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Trained and evaluated performance of various classifiers (SVM, Naïve Bayes, Logistic Regression, Random Forest) for Natural Language Processing (NLP) in order to mine real-time data from Twitter and rate each tweet on its primary emotional content. Applied Synthetic Minority Oversampling Technique (SMOTE) on unbalanced dataset, achieved classification accuracy of 95%.