Intelligent emotion sensing using BERT BiLSTM and generative AI for proactive customer care

Contact centers now rely on intelligent chatbots and virtual assistants that detect and respond to customer emotions in real time. The paper presents a novel method for detecting emotions in customer care chat by utilizing a hybrid model that combines the BERT (Bidirectional Encoder Representations from Transformers) and Bi-LSTM (Bidirectional Long Short-Term Memory) networks. This combination of features enhanced AI capabilities. This method overcomes the limitations of current emotion recognition techniques by integrating BERTs contextual understanding with Bi-LSTMs sequential modeling power. It allows for more accurate and nuanced understanding of customer emotions. We process incoming customer messages in real time-our hybrid BERT + Bi-LSTM classifier detects emotions and a generative AI module drafts agent responses with end-to-end latency below 200 ms. Analyzing customer-agent interactions to classify emotions such as frustration, anger, sadness, and satisfaction. By incorporating Generative AI, the model not only detects emotions but also generates context-aware responses to de-escalate tense situations, providing agents with actionable insights and support. The proposed solution is validated using real-world contact center data, demonstrating superior performance in emotion recognition and aggression detection compared to existing methods. This advancement paves the way for improved customer experiences, reduced agent burnout, and enhanced operational efficiency in high-stress contact center environments. The integration of BERT-Bi-LSTM with Generative AI represents a significant step forward in creating empathetic, intelligent, and proactive customer care systems.