Online shopping has evolved significantly with the integration of augmented reality (AR) technology, offering users the ability to visualize products in their physical space before making a purchase. However, sentiment analysis within AR-enabled platforms faces challenges due to sparse review data, unlike traditional e-commerce platforms. The Consistent Fruitfly Optimization-Based Graph Neural Network (CFO-GNN) proposed in this paper addresses this challenge by combining fruitfly optimization with graph neural networks. This innovative approach allows CFO-GNN to efficiently handle sparse data while capturing the intricate relationships present in AR shopping experiences. By leveraging these techniques, CFO-GNN enables more accurate sentiment analysis, empowering businesses to make informed decisions and enhance user satisfaction in the dynamic landscape of AR-enabled online shopping. Through comprehensive evaluation on a diverse dataset, CFO-GNN demonstrates its effectiveness in improving sentiment analysis within AR environments, highlighting its potential to drive advancements in user experience and competitiveness for businesses operating in AR-enabled online retail.