Unigram.R

#create a vector of all the packages you want to load
packs <- c( "tm" , "tau" , "qdap" , "RWeka" , "wordcloud")
lapply(packs, require, character.only = TRUE)

#load the dataset as a matrix
inputText = read.csv(file = "pathToFile/AirlineTweets.csv", header = TRUE, sep = ",")
input=as.matrix(inputText)

# remove retweet entities
some_txt = gsub("(RT|via)((?:\\b\\W*@\\w+)+)", "", inputText[,1])

# remove at people e.g. @ArabWic
some_txt = gsub("@\\w+", "", some_txt)

# remove punctuation
some_txt = gsub("[[:punct:]]", "", some_txt)

# remove numbers
some_txt = gsub("[[:digit:]]", "", some_txt)

# remove html links
some_txt = gsub("http\\w+", "", some_txt)
#contd on next page
# remove unnecessary spaces
some_txt = gsub("[ \t]{2,}", "", some_txt)
some_txt = gsub("^\\s+|\\s+$", "", some_txt)

#convert some_txt into a corpus and remove stopwords and punctuations
corp=Corpus(VectorSource(some_txt)) 
as.VCorpus(corp)
corp=tm_map(corp, removeWords, stopwords('english'))
inspect(corp)
corp <- tm_map(corp, removePunctuation) 
inspect(corp)

#create the bigrams and Term document matrix
UnigramTokenizer <- function(y) NGramTokenizer(y, Weka_control(min = 1, max = 1))
tdm <- TermDocumentMatrix(corp, control = list(tokenize = UnigramTokenizer))
m= inspect(tdm)

#create the wordcloud 
v = sort(rowSums(m), decreasing = TRUE) 
wordcloud(names(v), v, min.freq = 20)

#prepare the csv file
DF <- as.data.frame(m, stringsAsFactors = FALSE)
nrow(DF)
DF=as.matrix(DF)
tdf=as.data.frame(t(DF))
tdf=cbind(tdf,input[,2])
len=ncol(tdf)
header=c(1:(len-1))
header=c(header,"Class")
colnames(tdf)=header

write.csv(tdf, file = "pathToFile/Unigram.csv",row.names=FALSE)