Emotion Detection and Psychological Prediction Using Capsule Networks and Recurrent Neural Networks

Emotions are priceless since they reveal a person's true mental condition. There are a lot of important decisions that need to be made under pressure, and knowing how people really feel can help us with that. Recent advances in artificial intelligence and machine learning have made affective computing an intriguing field of study. Imitating human emotional behavior, it promotes learning results directly tied to behavioral psychology. There are three steps that comprise the proposed method: preprocessing, feature extraction, and training the model. Tokenization, point-of-sale tagging, stemming, and stop word removal are all part of the preprocessing. The process of feature extraction involves transforming data into characteristics that may be used by machine learning algorithms. The model was trained using CNRNN. This cutting-edge method outperforms both AdaBoost and SVM, with an average accuracy of 95.31%.