Aug 2026· Neural Networks· Vol 205 Pt B, pp.
109490
· 0 citations· 43 references
Medicine
TL;DR
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.
Abstract
Aspect-Based Sentiment Analysis (ABSA) aims to determine the sentiment polarity expressed toward specific aspect terms within a sentence. Existing approaches often rely on syntactic dependency parse graphs to model the relationship between aspect terms and opinion words. Despite recent progress, two key challenges remain unresolved. First, dependency parse graphs often contain task-irrelevant yet syntactically valid edges that can mislead sentiment reasoning. Second, meaningful sentiment relations between aspect terms and opinion words frequently span multiple dependency edges, making it difficult for models to capture long-range semantic interactions. To address these challenges, we propose a Sentiment-Aware Graph Anomaly detection and Mix convolution (SAGAM) model. The sentiment-aware graph anomaly detection framework integrates graph anomaly detection with a sentiment consistency evaluation mechanism to eliminate misleading dependencies and preserve key sentiment information from the original graph. To further capture long-range dependencies between aspect terms and opinion words, the mix convolution module combines multi-hop message propagation with edge feature integration, enabling the model to capture both local and distant semantic interactions. Extensive experiments on three widely used ABSA benchmark datasets demonstrate that SAGAM consistently outperforms strong baselines in both accuracy and macro-F1. Notably, the proposed sentiment-aware graph anomaly detection framework can be seamlessly incorporated into other graph-based ABSA models to enhance performance and robustness.
A Probabilistic Syntax-Aware Joint Span–Sentiment Learning (PSJL) approach for ABSA that relaxes discrete span selection into a differentiable span distribution and dynamically incorporates syntactic constraints for end-to-end optimization.
Yuxun Wang· Poster Volume 0007 The 2026...· 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.
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
By integrating textual contextual information, word co-occurrence information, and syntactic dependency structure information, the model captures text features from semantic, lexical, and syntactic perspectives, thereby mitigating the limitations of relying on single-dimensional features.
Multimodal Aspect-Based Sentiment Analysis (MABSA) infers fine-grained sentiment polarity toward specific aspects by jointly modeling text and images. Despite progress in cross-modal fusion, two challenges remain in multi-aspect settings: (1) multimodal noise, where aspect-irrelevant content distracts sentiment learning; and (2) weak cross-modal sentiment alignment, as visual evidence can be ambiguous and textual--visual sentiments may conflict, limiting multimodal complementarity. To address these issues, we propose a Gated Noise-filtered Sentiment-Relevance Interaction (GNSRI) framework. It employs a gated noise-filtering module to suppress sentiment-irrelevant features and enhance aspect-aware sentiment cues, and a sentiment-relevance interaction module to capture consistent and conflicting cross-modal signals at micro and macro levels. Finally, a learnable decision fusion mechanism adaptively combines predictions from textual, visual, and cross-modal branches at the aspect level. Experiments on public MABSA benchmarks show that GNSRI outperforms state-of-the-art methods, improving accuracy by 1.94\% and 2.06\% on Twitter-2015 and Twitter-2017, respectively.
Chen Huang, Liang-Wei Guo, Ya-Min Li et al.· 0 citations
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