Advancing Emotional Intelligence in Chatbots through Deep Learning: A Framework for Real-Time Sentiment and Emotion Recognition

Abstract

  Emotionally intelligent chatbots are emerging as transformative tools in conversational AI, particularly in applications that benefit from empathy, such as mental health support, customer service, and education. However, many chatbots still lack the capability to accurately recognize and respond to human emotions, making interactions feel impersonal and sometimes ineffective. This research introduces a multi-modal framework that integrates text, voice, and facial expression analysis to enhance emotion recognition and response adaptability. Advanced machine learning techniques, including transformers and CNN-LSTM architectures with attention mechanisms, are employed to capture and process emotional cues across these modalities. The proposed model’s training involved data augmentation and bias mitigation strategies to improve robustness and fairness across diverse user groups. Experiments show that the fusion model achieves a significant improvement in accuracy (91.3%) over single-modality models and performs well in real-time interactions, with an efficient training time and convergence rate. The results highlight the model's ability to detect subtle emotional shifts and adapt responses accordingly, increasing user engagement and satisfaction. This study contributes a comprehensive approach to the design and deployment of emotionally intelligent chatbots, setting a foundation for future developments in empathetic AI systems.