An enhanced social emotional recognition model using bidirectional gated recurrent unit and attention mechanism with advanced optimization algorithms

Social-emotional learning (SEL) is gradually becoming a region of attention for defining children's school readiness and forecasting academic success. It is the procedure of incorporating cognition, behaviour, and emotion into daily life. School structure contains systemic practices to integrate SEL into teaching and learning so that kids and adults construct social- and self-awareness, acquire the ability to handle their specific and other's feelings and behaviour, make reliable decisions, and build positive relations. Recent school-based programs have demonstrated that SEL greatly improves mental and physical health, academic success, moral judgment, citizenship, and motivation. This paper proposes a Deep Representation Model with Word Embedding and Optimization Algorithm for Social Emotional Recognition (DRMWE-OASER) methodology. The DRMWE-OASER methodology primarily aims to develop an effectual method for detecting social-emotional learning using advanced techniques. At first, the text pre-processing stage is applied at various levels to clean and convert text data into a meaningful and structured format. Moreover, the word embedding process is implemented using the TF-IDS method. Furthermore, a bidirectional gated recurrent unit with attention mechanism (BiGRU-AM) method is employed for classification. Finally, the improved whale optimizer algorithm (IWOA)-based hyperparameter selection process is utilized to optimize the classification results of the BiGRU-AM method. A wide range of experiments using the DRMWE-OASER approach is performed under emotion detection from text dataset. The experimental validation of the DRMWE-OASER approach portrayed a superior accuracy value of 99.50% over existing models.