This work proposes the SENSE model, a confidence-aware soft sentiment assignment and a sentiment exchange mechanism to quantify fine-grained preferences and explicitly model sentiment interaction, and employs a confidence-aware soft sentiment assignment and a sentiment exchange mechanism to quantify fine-grained preferences and explicitly model sentiment interaction.
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
Graph-based multimodal recommendation systems leverage visual and textual item features to alleviate data sparsity, yet three systematic limitations persist: textual modeling is confined to item metadata, neglecting review-derived sentiment signals; user-side representations rely on ID embeddings without personalized multimodal alignment; and the standard BPR objective applies uniform gradient pressure irrespective of sample quality. We propose
DAURA
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ual-Channel
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daptive
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ser-Intent
R
outing
A
lignment
), a plug-in enhancement framework comprising three modules: (1) an LLM Review Sentiment Channel that distills affective signals from 5-core user reviews into an independent textual representation, complementing metadata-derived semantic features; (2) an Adaptive Dual-track User-Centric Alignment (AD-UCA) module that constructs per-user multimodal profiles via graph aggregation and employs a lightweight intent router to assign personalized modality-preference weights at the loss level; and (3) a Dual-core Adaptive Margin BPR (DAM) module that fuses sentiment polarity and semantic density to generate item-level adaptive ranking margins. Experiments on three Amazon benchmark datasets and the Yelp Open Dataset show that DAURA consistently outperforms all baselines across product and restaurant recommendation domains, while ablation studies validate the contribution of each module.
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
Multimodal Sentiment Analysis leverages the fusion of heterogeneous data to achieve fine-grained emotional understanding, which finds extensive application in large-scale public opinion monitoring and data mining. However, existing methods face two key challenges: (1) cross-modal alignment suffers from redundancy and semantic drift without explicit modeling of sentiment-critical cues, inducing spurious correlations; and (2) heterogeneous representation spaces lead to imbalanced modality contributions, particularly under weak image–text correlation or sentiment inconsistency. To address these challenges, we propose an explicit sentiment-guided alignment and multi-dimensional cross-modal mutual supervisionbased model for multimodal sentiment analysis. The model primarily employs a fine-grained sentiment–saliency directed alignment mechanism, which leverages bidirectional cross-attention to couple textual sentiment cues with visual saliency, enabling precise localization of sentiment-relevant regions. Furthermore, we introduce a tripartite strong contrastive learning strategy to mitigate distribution discrepancies between heterogeneous modalities within a shared latent space, thereby enhancing cross-modal coherence and complementarity. Finally, we design a noiserobust gating-based fusion module, which, together with text augmentation and deep supervision, facilitates effective joint optimization. Experimental results show that SAMS-M obtains the best results on MVSA-Single and MSD and remains competitive on the noisier MVSA-Multiple benchmark; thus, the evidence supports strong but dataset-dependent performance rather than uniform state-of-the-art superiority.
Shi-Shu Qi, Yulei Zhang, Siyang Zhang et al.· Computer Science and Informa...· 0 citations
User-generated reviews contain rich semantics that can reveal users’ fine-grained preferences beyond what interaction data alone can capture. However, existing review-based recommender systems often fail to achieve semantic alignment between review content and user–item interactions, as they either treat reviews as coarse textual signals or rely on sentiment-oriented heuristics. Achieving such fine-grained alignment is challenging due to the inherent complexity and context dependence of review texts, as well as the semantic gap between explicit review information and implicit interaction preferences. To bridge this gap, we propose LLM-ASAR, a Large Language Model-driven framework for Aspect-based Semantic Alignment in Review-based Recommendation that explicitly aligns aspect-level review semantics with user–item interaction patterns. Specifically, LLM-ASAR leverages LLMs with Chain-of-Thought (CoT) prompting to perform sentence-level reasoning on user reviews, extracting aspect-specific semantics that are structurally integrated into aspect-specific interaction graphs. A multi-aspect contrastive alignment mechanism is further introduced to align user and item embeddings with corresponding aspect-level review features, ensuring consistency across modalities. Extensive experiments on three real-world datasets demonstrate that LLM-ASAR not only achieves significant accuracy improvements over state-of-the-art baselines but also yields interpretable insights into user preferences through its aspect-aware design. Our data and code are available at https://github.com/HuilinChenJN/LLM-ASAR.
Huilin Chen, Zhi-Yong Cheng, Fan Liu et al.· ACM Transactions on Recommen...· 0 citations
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.
Sonal Gupta, Indu Kashyap· International journal of com...· 0 citations
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