# Init
libs <- c("tm", "plyr", "class")
lapply(libs,require, character.only= TRUE)
# Set Options
options(stringsAsFactors = FALSE)
#Set Parameters
candidates <-
c("M","S")
pathname <-
"C:/Data"
# Clean text
cleanCorpus <- function(corpus){
corpus.tmp <- tm_map(corpus, removePunctuation)
corpus.tmp <- tm_map(corpus.tmp,stripWhitespace)
#corpus.tmp <- tm_map(corpus.tmp,tolower)
corpus.tmo <- tm_map(corpus.tmp, PlainTextDocument)
corpus.tmp <- tm_map(corpus.tmp,removeWords, stopwords("english"))
return(corpus.tmp)
}
# Buildt TDM
gererateTDM <- function(cand,path){
s.dir <-sprintf("%s/%s",path,cand)
s.cor <-Corpus(DirSource(directory = s.dir, encoding=
"Windows-1254"))
s.cor.cl <- cleanCorpus(s.cor)
s.tdm <- TermDocumentMatrix(s.cor.cl)
s.tdm <- removeSparseTerms(s.tdm, 0.7)
result <- list(name = cand, tdm = s.tdm)
}
tdm <- lapply(candidates,gererateTDM, path=pathname)
# Attach Name
bindCandidateToTDM <- function(tdm) {
s.mat <- t(data.matrix(tdm[["tdm"]]))
s.df <- as.data.frame(s.mat, stringAsFactors = FALSE)
s.df <-cbind (s.df, rep(tdm[["name"]], nrow(s.df)))
colnames(s.df)[ncol(s.df)] <- "targetCandiate"
return(s.df)
}
candTDM <- lapply(tdm, bindCandidateToTDM)
# Stack
tdm.stack <- do.call(rbind.fill, candTDM)
tdm.stack[is.na(tdm.stack)] <- 0
# Hold-Out
train.idx <- sample(nrow(tdm.stack), ceiling(nrow(tdm.stack) * 0.7))
test.idx <- (1:nrow(tdm.stack)) [- train.idx]
# Model - KNN
tdm.cand <- tdm.stack[, "targetCandiate"]
tdm.stack.nl <- tdm.stack [, !colnames(tdm.stack) %in% "targetCandiate"]
knn.pred <-knn(tdm.stack.nl[train.idx, ], tdm.stack.nl[test.idx, ], tdm.cand[train.idx])
# Accuracy
conf.mat <- table("Predictions" =knn.pred, Actual= tdm.cand[test.idx])
(accuracy <- sum(diag(conf.mat)) / length(test.idx)* 100)
