Emotion analysis plays a part in understanding the feelings of human beings. People's actions and speech express various feelings, behaviors and emotions which can have various impacts. Emotion and sentiment analysis is a broad research area for finding emotion which helps getting useful insight through text and speech. In most of previous work, nearly all projects have focused on analyzing the expression based on positive, negative and neutral classification. This research work analyzes the proposed system by categorizing the text into emotion classes called joy, sadness, anger, fear, love and surprise. This work helps us to label text emotions into multiple classes and categorize the text for better accuracy. This research work represents the enhancement of a novel Deep Learning model scheme, Long Short-Term Memory and Recurrent Neural Network which discuss various categories distribution on knowledgeable data. This will also summarize the previous works done on textual emotional classification based on various sentiment models and approach with comparative survey analysis.