Aspect-level sentiment classification aims to predict sentiment polarities for different aspect terms within the same sentence or document. However, existing methods rely heavily on modeling the semantic relevance of an aspect term and its context words, and ignore the importance of syntax analysis to a certain extent. Consequently, this may cause the model to pay attention to the context word which is used to describe other aspect terms. In this paper, we propose a model which analyze sentences both syntactically and semantically. At the same time, we propose a simple and effective fusion mechanism to make the integration of aspect information and context information more adequately. We conduct extensive experiments on the SemEval 2014 benchmark datasets, and the results show that our model achieves a new state- of-the-art performance.

方面级别的情感分类旨在预测同一句子或文档中不同方面术语的情感极性。但是，现有方法在很大程度上依赖于对方面术语及其上下文词的语义相关性进行建模，并且在一定程度上忽略了语法分析的重要性。因此，这可能会导致模型注意用于描述其他方面术语的上下文词。在本文中，我们提出了一个在句法和语义上都分析句子的模型。同时，我们提出了一种简单有效的融合机制，以使方面信息和上下文信息的融合更加充分。我们对SemEval 2014基准数据集进行了广泛的实验，结果表明我们的模型实现了新的最新性能。