Emotionally Intelligent Chatbots: Enhancing Human-Machine Interaction using BERT Transformer

Emotionally intelligent chatbots (EICs) enhance human-computer interaction by recognizing and responding to user emotions. Using advanced techniques such as sentiment analysis, natural language processing (NLP), and machine learning, these systems are capable of offering more human-like and empathetic conversations. This paper explores past research, current advancements, and the significance of emotionally aware chatbots across multiple domains such as healthcare, customer service, and education. Additionally, it highlights the key challenges in developing such chatbots, including issues related to dataset quality and availability, ethical implications, and the need for multi-modal emotion recognition. Using datasets like EmpatheticDialogues and DailyDialog together, it showcase notable advancements in the quality of empathetic responses, thereby increasing user engagement and satisfaction. By tackling the complexities associated with contextual emotional comprehension and the suitability of responses, this research plays a vital role in the development of conversational systems that are sensitive to emotional nuances.