Abstract
Aspect-level sentiment classification aims to distinguish the sentiment polarities over one or more aspect terms in a sentence. Existing approaches mostly model different aspects in one sentence independently, which ignore the sentiment dependencies between different aspects. However, we find such dependency information between different aspects can bring additional valuable information. In this paper, we propose a novel aspect-level sentiment classification model based on graph convolutional networks (GCN) which can effectively capture the sentiment dependencies between multi-aspects in one sentence. Our model firstly introduces bidirectional attention mechanism with position encoding to model aspect-specific representations between each aspect and its context words, then employs GCN over the attention mechanism to capture the sentiment dependencies between different aspects in one sentence. We evaluate the proposed approach on the SemEval 2014 datasets. Experiments show that our model outperforms the state-of-the-art methods. We also conduct experiments to evaluate the effectiveness of GCN module, which indicates that the dependencies between different aspects is highly helpful in aspect-level sentiment classification.
Abstract (translated)
层面情绪分类旨在区分句子中一个或多个层面术语的情绪极性。现有的方法大多是在一句话中独立地模拟不同的方面，忽略了不同方面之间的情感依赖性。然而，我们发现，不同方面之间的依赖信息可以带来额外的有价值的信息。本文提出了一种新的基于图卷积网络（GCN）的层面情感分类模型，该模型能有效地捕捉一句话中多个层面之间的情感依赖关系。我们的模型首先引入带有位置编码的双向注意机制，对每个方面与其上下文词之间的特定方面表示进行建模，然后在注意机制上使用GCN来捕获一句话中不同方面之间的情感依赖关系。我们评估了Semeval 2014数据集的建议方法。实验表明，我们的模型优于最先进的方法。并对GCN模块的有效性进行了实验评估，结果表明，不同方面之间的依赖性对层面情绪分类有很大的帮助。
URL
https://arxiv.org/abs/1906.04501
