Sentiment Analysis for User-Generated Content-Based Hybrid Recommendation with Collaborative Multi-View Fusion

Abstract

This research paper introduces a groundbreaking approach to recommendation systems,termed "Sentiment Analysis with Ensemble Hybrid Deep Learning Model." In an era of data abundance, conventional recommendation systems grapple with the formidable challenges of accurately gauging user preferences and mitigating rating biases. To surmount these limitations, our methodology fuses the power of deep sentiment analysis, delving into user comments to uncover emotional cues that rectify deviations in user ratings and provide a nuanced grasp of their preferences. Simultaneously, we harness neural networks to transform item content descriptions into distributed paragraph vectors, bolstering content-based recommendations. Further bolstered by data selection strategies founded on confidence estimation and cluster analysis, our methodology aims to redefine recommendation systems, promising more precise, emotionally resonant, and personalized content recommendations, ultimately elevating user satisfaction and engagement.