Mining Syntactic Relationships via Recursion and Wandering on A Dependency Tree for Aspect-Based Sentiment Analysis
Jintao Zhang, Xiao Na Sun, Yuanlin Li · 2022 International Joint Conference on Neural Networks (IJCNN) · 2022
Aspect-Based Sentiment Analysis aims to determine the emotional orientation of a particular aspect of an online comment. Considering that syntax and even sentences themselves are a specific graph structure, most researchers' recent work mainly focuses on the dependency tree. They construct sentences into tree structures according to dependency parser. However, because a sentence can contain many aspects, it is difficult to correctly associate each aspect with the corresponding important part of the sentence by directly using the original dependency tree. To strengthen this association, a reconstructed dependency tree with aspect as the root is constructed by fine-tuning the original dependency tree for each aspect. We propose a neural network model named ARWAT, which performs on the reconstructed tree to learn informative contextual words and grammatical information effectively. Extensive experiment results demonstrate the superior performance of our proposed model against multiple baselines on five benchmark datasets.