Built a hybrid sentiment model using BERT-based augmentation, transformer embeddings, ensemble learners, and a CNN-BiGRU-Attention network. Reached 91.15% accuracy on Amazon reviews via stacking and Optuna hyperparameter tuning. Used contextual augmentation, mpnet embeddings, and SMOTE to improve class balance and data quality

Hybrid Sentiment Analysis BERT and CNN Ensemble - This project merges BERT's deep semantic understanding with CNN's feature extraction for more accurate sentiment analysis. Through careful optimization, it significantly improves sentiment analysis precision, promising better text analysis in various applications.