Automated sentiment analysis of Vietnamese social media comments presents several distinctive linguistic challenges, including ambiguous compound-word boundaries, implicit negation, polarity shifts across contrastive clauses, and severe class imbalance among fine-grained sentiment levels (N1: Light Negative, N2: Heavy Negative, P1: Light Positive, and P2: Heavy Positive). Although contextual transformer models provide strong predictive performance, they offer limited interpretability and may incorrectly handle polarity changes caused by intensifiers, negations, and contrastive conjunctions. In contrast, rule-based ontologies provide transparent reasoning but are often brittle when processing informal language, spelling errors, slang, and out-of-vocabulary expressions. To address these limitations, this paper proposes the \textbf{Enhanced Ontology-Based Sentiment Analysis Framework (EOSAF)}, which combines deep contextual representations with structured Knowledge Graph (KG) reasoning. The framework comprises: (1) a selectively fine-tuned PhoBERT encoder with hybrid CLS-mean pooling, producing a 768-dimensional contextual representation ($h_{\text{bert}} \in \mathbb{R}^{768}$); (2) an ontology-guided KG reasoning module that models intensifier scaling, negation-window inversion, contrastive-clause analysis, and idiom substitution as a 64-dimensional interpretable feature vector ($h_{\text{kg}} \in \mathbb{R}^{64}$); (3) feature-fusion mechanisms that integrate contextual and symbolic representations; and (4) a learnable Ontology-based Threshold Adjustment layer that calibrates class logits using structured KG evidence. An adaptive offline caching mechanism containing 4,771 domain entities is further incorporated to support efficient local knowledge retrieval during inference. Experiments on real-world Vietnamese social media comments show that the proposed framework achieves an accuracy of \textbf{98.71\%} and an F1-Macro score of \textbf{0.9868}, outperforming existing baselines by up to 12.14\% while preserving interpretable ontology-based reasoning.

Vietnamese,Sentiment Analysis,Opinion Mining,Transformer,Classification,Social Media.