@article{lu2025asyncnet,title={TBA},author={Lu, Qingjie and TBA},journal={ACM Conference on Computer and Communications Security},year={2025},note={Under Review}}
2022
ISET
Neural Network-Based Approaches for Aspect-Based Sentiment Analysis
Qingjie
Lu
Highlights in Science, Engineering and Technology, 2022
Proceedings of the 4th International Conference on Information Science and Electronic Technology
The research of Aspect-based Sentiment Analysis which is a process that has a more specific focus than general sentiment analysis is trending upwards in numbers. Stemming from Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), novel approaches introduced new components like Graph Convolutional Networks (GCNs) and Transformers that improved the overall accuracy dramatically. Along with summarizing the models, the focus of this survey will be on comparing the several novel methods. Although this paper found that Dependency graph enhanced dual-transformer network (DGEDT) coupled with Bidirectional Encoder Representations from Transformers (BERT) is the best performing model thus far, this paper also identified challenges that needed to be addressed in order to better evaluate current and future models.
@article{lu2022aspectbased,title={Neural Network-Based Approaches for Aspect-Based Sentiment Analysis},author={Lu, Qingjie},journal={Highlights in Science, Engineering and Technology},volume={12},pages={222--229},year={2022},doi={10.54097/hset.v12i.1457},url={https://drpress.org/ojs/index.php/HSET/article/view/1457},note={Proceedings of the 4th International Conference on Information Science and Electronic Technology}}