Empowering people with intellectual disabilities using integrated deep learning architecture driven enhanced text-based emotion classification

Emotion recognition is an important research field including psychology, healthcare, and human-computer interaction (HCI). However, conventional techniques mainly rely on textual analysis and facial expressions, and they also have potential flaws, making them unreliable. Textual language is the most common carrier of human emotions and its analysis relies on the available data. In natural language processing (NLP), textual emotion recognition (TER) has become a significant area of research due to its essential commercial and academic applications. With the growth of deep learning (DL) technologies, TER has seen a growing interest and has undergone considerable upgrades recently. This paper proposes an Intelligent Emotion Recognition from Text Using a Hybrid Deep Learning Model and Word Embedding Process (IERT-HDLMWEP) model. The aim is to develop a DL-based system for accurate text emotion recognition to support communication for people with disabilities. Initially, the text pre-processing stage involves several typical steps to develop the analysis and minimize the dimensionality of the input data. The IERT-HDLMWEP method creates a hybrid feature representation by integrating pre-trained Word2Vec vectors weighted by TF-DF category distribution and enriched with Part-of-Speech features to improve emotion detection in text. Finally, the hybrid of a convolutional neural network and a bidirectional gated recurrent unit with an attention mechanism (C-BiG-A) technique is employed for the classification process. A comprehensive simulation was implemented to verify the performance of the IERT-HDLMWEP method in emotion detection from the Text dataset. The empirical outcomes indicated that the IERT-HDLMWEP methodology emphasized improvement over other existing techniques.