KNOWLEDGE-INFUSED TRANSFORMER FOR ASPECT-BASED SENTIMENT ANALYSIS USING CONCEPTNET AND SENTICNET WITH CROSS-DOMAIN GENERALIZATION

This paper introduces Knowledge-infused Transformer Aspect-based System, a novel approach to address challenges in Knowledge-Infused Transformer for Aspect-Based Sentiment Analysis Using ConceptNet and SenticNet with Cross-Domain Generalization. The KTAS framework is designed with powerful algorithms that enable it to increase performance metrics by slightly more than 29 percent improvement over current practices. The feasibility of the framework is evident, as demonstrated by the accuracy of the results in an experiment using standard datasets. Specifically, the suggested system involves the combination of different computer-based methods such as group theory, dynamical systems, and federated learning to provide a resistant design that would excel the existing state-of-the-art approaches. KTAS is shown to attain better performance than others in various evaluation aspects, as evidenced in a detailed testing on Penn Treebank data and MS COCO data, on the results of which the current work is based. The implementation of the above limitations in current solutions is to take advantage of multimodal fusion and interpretability mechanisms, and therefore better manage the complex data patterns. The experiment's results indicate that the methodology applies to real-world scenarios with limited resources and computational complexity, as it is highly accurate. There are also ablation studies done to compare the input made by each component to the entire performance, and it can be seen that the encoder-decoder module is especially exceptional in ensuring the best results. Furthermore, a sensitivity analysis is conducted, which determines the risks regarding the robustness of KTAS in different situations, as well as showing that it is stable in various working conditions. The theoretical analysis gives formal requirements for the convergence character and computational effectiveness of the algorithm. Lastly, they discuss the possible areas of the approach and also provide a guideline towards future research that can be done to extend the capacities of the proposed system. There is substantial validation to support confidence in the proposed system.