Syntactic Graph Attention Network for Aspect-Level Sentiment Analysis

Li, Yuan; Wang, Jin; Yu, Liang-Chih; Zhang, Xuejie

doi:10.1109/tai.2022.3227535

Cited by 8 publications

(1 citation statement)

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“…When a sentence contains multiple evaluation entities whose emotional expression is not the same, one cannot accurately judge their emotional tendency. Aspectbased sentiment analysis, ABSA, is a fine-grained technique for identifying sentiment polarity for each aspect term based on subsequent research [2].…”

Section: Introductionmentioning

confidence: 99%

Multi-task Dual-Graph Network framework for Aspect Sentiment Triplet Extraction

Wang,

Zheng,

Tang

et al. 2024

Preprint

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Aspect Sentiment Triplet Extraction is a task that extracts aspects, opinion words corresponding to aspects and sentiment polarity from review sentences. Although aspect emotion triple extraction technology has achieved remarkable results in recent years, it still has a few drawbacks. Many existing research methods ignore important issues, such as the dependency relationships between emotional triad elements and the detection of aspect and viewpoint boundary terms at the phrase-level. Due to the above problems, this paper proposes a multi-task joint learning architecture based on dual graph networks (DGNMT). Firstly, a shared sentence encoder module is proposed in the model. The middle-layer and top-level feature representations of the BERT encoder are applied to two different subtasks in this module. Secondly, Dual-Graph Network Learning module has been designed. As part of the dual-graph network learning layer, this module introduces a graph attention network based on phrase structure trees in the joint extraction task branch. For the triple extraction task branch, a graph convolution network is used based on the modified syntactic dependency graph in order to determine the syntactic relationships between triple elements. Additionally , the Multi-Task Tagging module is proposed to extract labels for tasks as well as triplet extraction tasks. And then, predictions are made based on 1D sequence tags and 2D word pair matrices, and the tag information of the two tasks is transformed into each other through a conversion strategy. According to the final experimental results, the model outperforms a state-of-the-art baseline model on four commonly used evaluation indicators.

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Section: Introductionmentioning

confidence: 99%