In recent years, the increasing advancement and homogenization of products and services have made differentiation solely based on functionality or price increasingly difficult. Within this context, emotional resonance—emotional responses evoked through user experiences—has gained increasing recognition as a critical factor in contemporary marketing strategies. This study aims to quantitatively assess experiential value by analyzing users’ emotional responses during interactions with products or services. To this end, two multidimensional sentiment analysis models were proposed, which extend beyond the conventional binary classification of positive and negative sentiments. Experimental results demonstrated that the proposed models outperform existing sentiment analysis models in accurately reproducing and classifying human emotions within a multidimensional space. Moreover, the models’ effectiveness was demonstrated not only in terms of predictive accuracy but also in its ability to capture and reproduce the latent structure of emotions.