Abstract
The research introduces a deep neural network system which achieves multi-class emotion classification through its development process. The system identifies seven emotional states through its classification system which includes angry, disgust, fear, happy, neutral, sad and surprise. The researchers divided their dataset into training and testing parts after preprocessing and they used precision and recall and F1-score and confusion matrix and ROC-AUC curves to evaluate their results. The model achieves its highest accuracy when detecting happy emotions at 89% followed by surprise at 68% and disgust at 49% according to the confusion matrix. The model achieves good to excellent classification results for most emotions yet it struggles with “fear and neutral emotions because their features overlap or their class distributions are unbalanced. The researchers computed Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) values for each class in the study. The model produced its best AUC results for happy and surprise emotions at 0.92 and 0.90 respectively followed by disgust at 0.84. The lowest AUC score of 0.71 appeared in the fear category because this emotion showed weak discriminative properties. The model achieved a macro-averaged AUC score of 0.82 when evaluating all classes together. The proposed neural network shows strong performance in emotion recognition tasks through its ability to detect intense emotions such as happiness and surprise.