LLM-Driven Sentiment Analysis in Customer Service Dashboards: A Framework for Real-Time Feedback Intelligence - Khonqulova Nilufar Ravshanovna (Goodwood Publishing, 2026)
Purpose: This study develops and evaluates a conceptual framework for integrating Large Language Model (LLM) driven sentiment analysis into customer service dashboards to enable real-time, emotionally intelligent feedback monitoring. The framework addresses gaps in traditional dashboards that fail to capture contextual and affective dimensions of customer experience.Research.Methodology: A conceptual and analytical approach synthesizes literature on LLM based sentiment analysis, aspect based opinion mining, customer service automation, and dashboard design. A five-layer integration architecture covering data ingestion, processing, LLM analysis, visualization, and human in the loop is proposed and evaluated for real-time enterprise feedback intelligence.Results: The framework introduces six capabilities real-time negative trend detection, emotionally weighted ticket prioritization, automated escalation, aspect-based sentiment disaggregation, sentiment-trajectory agent performance evaluation, and predictive customer satisfaction modeling. An LLM output schema defines sentiment polarity, emotion, urgency, service aspect, customer risk, and recommended action. Key challenges, including privacy, bias, hallucination, latency, computational cost, and over-reliance, are discussed.Conclusions: LLM driven sentiment analysis can transform dashboards into emotionally aware decision-support systems, combining contextual understanding with operational metrics, human oversight, and continuous validation.Limitations: The framework remains conceptual and untested in live deployments, with implementation feasibility and user acceptance to be empirically examined.Contributions: This study provides a structured, practitioner oriented framework consolidating NLP, customer experience, and information systems knowledge for multilingual, multichannel service environments.