Aug 2026· Entropy· Vol 28, pp. 930· 0 citations· 52 references
Medicine
TL;DR
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.
Abstract
Aspect-based Sentiment Analysis (ABSA) is a fine-grained sentiment classification task that aims to predict the sentiment polarity associated with aspect terms in sentences. Traditional methods based on syntactic and semantic dependency trees are insufficient for capturing contextual sentence features. To address this, we propose a Knowledge-Enhanced and Multidimensional Feature Fusion (KEMFF) model for ABSA, which captures sentiment feature representations across multiple dimensions, including syntax, semantics, and knowledge. First, the pre-trained model RoBERTa is used to obtain embeddings of sentences and aspect terms. Then, a syntactic dependency parser and a graph convolutional network are utilized to learn syntactic features. Meanwhile, an Abstract Meaning Representation (AMR)-based parser is employed to construct semantic relations, and axial attention is used to aggregate incoming and outgoing semantic dependencies. Furthermore, external knowledge is embedded, and an attention mechanism is employed to obtain aspect-specific knowledge representations, thereby complementing syntactic and semantic representations with external lexical knowledge. Finally, multidimensional features are fused and passed to a softmax classifier for predicting sentiment polarities. Unlike previous models that mainly focus on either syntax–semantic fusion or knowledge-enhanced graph propagation, KEMFF explicitly models syntax, semantics, and lexical knowledge in three parallel branches and aligns them into a unified aspect-level representation. Experiments on Laptop14, Restaurant14, and Twitter datasets show that KEMFF achieves the best performance among the compared baselines on Laptop14 and Restaurant14, and it obtains competitive results on Twitter.
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.
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
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
Sentiment analysis for Greek social media texts remains challenging because of limited annotated resources, linguistic variation, and domain-dependent sentiment expression. This study presents a topic-aware, lexicon-guided framework for sentiment classification across five reference domains in Greek social media. Domain-specific sentiment lexicons are activated according to the relevant domain and transformed into a structured representation comprising a token-level multi-channel lexical matrix and aggregate lexical descriptors. A fusion convolutional neural network combines these complementary components to classify sentiment while retaining explicit lexical evidence for inspection. The evaluation follows a leakage-free protocol: lexicons are constructed exclusively from the sentiment inner-training subset, validation data are used for model selection, and a held-out test set is reserved for final evaluation. The proposed fusion CNN achieved the strongest held-out sentiment result among the evaluated models, with an Accuracy of 0.8029 and a Macro-F1 of 0.7883, exceeding TF–IDF + Linear SVM and fine-tuned GreekBERT baselines in the present experimental setting. Ablation results show that the token-level lexical matrix and global descriptors provide complementary information. For domain routing, GreekBERT late fusion achieved an Accuracy of 0.9162 and a Macro-F1 of 0.9116. When lexicon activation used predicted rather than reference domains, the end-to-end sentiment pipeline achieved a Macro-F1 of 0.7569. These findings indicate that explicit domain-specific lexical knowledge can support an interpretable sentiment representation while making the effects of lexical coverage and topic-routing uncertainty visible.
Kyriakos Skoularikis, I. Savvas· Applied Sciences· 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
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