This study addresses the ongoing challenge of optimizing chatbot interactions to significantly enhance customer experience and satisfaction through personalized, empathetic responses. Using advanced NLP tools and strong statistical methodologies, we developed and evaluated a multi-layered analytical framework to accurately identify user intents, assess customer feedback, and generate emotionally intelligent interactions. With over 270,000 customer chatbot interaction records in our dataset, we employed spaCy-based NER and clustering algorithms (HDBSCAN and K-Means) to categorize customer queries precisely. Text classification was performed using random forest, logistic regression, and SVM, achieving near-perfect accuracy. Sentiment analysis was conducted using VADER, Naive Bayes, and TextBlob, complemented by semantic analysis via LDA. Statistical tests, including Chi-square, Kruskal–Wallis, Dunn’s test, ANOVA, and logistic regression, confirmed the significant impact of tailored, empathetic response strategies on customer satisfaction. Correlation analysis indicated that traditional measures like sentiment polarity and text length insufficiently capture customer satisfaction nuances. The results underscore the critical role of context-specific adjustments and emotional responsiveness, paving the way for future research into chatbot personalization and customer-centric system optimization.
Uzan et al. (Thu,) studied this question.