NSAR-T: A Fine-Grained Aspect-Based Sentiment and Transformer Framework for Personalized Recommendations

Abstract

The rapid growth of digital platforms and online review systems has significantly increased the need for intelligent recommendation systems capable of understanding user preferences and contextual sentiments. However, traditional recommendation models often fail to capture fine-grained emotional variations, aspect-level opinions, and semantic relationships present in textual reviews, leading to reduced personalization accuracy. This work proposes a Nuanced Sentiment-Aware Recommendation with Transformers (NSAR-T) model incorporating Aspect-Based Sentiment Analysis (ABSA) with the Robustly Optimized Bidirectional Encoder Representation from Transformers (RoBERTa) to enable the generation of more granular personalized recommendations. A framework for hotel recommendations based on a combination of data sources consisting of 200,000 scraped review records from the primary source of hotels, 878,561 TripAdvisor reviews, and 37.6 million records for Expedia recommendations. The methodology consists of data preparation, extraction of aspects, sentiment classification based on RoBERTa, seven varieties of fine-grained modelling of sentiment, aggregation of sentiment, Principal Component Analysis (PCA) based feature engineering, Cross-Validation (CV), and generation of recommendations from the Top-10 list. The results show an accuracy of 99.85%, precision of 99.91%, recall of 99.81%, F1-Score of 99.86%, Root Mean Square Error (RMSE) of 0.03, Mean Average Precision (MAP) of 0.99, and Normalized Discounted Cumulative Gain (NDCG)@10 of 1; demonstrating superiority over currently employed machine learning and transformer-based recommendation systems with regard to the relevance of recommendations, contextual knowledge of recommendations, and how personalized recommendations are created.