[1]DUAN Wenjie,DENG Jinke,ZHANG Shunxiang,et al.Aspect-based sentiment analysis model based on multilevel knowledge enhancement[J].CAAI Transactions on Intelligent Systems,2024,19(5):1287-1297.[doi:10.11992/tis.202308044]
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Aspect-based sentiment analysis model based on multilevel knowledge enhancement

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 5 Page number: 1287-1297 Column: 学术论文—人工智能基础 Public date: 2024-09-05
Title:
Aspect-based sentiment analysis model based on multilevel knowledge enhancement
Author(s):
DUAN Wenjie1; 2;  DENG Jinke1; 2;  ZHANG Shunxiang1; 2; 3;  LI Shuyu1; 2;  ZHOU Ruotong1; 2
1. School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China;
2. Artificial Intelligence Research Institute, Hefei Comprehensive National Science Center, Hefei 230088, China;
3. School of Computer, Huainan Normal University, Huainan 232038, China
Keywords:
aspect-based sentiment analysis; knowledge enhancement; sentiment knowledge; syntax knowledge; conceptual knowledge; dependency graph; graph convolution network; interactive attention mechanism
CLC:
TP391
DOI:
10.11992/tis.202308044
Abstract:
In aspect-based sentiment analysis tasks, mining semantic information and syntactic dependency constraints from comment sentences is a key focus in existing research. However, this often underestimates the influence of comprehensive factors, including sentiment knowledge, conceptual knowledge, and syntactic dependency types between words on aspect sentiment orientation judgment. To address this problem, we propose an aspect-based sentiment analysis model based on multilevel knowledge enhancement (MLKE), which uses external knowledge to enhance the knowledge of comment sentences on three levels: sentiment, syntax, and concept. First, sentiment knowledge and the dependency types between words are employed to enhance the dependency graph of sentences. Specific aspect representations containing sentiment and syntactic enhancements are obtained through graph convolution networks that model modular node features. Second, to obtain multilevel knowledge-enhanced aspect representation, the concept graph is used to enhance the conceptual understanding of aspect words, and then the aspect word representation is fused with the specific aspect representation obtained in the previous step. Finally, the coordination and optimization between context representation and aspect representation is achieved using an interactive attention mechanism. The experimental results show that the accuracy and macro-F1 values of the model are improved on five datasets.
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