Textual emotion analysis using improved metaheuristics with deep learning model for intelligent systems

Complex, intelligent systems, namely social network services (SNS), internet-of-things (IoT), augmented reality (AR), virtual reality (VR), and so on have become a crucial part of our day-to-day life. The dynamics of this system alter human behaviors in various forms. Therefore, it is important to understand the fundamental dynamics of the system and what it involves for user sentiment. Emotion analysis is a commonly used and very powerful analysis methodology in data mining. It gives an outstanding option to evaluate, determine, understand, and monitor the sentiments of consumers with respect to a service or a product. This article focuses on the design of textual emotion analysis using the improved metaheuristics with deep learning (TEA-IMWDL) model for intellectual systems. The goal of the TEA-IMWDL technique lies in the identification and classification of emotions that exist in social media content. To attain this, the presented TEA-IMWDL technique undergoes preprocessing to make the input data compatible with the latter stages of processing. For the word embedding process, the Fast text approach is utilized in this study. Next, the long short term memory-recurrent neural network (LSTM-RNN) method can be enforced for the emotion classification procedure. To boost the classification efficiency of the LSTM-RNN model, an improved gravitational search optimization algorithm (IGSA) based hyperparameter tuning process is involved in this study. A brief set of simulation analyses of the TEA-IMWDL technique is carried out and the results are tested on a benchmark dataset. The experimental outcomes demonstrate the enhanced performance of the TEA-IMWDL technique over other recent approaches.