DEEP LEARNING TECHNIQUE BASED TEXT EMOTION CLASSIFICATION SYSTEM USING GENETIC ALGORITHM TECHNIQUE

In today's era the research area of emotion detection has becomes trendy field of research. The data in text form is the very easy way to communicate among interaction of human-machine through the social networking sites, is one of the schemes to share users views. Recognition of human's emotion through analyzing textual documents is useful and essential, but sometimes difficult because of the fact that it is not necessary to use emotion words directly in textual expressions. In this research, a deep learning technique based Text Emotion Classification (TEC) System using Genetic Algorithm (GA) as an optimization technique is presented. Initially, a lexicon dictionary is prepared based on the emotional words and different processes such as pre-processing, feature extraction, optimization and classification has been applied to classify the textual emotion. The test data has been trained using deep learning scheme named as Deep Neural Network (DNN) with optimization technique based on GA with a novel fitness function. The emotion; happy, sad and angry are identified as per the shared data on the social media platform. Most of the state-of-the-art in the previous research on textual emotion mining is mainly focused on without utilizing the feature selection concept, so we introduce the concept of feature selection using the GA and passes an input to DNN. At the last, we compare the performance of the proposed TEC system with existing work proposed by Chatterjee et al. terms of precision, recall and F-measure and we observed that the system got better emotion classification accuracy.