Emotion Prediction With Chatbot: A Deep Learning Approach

This paper presents a novel approach for emotion prediction using a chatbot interface,leveraging deep learning techniques to analyses textual input and predict user emotions in realtime. Traditional emotion detection methods rely on facial expressions or voice modulation,which may not be feasible in text-based interactions. The proposed system employs NaturalLanguage Processing (NLP) techniques, including Bidirectional Encoder Representations fromTransformers (BERT) for contextual understanding and Long Short- Term Memory (LSTM)networks for sequential emotion classification. The model is trained on a curated dataset ofconversational text labelled with emotional states (e.g., happiness, sadness, anger). Validationagainst human-annotated datasets demonstrates an accuracy of 92%, outperforming existingrule-based and machine learning approaches. This research contributes to enhancing humancomputer interaction by enabling chatbots to respond empathetically based on detectedemotions, with applications in mental health support, customer service, and virtual assistants.