Aspect-based sentiment classification is a crucial problem in fine-grained sentiment analysis, which aims to predict the sentiment polarity of the given aspect according to its context. Previous works have made remarkable progress in leveraging attention mechanism to extract opinion words for different aspects. However, a persistent challenge is the effective management of semantic mismatches, which stem from attention mechanisms that fall short in adequately aligning opinions words with their corresponding aspect in multi-aspect sentences. To address this issue, we propose a novel Aspect-oriented Opinion Alignment Network (AOAN) to capture the contextual association between opinion words and the corresponding aspect. Specifically, we first introduce a neighboring span enhanced module which highlights various compositions of neighboring words and given aspects. In addition, we design a multi-perspective attention mechanism that align relevant opinion information with respect to the given aspect. Extensive experiments on three benchmark datasets demonstrate that our model achieves state-of-the-art results. The source code is available at https://github.com/AONE-NLP/ABSA-AOAN.

翻译：基于方面的情感分类是细粒度情感分析中的关键问题，旨在根据上下文预测给定方面的情感极性。以往研究在利用注意力机制提取不同方面的观点词方面取得了显著进展。然而，一个持续的挑战是有效管理语义不匹配问题，这些问题源于注意力机制在多方面句子中未能充分地将观点词与其对应方面进行对齐。为解决此问题，我们提出了一种新颖的面向方面的情感对齐网络（AOAN），以捕捉观点词与对应方面之间的上下文关联。具体而言，我们首先引入了一个相邻跨度增强模块，该模块突出显示了相邻词语与给定方面的多种组合。此外，我们设计了一种多视角注意力机制，将相关观点信息与给定方面进行对齐。在三个基准数据集上的大量实验表明，我们的模型达到了最先进的结果。源代码可在 https://github.com/AONE-NLP/ABSA-AOAN 获取。