A graph fusion network that leverages augmented semantic and syntactic graphs that explicitly model word‐dependency correlations via contextual augmentation, and incorporate selected part‐of‐speech (POS) features to refine semantic graph construction is proposed.
Abstract
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.
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
A Knowledge-Enhanced and Multidimensional Feature Fusion (KEMFF) model for ABSA, which captures sentiment feature representations across multiple dimensions, including syntax, semantics, and knowledge, and aligns them into a unified aspect-level representation.
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.
Ying-Jie Chen, Xiang Li, Dong Chen et al.· Proceedings of the Thirty-Fi...· 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
A sentiment-aware graph anomaly detection framework that integrates graph anomaly detection with a sentiment consistency evaluation mechanism to eliminate misleading dependencies and preserve key sentiment information from the original graph and can be seamlessly incorporated into other graph-based ABSA models to enhance performance and robustness.
Jiawen Wang, Zhaojian Cui, Yu-Bing Li et al.· Neural Networks· 0 citations
Aspect-based sentiment analysis (ABSA) aims to identify the sentiment polarity expressed toward a specific aspect in a sentence. Existing sequential and Transformer-based methods can effectively capture contextual semantics, but they often lack explicit modeling of aspect–opinion relations. Graph-based approaches partially address this limitation by incorporating syntactic dependency structures; however, most rely on deterministic parser-derived graphs or fixed relation weights, which may introduce noisy edges and unstable message propagation for ambiguous, informal, or domain-shifted text. To address these issues, this paper proposes a Bayesian Biaffine Variational Graph Convolutional Network (BBV-GCN) for ABSA. Specifically, a contextual encoder first generates token-level representations for each sentence–aspect pair. A biaffine relation scorer then estimates aspect-aware pairwise token interactions and constructs a soft latent relation graph. Rather than treating relation weights as deterministic values, BBV-GCN introduces latent relation variables and learns their posterior distributions through variational inference, thereby enabling uncertainty-aware graph construction. Based on the learned graph, a variational graph convolutional network performs multi-hop message passing to aggregate opinion cues, modifiers, negation patterns, and contrastive signals toward the target aspect representation. Experiments on five benchmark datasets, including Twitter, Laptop14, Restaurant14, Restaurant15, and Restaurant16, demonstrate that BBV-GCN achieves competitive and well-balanced performance relative to representative attention-based, Transformer-based, and graph-based baselines. Ablation studies further confirm the contributions of Bayesian relation modeling, KL regularization, and variational graph propagation. Visualization results illustrate how uncertainty-aware weighting can attenuate spurious relations and produce more interpretable aspect-specific latent graphs. Overall, BBV-GCN provides a robust and uncertainty-aware graph reasoning framework for fine-grained sentiment analysis.
Wen-Jie Liang, Nan Wang· Applied Sciences· 0 citations
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