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.
Abstract
Existing research on sentiment classification primarily focuses on textual contextual semantics while neglecting word co-occurrence and syntactic structure information. It also emphasizes semantic analysis of sequential structures but overlooks non-linear structural semantics. To address these limitations, we propose the DGCNCF (Dual-Graph Convolutional Network with Context Fusion) model for sentiment classification. The model employs a BiLSTM with an attention mechanism to extract textual contextual information. Subsequently, a text co-occurrence graph and a syntactic dependency graph are constructed separately. Meanwhile, contextual information is fused with node information within these graphs to obtain graph embedding representations of the text. Then, the model performs representation learning on the two graphs using GCN, thereby capturing the global word co-occurrence features and global syntactic dependency structure characteristics of the text. 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. The validity of the model is verified on two public datasets, and the experimental results demonstrate that the model achieves effective sentiment classification performance.
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.
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
An innovative model which combines transformer-based context embedding, BiLSTM for capturing of sentiment flows, and GAT for examining relational data is introduced which incorporates contextual, sequential and relational modelling of multilingual opinion mining.
Manoharan Thangavel, A. Kalpana, Saravanan Ananth· Serbian Journal of Electrica...· 0 citations
An integrated framework based on transformer architecture for topic modeling and sentiment analysis for Hindi and Italian social-media discourse, customer reviews and news corpus is introduced and it is suggested that there is clear benefit for morphologically complex text and mixed script text for using contextual embeddings and language-specific pretraining.
Sunita Basalingayya, T. J. Peter· Journal of Intelligent Decis...· 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, Zhipeng Liang et al.· Signal, Image and Video Proc...· 0 citations
English writing texts often show complex semantic layers, implicit emotional expression, and strong temporal dependence, which leads to limitations in semantic modeling and restricted feature extraction in existing methods. To address this issue, the study constructed a fine-grained sentiment recognition model that integrates bidirectional encoder representations from transformers (BERT), bidirectional gated recurrent unit (BiGRU), convolutional neural network (CNN), and an attention mechanism. BERT was used to generate context-aware semantic representations and improve overall semantic understanding of the text. BiGRU was applied to capture bidirectional temporal dependencies and describe the dynamic evolution of emotions in discourse. CNN was employed to extract phrase-level local emotional features and enhance the detection of emotion-triggering segments. The attention mechanism was introduced to highlight key emotional information and improve feature discriminability. On this basis, a gating fusion strategy was used to dynamically integrate multi-source features, and a multi-task learning framework was incorporated. The model performed emotion intensity prediction while conducting multi-class emotion classification. In this way, fine-grained sentiment modeling was achieved from both category and intensity perspectives. The results indicate that the proposed model achieves excellent performance across several datasets, with overall accuracy and F1-score remaining above 91%, ROC-AUC reaching up to 0.944, and recognition rates for all emotion categories staying above 84%, clearly outperforming existing mainstream approaches. Ablation results demonstrated that all six modules contributed to performance improvement. On the SST-2 and IMDB datasets, the complete model achieved an accuracy of 93% and an AUC of 0.97. In summary, this research offers a compact, stable, and adaptable solution for emotion modeling in English texts, with both theoretical and practical value.
Zhigang Peng· Journal of Advanced Computat...· 0 citations
