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Preprocess.R
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Preprocess.R
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library(readr)
library(textmineR)
library(wordcloud)
set.seed(123)
NewsDatasetLabeled <- read_csv("C:/Users/diior/Desktop/NewsDatasetLabeled.csv")
data<-NewsDatasetLabeled
Relevant=data[data$class==1,]
Not_Relevant=data[data$class==0,]
N=dim(data)[1]
N_Rel=dim(Relevant)[1]
N_not_Rel=dim(Not_Relevant)[1]
random_idxs=sample(c(1:(N_Rel)),replace=T,size=(N_not_Rel-N_Rel))
Extended_relevant=Relevant[random_idxs,]
#a <- read.table("Matrix.csv",header = TRUE,sep=" ")
fulldata=rbind(data,Extended_relevant)
dtm <- CreateDtm(doc_vec = fulldata$Text, # character vector of documents
doc_names = fulldata$NumArt, # document names
ngram_window = c(1, 2), # minimum and maximum n-gram length
stopword_vec = c(stopwords::stopwords("en"), # stopwords from tm
stopwords::stopwords(source = "smart")), # this is the default value
lower = TRUE, # lowercase - this is the default value
remove_punctuation = TRUE, # punctuation - this is the default
stem_lemma_function = function(x) SnowballC::wordStem(x, "porter"),
remove_numbers = TRUE, # numbers - this is the default
verbose = FALSE, # Turn off status bar for this demo
cpus=2) # default is all available cpus on the system
dtm_origi <- CreateDtm(doc_vec = data$Text, # character vector of documents
doc_names = data$NumArt, # document names
ngram_window = c(1, 2), # minimum and maximum n-gram length
stopword_vec = c(stopwords::stopwords("en"), # stopwords from tm
stopwords::stopwords(source = "smart")), # this is the default value
lower = TRUE, # lowercase - this is the default value
remove_punctuation = TRUE, # punctuation - this is the default
stem_lemma_function = function(x) SnowballC::wordStem(x, "porter"),
remove_numbers = TRUE, # numbers - this is the default
verbose = FALSE, # Turn off status bar for this demo
cpus=2) # default is all available cpus on the system
# for (i in c(1,2,3,4,5,6,7,8,9,10,c(15:5:60))){
# prova<-length(which(colSums(dtm)>i))
# print(c(i,prova))
# }
#
# dtm=dtm[,colSums(dtm)>60]
#
#
# dtm_Relevant= CreateDtm(doc_vec = Relevant$Text, # character vector of documents
# doc_names = Relevant$NumArt, # document names
# ngram_window = c(1, 2), # minimum and maximum n-gram length
# stopword_vec = c(stopwords::stopwords("en"), # stopwords from tm
# stopwords::stopwords(source = "smart")), # this is the default value
# lower = TRUE, # lowercase - this is the default value
# remove_punctuation = TRUE, # punctuation - this is the default
# remove_numbers = TRUE, # numbers - this is the default
# verbose = FALSE, # Turn off status bar for this demo
# cpus=2) # default is all available cpus on the system
#
# dtm_Not_Relevant= CreateDtm(doc_vec = Not_Relevant$Text, # character vector of documents
# doc_names = Not_Relevant$NumArt, # document names
# ngram_window = c(1, 2), # minimum and maximum n-gram length
# stopword_vec = c(stopwords::stopwords("en"), # stopwords from tm
# stopwords::stopwords(source = "smart")), # this is the default value
# lower = TRUE, # lowercase - this is the default value
# remove_punctuation = TRUE, # punctuation - this is the default
# remove_numbers = TRUE, # numbers - this is the default
# verbose = FALSE, # Turn off status bar for this demo
# cpus=2) # default is all available cpus on the system
Tf=TermDocFreq(dtm)
Tf_orig=TermDocFreq(dtm_origi)
# Tf_Relevant=TermDocFreq(dtm_Relevant)
# Tf_Not_Relevant=TermDocFreq(dtm_Not_Relevant)
# which.max(Tf$term_freq)
# Tf[which.max(Tf$term_freq),1]
# Tf_Relevant[which.max(Tf_Relevant$term_freq),1]
# Tf_Not_Relevant[which.max(Tf_Not_Relevant$term_freq),1]
pal <- brewer.pal(8, "Dark2")
wordcloud(Tf$term,freq = Tf$term_freq,max.words=50,colors=pal)
# wordcloud(Tf_Relevant$term,freq = Tf_Relevant$term_freq,max.words=50,colors =pal)
# wordcloud(Tf_Not_Relevant$term,freq = Tf_Not_Relevant$term_freq,max.words=50,colors=pal)
Tf$term
tfidf <- t(dtm[ , Tf$term ]) * Tf$idf
tfidf <- t(tfidf)
Tf_orig$term
tfidf_orig <- t(dtm_origi[ , Tf_orig$term ]) * Tf_orig$idf
tfidf_orig<- t(tfidf_orig)
idd=which(colSums(tfidf)>2000)
idd_orig=which(colSums(tfidf_orig)>1000)
tfidf <- tfidf[,colSums(tfidf)>2000]
tfidf_orig <- tfidf_orig[,colSums(tfidf_orig)>1000]
a=matrix(0,dim(tfidf)[1],dim(tfidf)[2])
a_orig=matrix(0,dim(tfidf_orig)[1],dim(tfidf_orig)[2])
idx=summary(tfidf)
idx_orig=summary(tfidf_orig)
a[cbind(idx$i,idx$j)]<-idx$x
a_orig[cbind(idx_orig$i,idx_orig$j)]<-idx_orig$x
colnames(a)<-Tf$term[idd]
colnames(a_orig)<-Tf_orig$term[idd_orig]
Reduced=prcomp(a,rank=300)
Full=data.frame(fulldata$class,a)
Full_orig=data.frame(data$class,a_orig)
colnames(Full)[1]<-"class"
colnames(Full_orig)[1]<-"class"
rel=Full[Full$class==1,]
not_rel=Full[Full$class==0,]
write.table(rel,"Relevant.csv",row.names = FALSE,col.names=TRUE)
write.table(not_rel,"Not_Relevant.csv",row.names=FALSE,col.names = TRUE)
write.table(xx,"full.csv",row.names = FALSE,col.names=TRUE)
write.table(Full_orig,"full_orig.csv",row.names = FALSE,col.names=TRUE)
prova=read.table("Relevant.csv",header=TRUE,sep=" ")