Golden Tortoise Beetle Optimizer with Deep Learning Based Emotion Detection in Social Media

Social networking sites like Twitter developed as social media platform that impart a vast knowledge base for an individual to express their perspectives and exclusive ideologies on several subjects and problems with families and friends. In recent times, the two methods that were commonly used in Natural Language Processing (NLP) domain are sentiment analysis (SA) and emotion classification. Emotion analysis can be defined as the process of detecting the feature against targets or topics. The attitude can be polarity either negative/positive or an emotional condition like anger, miserable, or bliss. Hence, opinion mining and classifying posts manually will be complex. In this article, we focus on the design of Golden Tortoise Beetle Optimizer with Deep Learning based Emotion Detection (GTBO-DLED) technique on Social Media. The presented GTBO-DLED technique identifies various kinds of emotions expressed by people on social media. Primarily, the GTBO-DLED technique undergoes a series of preprocessing to transform the text into useful format. For emotion detection, the preprocessed data gets analyzed by deep belief network (DBN). Since trial and error hyperparameter tuning will be a tedious task, automated procedure using GTBO method was designed in this study. The simulation values of the GTBO-DLED approach was executed on benchmark emotion detection database. The GTBO-DLED technique shows promising performance over other recent algorithms with respect to various measures.