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sentiment analysis
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)
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