Aug 2026· International Conference on Machine Vision and Deep Learning· Vol 14326, pp. 143261B - 143261B-8· 0 citations· 5 references
Engineering
TL;DR
This study proposes a teaching evaluation text analysis framework integrating knowledge graph embedding, semantic-enhanced encoding, and joint multi-task learning to address the difficulty of accurately analyzing large-scale unstructured teaching evaluation texts.
Abstract
To address the difficulty of accurately analyzing large-scale unstructured teaching evaluation texts, this study proposes a teaching evaluation text analysis framework integrating knowledge graph embedding, semantic-enhanced encoding, and joint multi-task learning. Based on approximately 30,000 course evaluation records, a teaching evaluation knowledge graph containing about 8,000 entity nodes and 35,000 relation edges is constructed to model heterogeneous associations among courses, teachers, evaluation aspects, and labels. A semantic-enhanced multi-channel encoding mechanism is then designed to fuse contextual text representations, graph embeddings, and structural features into a shared representation space. On this basis, a joint multi-task learning framework is developed to simultaneously perform sentiment polarity classification, evaluation aspect classification, and satisfaction regression. Experimental results on real university data show that the proposed model achieves an Accuracy of 0.915 in sentiment polarity classification and 0.886 in evaluation aspect classification, with Macro F1 improving by approximately 1.4 and 2.1 percentage points, respectively, compared with TF-IDF + SVM and text-only deep models. In addition, the deployed system maintains an average response time of about 295 ms and a QPS of approximately 200 under 200 concurrent requests, demonstrating good engineering efficiency and robustness.
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
In the context of large-scale online learning environments, analyzing student feedback is crucial for improving course content and learner engagement. This paper proposes MTL-BERTNet, a novel multi-task learning architecture that jointly performs aspect category classification and sentiment polarity detection from MOOC reviews. The model leverages contextual embeddings from a pre-trained BERT encoder and integrates a convolutional multi-head attention mechanism to capture subtle semantic nuances and inter-task dependencies. To further enhance shared representation learning across tasks, an inter-task matching layer (IML) is introduced. Experiments conducted on an imbalanced MOOC review dataset demonstrate strong performance, with macro F1-scores of 0.90 for aspect classification and 0.93 for sentiment prediction. These results highlight the effectiveness of jointly modeling aspects and sentiment, offering practical insights for improving course design, instructional quality, and learner satisfaction in MOOC platforms.
Raja Ouadad, Hicham Mouncif· Infocommunications journal· 0 citations
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.
Cross-domain e-commerce recommendation faces challenges from multimodal product heterogeneity, sparse intercategory associations, and opaque recommendation reasoning. To improve accuracy and interpretability, this study proposes a multimodal large-language-model-driven framework for self-evolving cross-domain product knowledge graphs and explainable recommendation. Product images, titles, and attributes from apparel, home-furnishing, and digital-product domains are encoded using a multimodal large model, mapped into a shared latent space, and aligned through contrastive learning for cross-domain entity and semantic association extraction. A self-evolution mechanism uses the large language model as a relation verifier and reasoning engine to validate, buffer, prune, or extend graph edges according to confidence and interaction feedback. A path-aware graph neural network then samples multi-hop cross-domain paths, encodes product sequences through gated recurrent units, and fuses graph representations with original multimodal embeddings. Recommendation explanations are generated from high-weight inference chains under loyalty constraints to ensure factual consistency. Experiments on a large e-commerce dataset show that the proposed method achieves Hits@10 of 0.892 and MRR of 0.537 for relation completion, while CTR, CVR, and Recall@20 reach 12.4%, 6.2%, and 22.1%, respectively. Explanation fidelity and perceived usefulness score 4.23 and 4.15. The framework supports semantic alignment, graph-based reasoning, and interpretable recommendation in multimodal information systems.
Li Ma, Yuanli Cui· Advanced Electromagnetics· 0 citations
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
In neural topic modeling, the reliability of a document representation varies from one document to the next: a syntactic view drawn from a dependency graph is informative when the parse is well formed, whereas a semantic view drawn from a pretrained transformer is more dependable when the parse is sparse or fragmented. Existing hybrid models combine the two views with a fixed rule, set once for the whole corpus through concatenation, averaging, or a constant weight, so the locally less reliable view is over-weighted on individual documents. To close these gaps, a Graph-Adapted Semantic Topic Model with Instance-Aware Dual-View Fusion (GASTM) is proposed. Within a variational autoencoder, a GGNN-based dependency encoder and a pretrained transformer semantic encoder are kept jointly trainable, while corpus-wide lexical statistics are carried through a learnable node embedding on the graph branch. A continuous per-document fusion weight is then produced by an instance-aware adaptive gate, allowing the two views to be combined in a controlled manner before reaching the latent inference space. On five benchmarks, GASTM is shown to consistently outperform the baselines on both topic quality and downstream classification. A fusion-type ablation further yields counterintuitive findings that support the effectiveness of the adaptive gate strategy over static alternatives.
R. Kuo, Thi-Thuy-Quynh Trinh· IEEE Access· 0 citations