In today’s modern businesses, understanding the emotional feedback dimensions has become crucial for enhancing organizational decision-making. Our paper proposes a framework that focuses on a novel approach, bridging the gap between what people know and how they feel about it, thereby making their experiences more impactful and human-centered. This helps organizations to depend not only on technical and procedural processes to extract and store knowledge, but also to provide deep empathetic insights. Using a public dataset and pre-trained NLP models, in addition to other tools, we constructed a knowledge graph that connects each node of feedback with others that share the same thematic context or emotion label. To ensure the feasibility and validity of our work, we integrate the design science research (DSR) method and a proof-of-concept (POC) implementation. The proposed graph analysis facilitates the identification and prioritization of critical issues in real-time, which can improve service response and user experiences.