Traditional feedback analysis methods are inadequate for extracting detailed insights from customer reviews, surveys, and opinions. They fail to identify specific topics, features, sentiments, and temporal references, making it difficult for businesses to understand customer concerns and competitive positioning. This paper presents a comprehensive framework that integrates Named Entity Recognition (NER), quintuple extraction, comparative opinion analysis, and coreference resolution to provide structured feedback analysis. The system employs advanced Natural Language Processing techniques with Retrieval-Augmented Generation (RAG) using FAISS vector databases for contextual similarity search. The approach utilizes specialized agents powered by Large Language Models (LLMs) to extract actionable insights including target objects, features, sentiments, opinion holders, and temporal references. The framework supports both single review analysis (up to 8,000 words) and batch processing capabilities. Experimental evaluation demonstrates significant improvements in feedback comprehension and business decision-making capabilities. The system addresses key limitations of traditional approaches by providing structured, disambiguated, and contextually-aware feedback analysis suitable for real-world business applications.