library("tm")

library("wordcloud")

library("slam")

library("topicmodels")

#Load Text

con <- file("tweets.txt", "rt")

tweets = readLines(con)

#Clean Text

tweets = gsub("(RT|via)((?:\\b\\W*@\\w+)+)","",tweets)

tweets = gsub("http[^[:blank:]]+", "", tweets)

tweets = gsub("@\\w+", "", tweets)

tweets = gsub("[ \t]{2,}", "", tweets)

tweets = gsub("^\\s+|\\s+$", "", tweets)

tweets <- gsub('\\d+', '', tweets)

tweets = gsub("[[:punct:]]", " ", tweets)

corpus = Corpus(VectorSource(tweets))

corpus = tm_map(corpus,removePunctuation)

corpus = tm_map(corpus,stripWhitespace)

corpus = tm_map(corpus,tolower)

corpus = tm_map(corpus,removeWords,stopwords("english"))

tdm = DocumentTermMatrix(corpus) # Creating a Term document Matrix

# create tf-idf matrix

term_tfidf <- tapply(tdm$v/row_sums(tdm)[tdm$i], tdm$j, mean) * log2(nDocs(tdm)/col_sums(tdm > 0))

summary(term_tfidf)

tdm <- tdm[,term_tfidf >= 0.1]

tdm <- tdm[row_sums(tdm) > 0,]

summary(col_sums(tdm))

#Deciding best K value using Log-likelihood method

best.model <- lapply(seq(2, 50, by = 1), function(d){LDA(tdm, d)})

best.model.logLik <- as.data.frame(as.matrix(lapply(best.model, logLik)))

#calculating LDA

k = 50;#number of topics

SEED = 786; # number of tweets used

CSC_TM <-list(VEM = LDA(tdm, k = k, control = list(seed = SEED)),VEM_fixed = LDA(tdm, k = k,control = list(estimate.alpha = FALSE, seed = SEED)),Gibbs = LDA(tdm, k = k, method = "Gibbs",control = list(seed = SEED, burnin = 1000,thin = 100, iter = 1000)),CTM = CTM(tdm, k = k,control = list(seed = SEED,var = list(tol = 10^-4), em = list(tol = 10^-3))))

#To compare the fitted models we first investigate the values of the models fitted with VEM and estimated and with VEM and fixed

sapply(CSC_TM[1:2], slot, "alpha")

sapply(CSC_TM, function(x) mean(apply(posterior(x)$topics, 1, function(z) - sum(z * log(z)))))

Topic <- topics(CSC_TM[["VEM"]], 1)

Terms <- terms(CSC_TM[["VEM"]], 8)

Terms