Emotion recognition from text is crucial Natural Language Processing task which can contribute enormous benefits to different areas such as artificial intelligence, human interaction with computers etc. Emotions are physiologic thoughts engendered in human reactions to the events. Analysis of these emotions without facial and voice modulation are critical and requires a supervisory approach for proper interpretation of emotions. In spite of these challenges, it’s essential to acknowledge the human emotions as they progressively communicate using mistreatment text through social media applications such as Facebook, Twitter etc. In this paper, we propose a sentimental classification of multitude of tweets. Here, we use deep learning techniques to classify the sentiments of an expression into positive or negative emotions. The positive emotions are further classified into enthusiasm, fun, happiness, love, neutral, relief, surprise and negative emotions are classified into anger, boredom, emptiness, hate, sadness, worry. We experimented and evaluated the method using Recurrent Neural Networks and Long short-term memory on three different datasets to show how to achieve high emotion classification accuracy. A through evaluation shows that the system gains emotion prediction on LSTM model with 88.47% accuracy for positive/negative classification and 89.13% and 91.3% accuracy for positive and negative subclass respectively.