To provide explainable and accurate aspect terms and the corresponding aspect–sentiment detection, it is often useful to take external domain-specific knowledge into consideration. In this work, we propose a knowledge-enabled language representation model BERT for aspect-based sentiment analysis. Specifically, our proposal leverages the additional information from a sentiment knowledge graph by injecting sentiment domain knowledge into the language representation model, which obtains the embedding vectors of entities in the sentiment knowledge graph and words in the text in a consistent vector space. In addition, the model is capable of achieving better performance with a small amount of training data by incorporating external domain knowledge into the language representation model to compensate for the limited training data. As a result, our model is able to provide explainable and detailed results for aspect-based sentiment analysis. Experimental results demonstrate the effectiveness of the proposed method, showing that the knowledge-enabled BERT is an excellent choice for solving aspect-based sentiment analysis problems.

为了提供可解释且准确的方面术语和相应的方面-情感检测，考虑外部特定领域的知识通常很有用。在这项工作中，我们提出了一种基于知识的语言表示模型 BERT，用于基于方面的情感分析。具体来说，我们的提议通过将情感领域知识注入语言表示模型来利用情感知识图中的附加信息，该模型在一致的向量空间中获得情感知识图中实体和文本中单词的嵌入向量。此外，该模型能够通过将外部领域知识纳入语言表示模型以补偿有限的训练数据，从而在少量训练数据下获得更好的性能。因此，我们的模型能够为基于方面的情感分析提供可解释的详细结果。实验结果证明了所提出方法的有效性，表明知识支持的 BERT 是解决基于方面的情感分析问题的绝佳选择。