Accurately measuring human emotions from textual data remains a significant challenge in Natural Language Processing (NLP) due to linguistic nuances like sarcasm, which often lead to misclassification. This paper presents a hybrid Emotion Measurement System (EMS) that utilizes NoSQL (MongoDB) for scalable data handling and a retrained RoBERTa model for irony detection. A primary focus is placed on the "Dissonance Score"—a novel parameter implemented to bridge the gap between static rule-based sentiment and ironic context. The research demonstrates that activating the Dissonance Score at a threshold of 0.5 significantly improves accuracy in cases where traditional models fail to recognize ironic intent.