Aspect-category sentiment analysis (ACSA) aims to identify all the aspect categories mentioned in the text and their corresponding sentiment polarities. Some joint models have been proposed to address this task. However, these joint models do not solve the following two problems well: mismatching between the aspect categories and the sentiment words, and data deficiency of some aspect categories. To solve them, we propose a novel joint model which contains a contextualized aspect embedding layer and a shared sentiment prediction layer. The contextualized aspect embedding layer extracts the aspect category related information, which is used to generate aspect-specific representations for sentiment classification like traditional context-independent aspect embedding (CIAE) and is therefore called contextualized aspect embedding (CAE). The CAE can mitigate the mismatching problem because it is semantically more related to sentiment words than CIAE. The shared sentiment prediction layer transfers sentiment knowledge between aspect categories and alleviates the problem caused by data deficiency. Experiments conducted on SemEval 2016 Datasets show that our proposed model achieves state-of-the-art performance.

翻译：Aspect 类情感分析(ACSA)旨在确定文本中提到的所有方面类别及其相应的情感极点。一些联合模型已经提出来应对这一任务。但是,这些联合模型并没有很好地解决以下两个问题:方面类别和情绪单词之间的不匹配,以及某些方面类别的数据缺陷。为了解决这些问题,我们提议了一个新颖的联合模型,其中包含一个背景化的嵌入层和共同情感预测层。嵌入层的上下文化层面提取了与方面相关的信息,这些信息用于生成与情绪分类有关的方方面面表达,如传统背景独立嵌入(CIAE),因此被称为背景化嵌入(CAE)。CAE可以缓解不匹配的问题,因为它与情绪单词之间具有内在的关联性比 CSAE更密切的关系。共享的感知层在各方面类别之间传递情感知识,缓解数据缺陷造成的问题。在SemEval 2016数据集上进行的实验显示,我们提议的模型达到了最新业绩。