs.list)

result<-xtabs(Freq ~ ID+Var1, data=freqs.df)

final.m<-apply(result, 2, as.numeric)

freqs.list <- mapply(data.frame, ID=seq_along(book.list.freqs), book.list.freqs, SIMPLIFY=FALSE, MoreArgs=list(stringsAsFactors=FALSE))

freqs.df <- do.call(rbind,freqs.list)

result <- xtabs(Freq ~ ID+Var1, data= freqs.df)

# End of yesterday's code

###############################################################

# now continue with clustering. . .

smaller.m <- final.m[,apply(final.m,2,mean)>=.25]

dm<-dist(smaller.m) # Creates a distance object

cluster <- hclust(dm) # Performs a cluster analysis on the distance object

cluster$labels<-names(book.list.freqs) #get the book file names to use as lables.

plot(cluster) # plots the results as a dendrogram for our inspection.

# OR in one line

plot(hclust(dist( final.m[,apply(final.m,2,mean)>=2.5] )), labels<-names(book.list.freqs))

##############################################################

# Classification

# A Small Authorship Classification Experiment

##############################################################

# Don't forget to clear workspace and then reset setwd()

library(XML)

source("code/corpusFunctions.r")

inputDir<-"data/XMLAuthorCorpus"

files<-dir(path=inputDir, pattern=".*xml")

book.list.freqs<-list()

for(i in 1:length(files)){

doc<-xmlTreeParse(file.path(inputDir, files[i]), useInternalNodes=TRUE)

chunkdata<- getWordSegmentLists(doc,10)

book.list.freqs[[files[i]]]<- chunkdata

}

freqs.list <- lapply(book.list.freqs, my.mapply)

freqs.df <- do.call(rbind,freqs.list)

dim(freqs.df)

head(freqs.df)

#eg

bookids<-gsub("\\..*", "", "anonymous.xml.1")

bookids<-gsub("\\..*", "", rownames(freqs.df))

book.chunk.ids<-paste(bookids, freqs.df$ID, sep="_")

freqs.df$ID<-book.chunk.ids

result <- xtabs(Freq ~ ID+Var1, data=freqs.df)

final<-as.data.frame.matrix(result) #convert to data.frame

# Need to create metadata columns from text-id column

metacols<-do.call(rbind, strsplit(rownames(final), "_"))

colnames(metacols)<-c("sampletext", "samplechunk")

author<-gsub("\\d.*", "", metacols[,"sampletext"])

final.df<-cbind(author, metacols, final)

#Winnow the data

freq.means<-colMeans(final.df[,4:ncol(final.df)])

keepers<-which(freq.means >=.005)

smaller.df<-final.df[, names(keepers)]

smaller.df<-cbind(author, metacols, smaller.df)

# Run SVM classification

library(e1071)

anon<-which(smaller.df$author == "anonymous")

train <- smaller.df[-anon,4:ncol(smaller.df)]

class <- smaller.df[-anon,"author"]

model <- svm(train, class)

pred <- predict(model, train) #same as pred <- fitted(model)

cm<-table(pred, class)

testdata <- smaller.df[anon,4:ncol(smaller.df)]

final.result<-predict (model, testdata)

as.data.frame(final.result)

###############################################################################

# Topic Modeling

###############################################################################

# Two options outlined below. Choose one of the two option and then

# skip down to section of code where the lda model gets run

###############################################################################

# OPTION ONE: Using a stop List

# Returns topics with lots of character names

inputDir<-"data/XMLAuthorCorpus"

files<-dir(path=inputDir, pattern=".*xml")

chunk.size<-1000 #number of words per chunk

stoplist<-read.csv("data/stoplist.csv", stringsAsFactors=F)

library(XML)

topic.df<-NULL

for(i in 1:length(files)){

doc<-xmlTreeParse(file.path(inputDir, files[i]), useInternalNodes=TRUE)

paras<-getNodeSet(doc, "/TEI/text/body//p")

words<-paste(sapply(paras,xmlValue), collapse=" ")

words.lower <-tolower(words)

words.lower<-gsub("[^[:alnum:][:space:]']", " ", words.lower)#replace all punc except apostrope and replace with space character

words.list<-strsplit(words.lower, "\\s+") #split on one or more spaces

words.unlist<-unlist(words.list)

word.vector<-words.unlist[-which(words.unlist %in% stoplist[,1])]

num.chunks<-length(word.vector)/chunk.size

max<-length(word.vector)/num.chunks

x <- seq_along(word.vector)

chunks <- split(word.vector, ceiling(x/max))

