The goal of aspect-level sentiment analysis is to identify the sentiment polarity of a specific opinion target expressed; it is a fine-grained sentiment analysis task. Most of the existing works study how to better use the target information to model the sentence without using the interactive information between the sentence and target. In this article, we argue that the prediction of aspect-level sentiment polarity depends on both context and target. First, we propose a new model based on LSTM and the attention mechanism to predict the sentiment of each target in the review, the matrix-interactive attention network (M-IAN) that models target and context, respectively. M-IAN use an attention matrix to learn the interactive attention of context and target and generates the final representations of target and context. Then we introduce two gate networks based on M-IAN to build a deep interactive memory network to capture multiple interactions of target and context. The deep interactive memory network can excellently formulate specific memory for different targets, which is helpful in sentiment analysis. The experimental results of Restaurant and Laptop datasets of SemEval 2014 validate the effectiveness of our model.

中文翻译：

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用于方面级情感分析的深度交互式记忆网络

方面级情感分析的目标是识别表达的特定意见目标的情感极性；这是一个细粒度的情感分析任务。现有的大部分工作都在研究如何在不使用句子和目标之间的交互信息的情况下，更好地利用目标信息对句子进行建模。在本文中，我们认为方面级情感极性的预测取决于上下文和目标。首先，我们提出了一个基于 LSTM 和注意力机制的新模型来预测评论中每个目标的情绪，矩阵交互式注意力网络 (M-IAN) 分别对目标和上下文进行建模。M-IAN 使用注意矩阵来学习上下文和目标的交互注意，并生成目标和上下文的最终表示。然后我们引入两个基于 M-IAN 的门网络来构建一个深度交互记忆网络来捕获目标和上下文的多重交互。深度交互记忆网络可以很好地为不同的目标制定特定的记忆，这有助于情感分析。SemEval 2014 餐厅和笔记本电脑数据集的实验结果验证了我们模型的有效性。