Dimensional Aspect-Based Sentiment Analysis (DimABSA) extends traditional ABSA by predicting continuous sentiment intensity in the Valence-Arousal space. To tackle the regression subtasks (DimASR and DimStance), we propose a Dual-Stream Syntax-Aware architecture synergizing contextual semantics with a Deep Syntax-Guided Graph Convolutional Network (GCN). It utilizes a Context-Aware Anchor for semantic filtering and post-norm residuals to prevent oversmoothing. For generative extraction, we apply Direct Preference Optimization (DPO) via a resource-efficient, heuristic-based data perturbation strategy to construct preference pairs without costly LLMs. Across multilingual settings, our regression model achieves top-5 rankings in nine domains and obtains the best result on the Chinese-Finance dataset. Empirical analysis shows that explicit syntactic modeling consistently improves continuous sentiment regression, while DPO provides modest but stable gains for boundary-constrained extraction.