Graph Attention-Based Engineering Framework for Aspect-Level Sentiment-Driven Recommendation Using BiLSTM–CRF Hybrid Model

Sentiment-aware Rating-based Recommendation via Semantic-enhanced Item Alignment

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

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.

Enhancing Aspect‐Based Sentiment Analysis via Augmented Semantic and Syntactic Graph Fusion

Recommendation systems are rapidly evolving from static interaction‐driven models to dynamic, knowledge‐augmented architectures. A key challenge in this evolution is accurately capturing users' fine‐grained preferences from unstructured review text, which directly impacts the explainability and personalization of recommendations. As an essential enabling technology, Aspect‐Based Sentiment Analysis (ABSA) extracts aspect‐level sentiment elements that can be explicitly mapped to user preference vectors or product attribute ratings. With the integration of semantic and syntactic information, current works have significantly enhanced the performance of ABSA. However, existing graph‐based approaches that rely on dependency‐tree structures often converge to suboptimal solutions when handling implicit sentiment in natural language. To address this gap, we propose a graph fusion network that leverages augmented semantic and syntactic graphs. Specifically, we explicitly model word‐dependency correlations via contextual augmentation, and incorporate selected part‐of‐speech (POS) features to refine semantic graph construction. Concurrently, a syntactic graph is constructed by pruning the nodes based on the distance to the aspect term. The resulting semantic and syntactic representations are then fused through a dual graph convolutional network block, whereas the gating mechanism is used to regulate information flow during graph construction. Experiments on seven benchmarks demonstrate that our approach outperforms baselines by up to 2.39%$$ 2.39\% $$ and 2.44%$$ 2.44\% $$ in Macro‐F1 scores, establishing new state‐of‐the‐art results and providing a more reliable sentiment extraction module for downstream recommendation tasks.

Multi-aspect sentiment classification on USA-based Amazon customer reviews with DeBERTa-v3 encoders and aspect attention

Aspect-level sentiment analysis of e-commerce reviews is important for identifying fine-grained customer experience signals that are often hidden by overall star ratings. This study proposes a dual-track framework for USA-based Amazon customer reviews, combining text-driven multi-aspect sentiment classification with structured-feature satisfaction prediction. For the text-based task, reviews are modeled across three operational aspects: Product & Value, Delivery & Fulfillment, and Service & Usability, each using a three-class sentiment scheme of Negative, Neutral, and Positive. The proposed deep learning model integrates DeBERTa-v3 contextual embeddings, bidirectional recurrent refinement, and aspect-specific additive attention to separate overlapping sentiment cues within the same review. Three recurrent variants, Bi-GRU, Bi-RNN, and Bi-LSTM, are evaluated under the same modeling framework. On the held-out Amazon Customer Reviews test set, the DeBERTa-v3 + Bi-GRU + aspect-attention model achieves the best overall performance, with 0.737 accuracy, 0.757 macro-F1, and 0.760 weighted-F1, outperforming the Bi-LSTM and Bi-RNN variants. Ablation analysis further shows that sequential refinement and aspect-specific attention improve performance over encoder-only and shared-attention configurations. In the structured-feature satisfaction task, LightGBM achieves the strongest result among the evaluated machine learning models, with 82.37% accuracy and an F1-score of 0.82, outperforming XGBoost and CatBoost. The results show that the proposed framework provides complementary insights: structured models support overall satisfaction prediction, while the DeBERTa-v3-based aspect model provides more detailed attribution of customer experience across product, delivery, and usability dimensions. Overall, the study offers a reproducible and interpretable approach for fine-grained review analytics in e-commerce settings.

A smart recommendation model for airline reviews via deep learning sentiment analysis (text ratings fusion with LSTM)

This study develops a smart recommendation model based on deep learning (DL) for sentiment analysis of airline customer feedback. It uses textual content and ratings to provide personalized recommendations. We evaluate four DL models— Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and 1D Convolutional Neural Network (COV1D) —for sentiment analysis of airline customer feedback. The LSTM model achieved the highest accuracy of 91% and an area under the curve (AUC) of 90%, outperforming the other models. The results show that all models achieved significant improvements in accuracy, precision, recall, and F1-score compared with baseline methods. By analyzing sentiment polarity in user reviews, the model inferred user preferences and the relationship between sentiment and ratings. Overall, its ability to capture textual context enabled more accurate sentiment-based predictions and enhanced recommendation quality.

Feature-level enhanced syntactic-semantic graph networks via optimal transport for aspect-based sentiment analysis

The Syntactic-optimal Transport Graph Network (SOT-Graph) is proposed, a model that jointly integrates structural and distributional signals and outperforms existing baselines by a margin of 1.30% Macro-F1 on Laptop14 and 1.01% on Twitter.