pos_tweets =  rbind(
  c('I love this car', 'positive'),
  c('This view is amazing', 'positive'),
  c('I feel great this morning', 'positive'),
  c('I am so excited about the concert', 'positive'),
  c('He is my best friend', 'positive')
)

neg_tweets = rbind(
  c('I do not like this car', 'negative'),
  c('This view is horrible', 'negative'),
  c('I feel tired this morning', 'negative'),
  c('I am not looking forward to the concert', 'negative'),
  c('He is my enemy', 'negative')
)

test_tweets = rbind(
  c('feel happy this morning', 'positive'),
  c('larry friend', 'positive'),
  c('not like that man', 'negative'),
  c('house not great', 'negative'),
  c('your song annoying', 'negative')
)

tweets = rbind(pos_tweets, neg_tweets, test_tweets)
matrix1= matrix(tweets[,1], language="english",removeStopwords=FALSE, removeNumbers=TRUE,stemWords=FALSE)
mat = as.matrix(matrix1)
classifier = naiveBayes(mat[1:10,], as.factor(tweets[1:10,2]) )
predicted = predict(classifier, mat[11:15,]); predicted
table(tweets[11:15, 2], predicted)
recall_accuracy(tweets[11:15, 2], predicted)