SAGU-Net: Gate-Level Lexicon–Neural Fusion via Sentiment-Aware Gated Units for Social Media Sentiment Analysis

Social media sentiment analysis demands systems that are simultaneously accurate, scalable, and interpretable. Lexicon-based methods offer transparency but ignore context, while pre-trained language models (PLMs) capture contextual semantics yet encode sentiment only implicitly. Existing integration strategies inject lexicon signals at the input, attention, or feature layer—all outside the recurrent gating mechanism that controls how affective evidence accumulates over a sequence. We propose the SAGU-Net, a framework built around the Sentiment-Aware Gated Unit (SAGU), a gated recurrent unit (GRU) variant with a dedicated sentiment gate conditioned on external lexicon signals. A complementary Context-Adaptive Sentiment Scoring (CASS) module transforms static polarity scalars into context-dependent vectors via learned projections over PLM representations, bridging the gap between discrete lexicon scores and continuous embeddings. The sentiment gate activations provide token-level explainability without post hoc attribution. On a 12,700-sample Chinese social media corpus of intellectual property co-branding reviews (Fleiss’ κ=0.82) and two public benchmarks, the SAGU-Net achieves 93.62% accuracy and 93.21% Macro-F1, outperforming nine baselines and matching or exceeding LoRA-fine-tuned large language models (GPT-5, Claude Sonnet 4.6, DeepSeek V3.2, Qwen3.5) while requiring three to four orders of magnitude fewer parameters. Ablation confirms the sentiment gate as the single most impactful component.