2026· Annual Meeting of the Association for Computational Linguistics· pp. 595-608· 0 citations· 35 references
Computer Science
TL;DR
Experiments on Lap14, Rest14, and MAMS demonstrate consistent improvements over strong graph-based baselines, suggesting that hypergraph-based relational modeling generalizes effectively to short-text sentiment composition.
Abstract
Aspect-Term Sentiment Analysis (ATSA) aims to predict sentiment polarity for specific aspect terms, a task complicated by conflicting sentiments and limited context in short texts. Existing graph-based approaches rely on pre-defined pairwise structures to capture different linguistic views. However, this leads to two key limitations: (1) their pairwise formulation often requires multiple graphs to improve expressive capacity, and (2) their reliance on predefined parsers or heuristic graph construction limits adaptability to sentence-specific sentiment composition. We propose HyperATSA, a dynamic hypergraph framework that overcomes these limitations through a single instance-specific hypergraph constructed directly from contextual token representations. Hyperedges are dynamically induced via Hierarchical Agglomerative Clustering (HAC) over token embeddings, where an acceleration-based cutoff identifies sentence-specific semantic groupings and enables adaptive hypergraph construction. Experiments on Lap14, Rest14, and MAMS demonstrate consistent improvements over strong graph-based baselines, suggesting that hypergraph-based relational modeling generalizes effectively to short-text sentiment composition.
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.
Xinfeng Liao, Xuanqi Chen, Lianxi Wang et al.· Knowledge and Information Sy...· 0 citations
Aspect-based sentiment analysis (ABSA) demands more than a single polarity label per document — it requires assigning a distinct sentiment to each named attribute a text discusses, even when the relevant opinion words are syntactically distant, negated, or figurative. Existing graph convolutional approaches to ABSA rely on three design decisions that limit how far they can go: sentiment lexicon weights computed once before training that cannot adapt to how words are used in context; a single deterministic dependency parser whose errors embed directly into the adjacency matrix with no correction path; and graphs built exclusively from token-level nodes, leaving clause-level and phrase-level sentiment units without any dedicated representation. MH-ABSA addresses each of these gaps through five independently ablatable modules. LLM-Guided Dynamic Edge Reweighting (LDEM) substitutes static lexicon scores with context-sensitive weights produced by a frozen instruction-tuned language model, blended into the adjacency matrix through a learnable coefficient. Ensemble-Uncertainty Dependency Fusion (EUDF) combines three structurally diverse parsers through a sentence-conditioned MLP, allowing the model’s trust in each parser to adapt with the input register. The Multi-Scale Heterogeneous Graph (MHG) extends the token-level graph to three tiers — token, span, and discourse — so that clause-level sentiment signals can propagate to individual aspect tokens in a single message-passing hop rather than through a long token chain. Two further modules round out the design. Contrastive Sentiment Alignment Module (CSAM) introduces a contrastive training objective built around LLM-generated paraphrases and domain-internal aspect swaps, giving the model a second gradient pathway that operates on meaning rather than surface vocabulary — useful precisely because sarcasm and implicit sentiment defeat both the lexicon and the parser. Domain-Adaptive Edge Masking (DAEM) addresses a subtler problem: polarity scores that are globally reasonable but locally wrong for a given product category, which it handles through per-edge learned gates that suppress unreliable weights at training time. The proposed framework addresses several limitations of existing graph-based ABSA systems, including static lexicon weighting, parser uncertainty, and lack of multi-scale linguistic representations.
Niranjan Kumar· Journal of King Saud Univers...· 0 citations
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.
Yongjie Wang, Zhiyuan Ma, Yuze Wang et al.· Expert systems· 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
Aspect-Based Sentiment Analysis (ABSA) aims to identify aspect terms in text and predict their sentiment polarities. The two subtasks are tightly coupled: inaccurate aspect boundaries can mislead sentiment prediction, while sentiment supervision should in turn refine span selection. However, existing methods still suffer from two limitations. First, although boundary detection and sentiment classification are interdependent, gradients between them are often weak, resulting in error propagation, especially in pipeline settings. Second, under complex syntactic phenomena such as negation and contrast, boundary shifts and the mismatch between training on gold spans and inferring on predicted spans cause a train–inference discrepancy and unstable optimization.
To address these issues, we propose a Probabilistic Syntax-Aware Joint Span–Sentiment Learning (PSJL) approach for ABSA. PSJL relaxes discrete span selection into a differentiable span distribution and dynamically incorporates syntactic constraints for end-to-end optimization. Specifically, (i) Probabilistic Joint Modeling (PJM) computes expectation-based span representations from a span distribution, enabling sentiment loss to backpropagate to boundary prediction. (ii) Syntax-Constrained Contrastive Learning (SCCL) derives span-level masks from dependency parse trees to contrast syntactically plausible and implausible candidates, regularizing boundary decisions. (iii) Adaptive Training Strategy (ATS) adaptively balances supervision from gold and predicted spans based on a syntax-consistency rate, mitigating train–inference discrepancy and improving training stability. Experiments on three benchmark datasets show that PSJL consistently outperforms state-of-the-art models and strong LLM-based baselines on aspect term extraction and sentiment classification.
Yuxun Wang· Poster Volume 0007 The 2026...· 0 citations
Aspect-Based Sentiment Analysis (ABSA) often experiences a significant performance decline in cross-domain settings due to vocabulary variation and domain-specific aspect expressions. Although transformer-based models achieve strong in-domain performance, they primarily rely on contextual embeddings and often ignore the syntactic structures that remain consistent across domains. Existing methods rarely integrate structured decoding with adaptive syntactic fusion for robust aspect boundary detection. This paper proposes a syntactic-aware cross-domain ABSA framework based on DeBERTaV3 and BIO-CRF decoding to alleviate the above problems. The proposed model introduces part-of-speech and dependency-relation embeddings, in addition to contextual embeddings, and uses an attention-based model to dynamically fuse syntactic and semantic information at multiple levels. We further apply a Conditional Random Field (CRF) layer to enforce valid BIO transitions and enhance the consistency of multi-word aspect spans under domain shift. The model was evaluated in three English review domains: Restaurant, Laptop, and Device across six zero-shot cross-domain transfer settings (D→L, D→R, L→D, L→R, R→D, and R→L). Test results show consistent advances over robust transformer-based and prompt-based baselines. The proposed method yields F1 scores for aspect extraction between 0.72 and 0.81 and achieves sentiment classification accuracies between 74.32% and 85.19%. The best performance was achieved in the L→R transfer setting. Through paired bootstrap testing (p < 0.01), Statistical analysis confirms that the proposed model achieves significant improvements over baseline methods. The results demonstrate that incorporating explicit syntactic knowledge, adaptive feature fusion, and structured decoding substantially improves cross-domain generalization in fine-grained sentiment analysis.
U. Teki, P. Ranjana· Future Technology· 0 citations