AGF-HAM: Adaptive Gated Fusion Hierarchical Attention Model for Explainable Sentiment Analysis

The rapid growth of user-generated content in the digital space has increased the necessity of properly and interpretively analyzing sentiment and emotion systems. This research paper presents a new hybrid model, HAM (Hybrid Attention-based Model), a Transformer-based contextual embedding model combined with deep sequential modeling and multi-layer explainability. The suggested framework integrates the BERT/RoBERTa encoders, Bidirectional LSTM, and Graph Attention that can be used to embrace semantic and aspect-level sentiment correlation. Additionally, an enhanced Explainability Module, including Attention Heatmaps, Aspect-Level Interpretations, and SHAP/Integrated Gradients analysis, contributes to the increased model transparency and interpretive reliability. Four benchmark datasets, namely GoEmotions-1, GoEmotions-2, GoEmotions-3, and Amazon Cell Phones and Accessories Reviews, were experimented on in order to have a strong cross-domain assessment. The 28 emotion words of GoEmotions were merged into five sentiment-oriented classes to harmonize the dissimilarity in the emotional granularities to fit the schema of the Amazon dataset. The proposed HAM model had a highest accuracy of 96.4% and F1-score of 94.9%, which was significantly higher than the state-of-the-art baselines like BERT (89.8%), RoBERTa (91.7%), and RoBERTa+BiLSTM (92.5%). These findings support the idea that HAM is a better solution to finer-grained emotional details and is still interpretable as a vital move towards creating open, exposible, and domain-tailored sentiment intelligence systems. Future endeavors will aim at expanding this architecture to multimodal fusion, cross-lingual adaptability, and federated learning systems to increase the scalability, generalization, and ethical application of AI.