Bengali text analysis in R - r

Question

I am working with Bengali text, have a data set which contains around 9800 tweets, Facebook status (Bengali text mixed with English) labeled with Positive negative sentiment.
I tried to perform Naïve Bayes algorithm for text classification and
other machine learning algorithms.
I found challenges in
Dealing with languages other than English (like Bengali)
Cleaning data
Creating document term matrix for Bengali text
Inconsistent result (though it shows results)
So, using my data set (Bengali text) how I could perfectly perform Naïve Bayes Algorithm and other machine learning algorithms (decision tree, support vector machine)
NB: i am sharing sample data
https://1drv.ms/x/s!Al917DZ-85m3ghcLoFHX4rWUTFOS
library(tm)
library(e1071)
library(MLmetrics)
rawData <- bntextt
colnames(rawData) <- c("type", "text")
rawData$text <- iconv(rawData$text, to = "utf-8")
rawData$type <- factor(rawData$type)
sms_train_raw <- rawData[1:9800, ]$type
Sms_test_raw<- rawData[9801:9883,]$type
sms_corpus <- Corpus(VectorSource(rawData$text))
corpus <- tm_map(sms_corpus, content_transformer(tolower))
corpus <- tm_map(corpus, removeNumbers)
corpus <- tm_map(corpus, removeWords, stopwords())
corpus <- tm_map(corpus, removePunctuation)
corpus <- tm_map(corpus, stripWhitespace)
sms_corpus_train<-corpus[1:9800]
sms_corpus_test<-corpus[9801:9883]
sms_dtm <- DocumentTermMatrix(corpus)
sms_dtm_train <- sms_dtm[1:9800,]
sms_dtm_test <- sms_dtm[9801:9883,]
five_times_words_train <- findFreqTerms(sms_dtm_train, 5)
five_times_words_test <- findFreqTerms(sms_dtm_test, 5)
sms_dtm_train <- DocumentTermMatrix(sms_corpus_train,control=list(dictionary = five_times_words_train))
sms_dtm_test <- DocumentTermMatrix(sms_corpus_test,control=list(dictionary = five_times_words_test))
convert_count <- function(x) {
y <- ifelse(x > 0, 1,0)
y <- factor(y, levels=c(0,1), labels=c("No", "Yes"))
y
}
sms_train <- apply(sms_dtm_train, 2, convert_count)
sms_test <- apply(sms_dtm_test, 2, convert_count)
sms_classifier <- naiveBayes(sms_train, sms_train_raw)
class(sms_classifier)
sms_test_pred <- predict(sms_classifier, newdata=sms_test,type="class")
table(Sms_test_raw,sms_test_pred)
Accuracy(sms_test_pred, testData$type)
F1_Score(sms_test_pred, testData$type)
Raw data
Document term matrix