#deal with small chunks at the end

if(length(chunks[[length(chunks)]]) <= 500){

chunks[[length(chunks)-1]]<-c(chunks[[length(chunks)-1]], chunks[[length(chunks)]])

chunks[[length(chunks)]]<-NULL

}

chunks<-lapply(chunks, paste, collapse=" ")

chunks.df<-do.call(rbind, chunks)

chunks.df<-cbind(files[i],chunks.df)

topic.df<-rbind(topic.df, chunks.df)

}

# Now skip to "Running the model" below

###############################################################################

# OPTION TWO: Requires POS tagging.

# With this corpus total tagging time = ~2.5 hours

# Here is the code I used to tag the files

# library(openNLP)

# for(i in 1:length(files)){

# doc<-xmlTreeParse(file.path(inputDir, files[i]), useInternalNodes=TRUE)

# paras<-getNodeSet(doc, "/TEI/text/body//p")

# words<-paste(sapply(paras,xmlValue), collapse=" ")

# tagged_text <- tagPOS(words)

# write(tagged_text, paste("data/taggedCorpus/",files[i], ".txt", sep=""))

# }

inputDir<-"data/taggedCorpus"

files<-dir(path=inputDir, pattern=".*xml")

chunk.size<-1000 #number of words per chunk

source("code/corpusFunctions.r")

topic.df<-NULL

for(i in 1:length(files)){

tagged_text<-scan(file.path(inputDir, files[i]), what="character", sep="\n")

tagged_words <- SplitText(tagged_text)

tagged_words <- c(SelectTaggedWords(tagged_words,"/NN$")) # SELECT POS TAG TYPES TO KEEP

tagged_words_less <- RemoveTags(tagged_words)

word.vector <- RemoveNonChar(tagged_words_less)

num.chunks<-length(word.vector)/chunk.size

max<-length(word.vector)/num.chunks

x <- seq_along(word.vector)

chunks <- split(word.vector, ceiling(x/max))

#deal with small chunks at the end

if(length(chunks[[length(chunks)]]) <= 500){

chunks[[length(chunks)-1]]<-c(chunks[[length(chunks)-1]], chunks[[length(chunks)]])

chunks[[length(chunks)]]<-NULL

}

chunks<-lapply(chunks, paste, collapse=" ")

chunks.df<-do.call(rbind, chunks)

chunks.df<-cbind(files[i],chunks.df)

topic.df<-rbind(topic.df, chunks.df)

}

# Now skip to "Running the model" below

##########################################################################################

# RUNNING THE TOPIC MODEL

##########################################################################################

library(lda)

doclines<-lexicalize(topic.df[,2])

set.seed(8675309)

K <- 40

num.iterations<-250

result <- lda.collapsed.gibbs.sampler(doclines$documents, K, doclines$vocab, num.iterations, 0.1, 0.1, compute.log.likelihood=TRUE)

top.words <- top.topic.words(result$topics, 25, by.score=TRUE)

topic.proportions <- t(result$document_sums) / colSums(result$document_sums)

# DONE. . . Naw, let's make some purdy clouds. . . .

##########################################################################################

# MAKE SOME WORD CLOUDS OF THE TOPIC MODEL

##########################################################################################

library(wordcloud)

result.dir<-"topicClouds/"

for(i in 1:nrow(result$topics)){

cloud.data<-sort(result$topics[i,], decreasing=T)[1:50]

pdf(paste(result.dir, i,".pdf", sep=""), paper="special")

print(wordcloud(names(cloud.data), freq=cloud.data, scale=c(3,.10), min.freq=1, rot.per=0, random.order=F))

dev.off()

}