Hybrid Neuro-Fuzzy Deep Learning Framework for Ultra-Precise Biomedical Signal Interpretation in Wearable Healthcare Systems

The explosive growth of social media platforms has generated an unprecedented volume of user-generated content, making sentiment classification a crucial task for understanding public opinion, brand perception, and societal trends. However, the informal language, context-dependent expressions, and sequential dependencies in social media texts pose significant challenges for conventional machine learning and deep learning models. To address these challenges, this study proposes a Hybrid Attention-Driven Recurrent Neural Network (HADRNN) model for sentiment classification of social media texts. The model integrates bidirectional gated recurrent units (Bi-GRUs) with a multi-level attention mechanism, enabling it to effectively capture both global semantic context and fine-grained sentiment cues. Pre-trained GloVe embeddings are employed for robust lexical representation, while convolutional layers extract local n-gram features that complement the sequential modeling capabilities of recurrent units. The attention module adaptively emphasizes sentiment-bearing words and contextual phrases, ensuring both interpretability and improved performance. Extensive experiments were conducted on benchmark datasets including Twitter Sentiment140 and IMDB reviews. The proposed HADRNN achieved an accuracy of 92.1%, F1-score of 91.3%, precision of 91.7%, and recall of 90.8% on Twitter Sentiment140, significantly outperforming baseline CNN (87.2% accuracy), LSTM (88.9% accuracy), and Transformer-based models (90.4% accuracy). On IMDB, the model obtained an accuracy of 94.6% and F1-score of 94.1%, demonstrating robustness to longer text sequences and noisy language. These findings confirm that the hybrid integration of recurrent dynamics, attention, and convolutional processing not only enhances classification performance but also provides interpretable insights into sentiment-laden expressions, offering a reliable framework for real-world social media analytics.