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Emotion recognition from text is an important application of natural language processing. It has vast potential in many fields like marketing, artificial intelligence, political science, psychology etc. In recent times, more attention has been brought to this field because of availability and access to large amounts of opinionated data. Over the years many techniques have been proposed to tackle this problem. This paper focuses on the problem of emotion recognition from a dataset containing labelled tweets using a CNN-LSTM classifier model. The feature encoding for this model was done using the pre-trained Word2Vec word embedding and the model classified the tweets into five emotion classes: anger, sadness, joy, fear and love. The classifier was trained on 80% of the dataset and tested on the remaining 20%. The results of this proposed system was then compared with results from Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM) and Convolution Neural Network (CNN) models. The proposed system was found to outperform all of them with an accuracy of 93.3%.