Aspect-based sentiment analysis can predict the sentiment polarity of specific aspect terms in the text. Compared to general sentiment analysis, it extracts more useful information and analyzes the sentiment more accurately in the comment text. Many previous approaches use long short-term memory networks with attention mechanisms to directly learn aspect-specific representations and model comment text. However, these methods always ignore the importance of the aspect terms position and interactive information between the aspect terms and other words. To address these issues, we propose an improved model based on convolutional neural networks. First, a novel relative position encode layer can integrate the relative position information of specific aspect terms validly in a text. Second, by using the aspect attention mechanism, the semantic relationship between aspect terms and words in the text is fully considered. To verify the effectiveness of the proposed models, we conduct a large number of experiments and comparisons on seven public datasets. The experimental results show that this model outperforms to other state-of-the-art methods.

基于方面的情感分析可以预测文本中特定方面术语的情感极性。与一般的情绪分析相比，它在注释文本中提取了更多有用的信息并更准确地分析了情绪。许多以前的方法使用带有注意力机制的长短期记忆网络来直接学习方面特定的表示形式和模型注释文本。但是，这些方法始终忽略方面术语位置和方面术语与其他词之间的交互信息的重要性。为了解决这些问题，我们提出了一种基于卷积神经网络的改进模型。首先，新颖的相对位置编码层可以将特定方面术语的相对位置信息有效地整合到文本中。其次，通过使用方面注意机制，充分考虑了方面方面和文本中单词之间的语义关系。为了验证所提出模型的有效性，我们对七个公开数据集进行了大量实验和比较。实验结果表明，该模型优于其他最新方法。