Fusion Network for Aspect-Level Sentiment Classification Based on Graph Neural Networks—Enhanced Syntactics and Semantics
-
Published:2024-08-26
Issue:17
Volume:14
Page:7524
-
ISSN:2076-3417
-
Container-title:Applied Sciences
-
language:en
-
Short-container-title:Applied Sciences
Author:
Li Miaomiao1ORCID, Lei Yuxia1, Zhou Weiqiang1ORCID
Affiliation:
1. School of Computer Science, Qufu Normal University, Rizhao 276800, China
Abstract
Aspect-level sentiment classification (ALSC) struggles with correctly trapping the aspects and corresponding sentiment polarity of a statement. Recently, several works have combined the syntactic structure and semantic information of sentences for more efficient analysis. The combination of sentence knowledge with graph neural networks has also proven effective at ALSC. However, there are still limitations on how to effectively fuse syntactic structure and semantic information when dealing with complex sentence structures and informal expressions. To deal with these problems, we propose an ALSC fusion network that combines graph neural networks with a simultaneous consideration of syntactic structure and semantic information. Specifically, our model is composed of a syntactic attention module and a semantic enhancement module. First, the syntactic attention module builds a dependency parse tree with the aspect term being the root, so that the model focuses better on the words closely related to the aspect terms, and captures the syntactic structure through a graph attention network. In addition, the semantic enhancement module generates the adjacency matrix through self-attention, which is processed by the graph convolutional network to obtain the semantic details. Lastly, the extracted features are merged to achieve sentiment classification. As verified by experiments, the model we propose can effectively enhance ALSC’s behavior.
Funder
Shandong Province Teaching Reform Project
Reference45 articles.
1. Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., and Manandhar, S. (2014, January 23–24). Semeval-2014 task 4: Aspect based sentiment analysis. Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), Dublin, Ireland. 2. Tan, K.L., Lee, C.P., and Lim, K.M. (2023). A survey of sentiment analysis: Approaches, datasets, and future research. Appl. Sci., 13. 3. A survey on aspect-based sentiment analysis: Tasks, methods, and challenges;Zhang;IEEE Trans. Knowl. Data Eng.,2023 4. Socher, R., Huval, B., Manning, C.D., and Ng, A.Y. (2012, January 12–14). Semantic compositionality through recursive matrix-vector spaces. Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Jeju Island, Republic of Korea. 5. Huang, Z., Liu, H., Zhu, J., and Min, J. (2023). Customer sentiment recognition in conversation based on contextual semantic and affective interaction information. Appl. Sci., 13.
|
|