The results demonstrate that incorporating explicit syntactic knowledge, adaptive feature fusion, and structured decoding substantially improves cross-domain generalization in fine-grained sentiment analysis.
Abstract
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.
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.
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
This paper presents a context-aware hybrid deep learning approach by integrating the Robustly Optimized BERT Pretraining Approach (RoBERTa) with Bidirectional Long Short-Term Memory (BiLSTM) networks to generate rich contextual word embeddings.
V. Gayatri, Rajani Rajalingam· International Journal for Re...· 0 citations
A novel SeqLab framework is proposed that enhances cross-lingual ABSA using a sequence-to-sequence model with an auxiliary sequence-labelling task performed by the encoder, enhancing aspect term recognition and sentiment predictions.
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
To address the challenges of diverse domain-specific terminology, highly colloquial expressions, and limited annotated samples in sentiment analysis of stock forum texts, this study proposes an ERNIE-Transformer sentiment classification model that integrates ERNIE and Transformer architectures. First, a systematic data preprocessing pipeline was developed, including sentiment label annotation, text cleaning, tokenization, stop-word removal, and sequence length normalization, to construct a high-quality classification corpus. Second, the knowledge-enhanced pretrained ERNIE model was employed as a fixed feature extractor to transform the preprocessed texts into high-dimensional dense vector representations, effectively capturing domain-specific semantics and contextual dependencies in Chinese financial texts. Third, a multi-layer stacked Transformer encoder was introduced as a lightweight classifier. Through the multi-head self-attention mechanism, the encoder performs secondary discriminative feature extraction and adaptive weight allocation on the sentence-level embeddings generated by ERNIE, thereby strengthening semantic signals that contribute significantly to classification decisions. Finally, grid search was applied to optimize key hyperparameters, while early stopping and learning rate scheduling strategies were incorporated to improve the model’s generalization capability.Experiments conducted on a dataset of 6,000 investor comments collected from the Eastmoney Stock Forum, consisting of three sentiment categories (positive, neutral, and negative) with an approximate distribution ratio of 35%/30%/35%, demonstrate that the proposed model achieves accuracy, precision, recall, and F1-score values of 92.75%, 89.14%, 87.21%, and 88.12%, respectively. Compared with the ERNIE fine-tuning baseline, the proposed model improves accuracy, precision, recall, and F1-score by 0.17, 0.70, − 0.25, and 0.20% points, respectively. Compared with the BERT-based Transformer baseline, it achieves improvements of 1.67, 3.35, 3.01, and 3.20% points in the four evaluation metrics. The experimental results indicate that the proposed approach achieves high classification accuracy for financial forum text sentiment analysis. Its decoupled architecture provides an efficient and accurate solution for investor sentiment monitoring.
Xiu-Mei Li, Fei Chen, Wen-Chao Ling et al.· Journal of Electrical System...· 0 citations
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