Enhancing Chatbot User Satisfaction: A Machine Learning Approach Integrating Decision Tree, TF-IDF, and BERTopic

Ensuring that large language models (LLMs) generate responses that resonate with users is critical for successful interactions. This study leverages machine learning techniques to improve chatbot interactions by aligning responses more closely with human preferences. By analyzing the Chatbot Arena dialogue dataset, we identified key features influencing user satisfaction and employed an ensemble model combining Decision Tree, TF-IDF, and BERTopic to enhance prediction accuracy. Our results demonstrate significant improvements in both accuracy and user satisfaction metrics compared to traditional models. This approach addresses the complexities of human-computer dialogue, providing a robust framework for future enhancements. The integration of advanced feature engineering and topic modeling techniques enables the chatbot to generate more contextually relevant and engaging responses, setting a new standard in the field of conversational AI.