SEAD-MGFE-Net: Schrödinger equation-based adaptive dropout multi-granular feature enhancement network for conversational aspect-based sentiment quadruple analysis

This research focuses on enhancing the extraction of sentiment quadruples consisting of target, aspect, opinion, and sentiment from multi-turn dialogs, which remains a challenging problem in conversational sentiment analysis. Existing methods frequently encounter challenges with complex sentence structures, presence of multiple sentiment quadruples, and interference from irrelevant contextual information. These challenges often result in suboptimal performance. These limitations are addressed by introducing Schrödinger equation-based adaptive dropout multi-granular feature enhancement network (SEAD-MGFE-Net), a novel framework that synergizes multigranular feature extraction with quantum-inspired adaptive regularization. The proposed methodology incorporates a multi-layer tree structure to segment sentences into semantically coherent fragments, thereby improving the alignment between aspect and opinion terms while simultaneously mitigating noise impact. Moreover, we engineer a multi-angle dynamic adjacency learning enhancement module that adeptly captures both local and global features inherent in graph-structured representations. Additionally, we devise an adaptive dropout mechanism based on the Schrödinger equation, facilitating automatic modulation of the regularization strength throughout training. Extensive evaluations on benchmark datasets in both Chinese and English validate the state-of-the-art effectiveness of our proposed SEAD-MGFE-Net model, achieving Micro-F1 scores of 46.53 % (Chinese) and 40.97 % (English), surpassing the strongest baseline models by 2.04 % and 1.57 %, respectively. SEAD-MGFE-Net exhibits efficacy in extracting cross-utterance quadruples and managing long-range dependencies. These findings confirm the effectiveness and broad applicability of SEAD-MGFE-Net for conversational sentiment analysis.