This paper presents an enhanced Bidirectional Long Short-Term Memory (Bi-LSTM) network with attention mechanism for sentiment analysis in e-commerce user-generated content, integrating approaches from cognitive science, linguistics, and deep learning. Drawing on cognitive attention theories and linguistic frameworks for sentiment expression, our model implements a cognitively-inspired multi-head attention mechanism combined with domain-specific word embeddings. Experiments conducted on a combined dataset of 75,000 reviews from IMDb and Stanford Sentiment Treebank, supplemented with 10,000 Amazon product reviews, demonstrate the model’s effectiveness. The enhanced Bi-LSTM achieves 91.8% accuracy, showing improvements of 4.2% over vanilla Bi-LSTM and 2.7% over BERT-base models. Computational efficiency analysis reveals a 35% reduction in inference time while maintaining stable memory utilization at 2.8GB during peak operation. This interdisciplinary approach effectively bridges theoretical insights from cognitive science with practical applications in natural language processing, providing robust solutions for real-world sentiment analysis challenges.