In the competitive landscape of e-commerce—especially in the cosmetics sector—user reviews significantly influence consumer decision-making. However, the overwhelming volume of reviews often leads to information overload, making it difficult for users to extract meaningful insights. This study introduces a novel tag cloud-based feature visualization method that summarizes consumer sentiments (positive and negative) extracted from textual reviews. Through a counterbalanced within-subjects experiment, we demonstrate that our interactive visualization system improves decision-making efficiency and user satisfaction compared to traditional text-based reviews. Methodologically, this approach offers a scalable and interpretable framework for integrating sentiment analysis with visual analytics, bridging the gap between qualitative user-generated content and quantitative decision support systems. Our findings indicate that the proposed method not only enhances consumers’ability to identify products aligned with their preferences but also reinforces the connection between visual behavioral analytics and consumer belief in brands (CBB). Consequently, it contributes to a deeper understanding of the cognitive mechanisms underlying consumer purchase decisions (CPD) and offers practical insights for enhancing brand competitiveness in digital marketplaces, while also illuminating how visual information processing influences consumer behavior and strategic brand positioning.