Emotion and Cause Detection in Dialogues Using Capsule Networks

Identifying the sources of emotions expressed in text-to-text conversational dialogs significantly contributes to improving applications such as virtual assistants, customer support, and interventions aimed at improving mental well-being. Traditional approaches to emotion analysis are primarily focused on classification procedures rather than consideration of the underlying causes responsible for triggering specific emotional behaviors in various contexts. This paper has presented a new model that identifies emotions in conversational data but simultaneously enables the determination of the root causes of such emotions. The framework uses DistilBERT to generate subtle contextual embeddings, which incorporate the richness of meaning for each utterance. For that purpose, it uses a Capsule Network for the efficient modeling of sequential dependencies and hierarchical relationships that exist in dialog. This altogether makes this system decode more complex emotional triggers with greater accuracy. Furthermore, the model is designed to be scalable and efficient, meaning it can comfortably be deployed in any real-time environment with minimal computational resources. The Experimental Results, integrated A two-step model consisting of Distil BERT and Capsule Network greatly enhances the accuracy of emotion cause detection and can be a strong solution for applications requiring minute emotions and causal inferences from conversational data.