Aspect sentiment classification is an important research topic in natural language processing and computational linguistics, assisting in automatically review analysis and emotional tendency judgement. Different from extant methods that focus on text sequence representations, this paper presents a network framework to learn representation from concurrence-words relation graph (LRCWG), so as to improve the Macro-F1 and accuracy. The LRCWG first employs the multi-head attention mechanism to capture the sentiment representation from the sentences which can learn the importance of text sequence representation. And then, it leverages the priori sentiment dictionary information to construct the concurrence relations of sentiment words with Graph Convolution Network (GCN). This assists in that the learnt context representation can keep both the semantics integrity and the features of sentiment concurrence-words relations. The designed algorithm is experimentally evaluated with all the five benchmark datasets and demonstrated that the proposed aspect sentiment classification can significantly improve the prediction performance of learning task.

中文翻译：

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从 Concurrence-Words 图中学习表示用于方面情感分类

方面情感分类是自然语言处理和计算语言学中的一个重要研究课题，辅助自动评论分析和情感倾向判断。与现有的专注于文本序列表示的方法不同，本文提出了一种网络框架来从并发词关系图（LRCWG）中学习表示，从而提高 Macro-F1 和准确性。LRCWG 首先采用多头注意力机制从句子中捕获情感表示，从而学习文本序列表示的重要性。然后，它利用先验情感词典信息，利用图卷积网络（GCN）构建情感词的并发关系。这有助于学习的上下文表示可以保持语义完整性和情感并发-词关系的特征。设计的算法在所有五个基准数据集上进行了实验评估，证明了所提出的方面情感分类可以显着提高学习任务的预测性能。