Mining Managerial Insights from User Reviews: A Mix Contrastive Method to Aspect–Opinion Mining

For businesses to optimize management decisions in the digital transformation, a process inherently characterized by symmetry between feedback collection and strategic adjustment, it is essential to automatically extract fine-grained opinions from large volumes of unstructured evaluations. However, traditional evaluation management techniques often fail to reflect this symmetrical balance between user perception and organizational response, primarily due to their inefficiency in processing unstructured textual data. Moreover, existing aspect–opinion mining algorithms exhibit limited practical generalization performance due to poor robustness against noise and semantic variations in real-world reviews. To address these gaps, this paper proposes MixContrast, an aspect–opinion mining method based on mix contrastive learning, which integrates mixed sample construction with data augmentation to generate continuous semantic transition samples. By symmetrically aligning positive and negative samples through a contrastive learning mechanism, MixContrast enhances representation learning and improves model generalization. Experiments conducted on cosmetics and multi-domain e-commerce review datasets demonstrate that MixContrast significantly outperforms several strong baseline models in both aspect and opinion extraction tasks. Theoretical analysis shows that MixContrast enhances robustness by ensuring Lipschitz continuity and enabling gradient decomposition in the representation space. Based on MixContrast predictions, we conduct a correlation analysis among aspects, opinions, and sentiment tendencies, delivering real-time quantitative support for marketing strategy formulation, product optimization, and service enhancement. Beyond advancing aspect–opinion mining technology, this work enables data-driven, symmetrical integration of technical insights with managerial decision-making, holding significant theoretical and practical value for digitally transforming enterprises.