Aspect-level sentiment classification aims to identify the sentiment polarity of a review expressed toward a target. In recent years, neural network-based methods have achieved success in aspect-level sentiment classification, and these methods fall into two types: the first takes the target information into account for context modelling, and the second models the context without considering the target information. It is concluded that the former is better than the latter. However, most of the target-related models just focus on the impact of the target on context modelling, while ignoring the role of context in target modelling. In this study, we introduce an interactive neural network model named LT-T-TR, which divided a review into three parts: the left context with target phrase, the target phrase, and the right context with target phrase. And the interaction between the left/right context and the target phrase is utilized by an attention mechanism to learn the representations of the left/right context and the target phrase separately. As a result, the most important words in the left/right context or in the target phrase are captured, and the results on laptop and restaurant datasets demonstrate that our model outperforms the state-of-the-art methods.

中文翻译：

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方面级别情感分类的目标和上下文交互模型。

方面级别的情感分类旨在识别针对目标表达的评论的情感极性。近年来，基于神经网络的方法在方面级别的情感分类中取得了成功，这些方法分为两种类型：第一种将目标信息考虑到上下文建模中，第二种在不考虑目标信息的情况下对上下文进行建模。结论是前者优于后者。但是，大多数与目标相关的模型只关注目标对上下文建模的影响，而忽略了上下文在目标建模中的作用。在这项研究中，我们介绍了一个名为LT-T-TR的交互式神经网络模型，该模型将评论分为三个部分：带有目标短语的左侧上下文，目标短语和带有目标短语的右侧上下文。并且，注意力机制利用左/右上下文与目标短语之间的交互来分别学习左/右上下文和目标短语的表示。结果，捕获了左/右上下文或目标短语中最重要的单词，笔记本电脑和餐厅数据集上的结果表明，我们的模型优于最新方法。