Jul 2026· International journal of computer information systems and industrial management applications· Vol 18, pp. 251-278· 0 citations
TL;DR
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.
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.
Organizations can now tap into fine-grained opinions about products or service features that can be extracted from customer reviews and ratings with aspect-based sentiment analysis (ABSA). The study introduced two models, the first for aspect extraction and another one involved in sentiment analysis using bidirectional encoder representation transformer (BERT). With a testing accuracy of 98%, the aspect extraction model produces outstanding results, corroborated by metrics for precision, recall, and F1-score for every class. Furthermore, the ABSA model gives remarkable progress over earlier research, attaining an 82% testing accuracy. The proposed framework illustrates how well the BERT-based ABSA model accurately identifies and evaluates various aspects of goods or services, as indicated in customer feedback, and adds value to the body of current sentiment analysis literature, suggesting useful recommendations for improving the explanation and understanding of customer feedback. There is a chance that this research project will help consumers and businesses alike.
Arwa Akram, A. Sabir· Basrah journal of science· 0 citations
This paper introduces an efficient DistilBERT-Attention model for aspect-based sentiment analysis (ABSA), designed to balance classification accuracy against computational cost. Unlike general sentiment analysis, which assigns a single polarity to a complete review, ABSA identifies the product aspects discussed within individual sentences or clauses, such as design, quality, and price, and determines the sentiment polarity expressed toward each of them. The proposed model combines DistilBERT, a compact transformer encoder, with a modified aspect-focused attention layer that captures fine-grained sentiment signals efficiently. Experiments were conducted on 3,152 valid textual reviews, drawn from an initial collection of 3,259 Amazon India reviews of Titan watches published in 2024, across five aspect categories: design, quality, price, functionality, and comfort. The proposed model achieved an accuracy of 84.7% and an F1-score of 0.81. Compared with traditional baselines, it improved accuracy by 13.5 percentage points over a support vector machine and by 13.9 percentage points over logistic regression. Although BERT-base achieved a slightly higher accuracy of 86.1%, the proposed model retained approximately 98.4% of BERT-base accuracy while reducing memory consumption by about 40% and lowering relative processing time from 2.5x to 1.5x. The main contribution of the proposed model is therefore not absolute superiority over large transformer models, but an improved balance between accuracy, interpretability, and computational efficiency for resource-constrained ABSA applications.
Mohammad Abu Kausar, M. Nasar, Sallam O. f. Khairy et al.· Journal of Computers, Mechan...· 0 citations
A novel recommendation algorithm that combines Aspect-Based Sentiment Analysis (ABSA) and a Graph Attention Network (GAT) to improve the recommendation quality and allow for improved interpretability because of the aspect-level sentiment representation.
S. Raipure, Balaji A· Journal of Information Assur...· 0 citations
The LSTM model achieved the highest accuracy of 91% and an area under the curve (AUC) of 90%, outperforming the other models and enabling more accurate sentiment-based predictions and enhanced recommendation quality.
Wael Haider, Mohamed I. Marie, Hebatullah Samir et al.· International Journal of Eng...· 0 citations
The rapid growth of e-commerce websites has caused the number of customer reviews to rapidly increase, making the analysis of such reviews more challenging than ever before. Existing approaches to summarizing the content of customer reviews primarily focus on lexical similarity while paying little attention to product aspect preservation, sentiment alignment, and redundant information filtering. In this work, we propose a Sentiment-Aware Redundancy-Minimized Maximum Marginal Relevance (SA-RMMR) framework for creating informative extractive summaries that can preserve product aspects, customer sentiments, and informational diversity. Our framework relies on SBERT-based semantic representation of text, TF-IDF and part-of-speech-based aspect extraction, VADER-based sentiment alignment, and a redundancy-aware maximum marginal relevance ranking. In order to make our framework robust, we introduce semantic centrality estimation, adaptive retrieval confidence, reward mixing, and balance adjustment. The quality of the created summaries is estimated with conventional lexical metrics and a new metric called Composite Quality Index (CQI) that jointly evaluates semantic relevance, aspect coverage, sentiment alignment, and redundancies. We performed experiments on several Amazon product reviews datasets and have shown that our framework was the best among all methods considered in the experiment, obtaining the highest Aspect Coverage (0.835) and Sentiment Alignment (0.719) scores while having competitive semantic similarity (BERTScore-F1 = 0.825). Our framework significantly outperformed classical extractive approaches and transformer-based models, including BART, PEGASUS, and FLAN-T5, in overall quality of summaries produced by them. Ablation studies showed that semantic representation of sentences, aspect awareness, adaptive retrieval, and redundancy elimination all have positive impact on performance of the framework. The proposed SA-RMMR framework represents an efficient and interpretable approach to customer review summarization that takes into account not only semantic relevance but also product aspects, sentiment preservation, and redundant information filtering. Additionally, the proposed Composite Quality Index allows evaluation of the quality of summaries from multiple perspectives, not limited to conventional lexical overlap, which makes it a good choice for opinion mining tasks in e-commerce.
Vijay H. Kalmani, Amol C. Adamuthe, P. Bagane· Discover Artificial Intellig...· 0 citations
Aspect Based Sentiment Analysis (ABSA) aims to determine sentiment with respect to specific aspects of a text, providing more detailed insights than conventional sentiment analysis which assigns a single polarity to the whole text. However conventional techniques often fail to capture fine grained aspect level sentiment limiting their effectiveness in real world applications such as product reviews and customer feedback analysis. In this article proposes an innovative ABSA framework that synchronizes enhanced feature engineering with a lightweight hybrid deep learning architecture. In the proposed method Text preprocessing is done using a BERT tokenizer followed by feature extraction with an improved TF-IDF approach and Aspect Term Extraction (ATE) allowing the model to capture both global context and aspect level information. A hybrid classifier synchronizes Link-Net and SqueezeNet for fast and accurate sentiment classification. Experiment was conducted on data for Restaurant Reviews containing 10,000 reviews. The dataset splitting in training, validation and testing set 70:15:15 respectively. Fiive-fold cross-validation was applied to ensure robustness of proposed framework. Outcome of analysis demonstrate that the proposed farmwork gained a high accuracy of 97.7%, sensitivity 96.4% and F-1 Score 91.0% as compare to several traditional framework. The Proposed technique suggests a balanced tradeoff between performance and computational cost making it suitable for real time ABSA applications.
