FEAM: a dynamic prompting framework for sentiment analysis with hierarchical convolutional attention

Introduction This paper proposes FEAM (Fused Emotion-aware Attention Model), a dynamic prompting framework for sentiment analysis. Unlike static or handcrafted templates, FEAM dynamically selects input-specific prompts, aiming to address the challenges of nuanced sentiment expressions, lexical ambiguity, and domain variability. Methods The framework integrates a query-aware prompt controller with a BERT encoder to generate contextualized representations. Emotion-aware modulation amplifies sentiment-bearing features, multi-scale convolution captures linguistic patterns at different granularities, and topic-aware attention aligns local cues with global semantics. Experiments are conducted on four benchmark datasets: Rest16, Laptop, Twitter, and FinancialPhraseBank. Results FEAM achieves F1-scores of 91.55% on Rest16, 92.83% on Laptop, 93.10% on Twitter, and 90.11% on FinancialPhraseBank, outperforming strong baselines such as transformer-based, graph-enhanced, and prompt-tuned models. Ablation studies verify the contribution of each module, and robustness tests demonstrate resilience to adversarial perturbations and domain shifts. Discussion The results show that FEAM effectively improves sentiment classification across diverse and noisy textual domains. By combining dynamic prompting with emotion-aware modeling and hierarchical convolutional attention, FEAM provides a scalable and robust framework for real-world sentiment analysis, with potential extensions in domain adaptation, multimodal integration, and automated prompt discovery.