This work introduces an Enhanced Affinity Fusion module that explicitly strengthens aspect–opinion relational modeling by selectively integrating complementary attention mechanisms, and proposes Label-Guided Data Amplification (LGDA), which enhances supervision diversity and domain robustness through label-driven text expansion, hard sample mining, and domain-adaptive sampling.
Abstract
Aspect-Based Sentiment Analysis (ABSA) aims to identify opinion targets and determine the sentiment polarity expressed toward each aspect, which requires fine-grained modeling of aspect–opinion relations. Despite recent advances, cross-domain ABSA remains challenging due to structural mismatches across domains and the scarcity of high-quality labeled data in target domains. Existing methods often struggle to jointly address relational modeling errors and data sparsity, particularly under low-resource and cross-domain settings. To tackle these challenges, we propose a structure- and data-co-enhanced framework for cross-domain ABSA. At the model level, we introduce an Enhanced Affinity Fusion (EAF) module that explicitly strengthens aspect–opinion relational modeling by selectively integrating complementary attention mechanisms. Specifically, EAF combines biaffine attention to capture second-order interactions with syntax-aware attention to inject structural inductive bias, enabling robust modeling of long-distance dependencies without introducing excessive architectural complexity. At the data level, we propose Label-Guided Data Amplification (LGDA), which enhances supervision diversity and domain robustness through label-driven text expansion, hard sample mining, and domain-adaptive sampling. By jointly enhancing structural representation learning and training data supervision, the proposed framework effectively alleviates both aspect–opinion mismatches and cross-domain data sparsity. Extensive experiments on benchmark ABSA datasets demonstrate that our approach consistently outperforms strong baselines and achieves state-of-the-art performance in cross-domain scenarios. Ablation studies further validate the complementary contributions of EAF and LGDA.
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
An Aspect-guided dual-branch fusion network (ADFN) to enhance sentiment prediction by incorporating external knowledge and integrating coarse and fine information is proposed, which incorporates syntactic dependency information to complement and enrich the textual semantic representations.
Bin Song, Wenjing Liu, Zhi Liang et al.· Signal, Image and Video Proc...· 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
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.· 0 citations
