In this study, we have explored the potentiality of KNN classifier to recognize four basic emotions (anger, happy, sadness and surprise) on three different heterogeneous emotion-annotated dataset which combines sentences from news headlines, fairy tales and blogs. For classification purpose, we have chosen the feature set to include the bag-of-words generated by our proposed n-gram based feature selection technique. Our study reveals the fact that the use of the resampling filter and the features generated by our n-gram based feature selection technique together contribute towards boosting the prediction accuracies of the classifiers.