Aspect-based sentiment analysis has received considerable attention in recent years because it can provide more detailed and specific user opinion information. Most existing methods based on recurrent neural networks usually suffer from two drawbacks: information loss for long sequences and a high time consumption. To address such issues, a hybrid attention model is proposed for aspect-based sentiment analysis in this paper, which utilizes only attention mechanisms rather than recurrent or convolutional structures. In this model, a self-attention mechanism and an aspect-attention mechanism are designed for generating the semantic representation at the word and sentence levels respectively. Two auxiliary features of word location and part-of-speech are also explored for the proposed models to enhance the semantic representation of sentences. A series of experiments are conducted on three benchmark datasets for aspect-based sentiment analysis. Experimental results demonstrate the advantage of the proposed models for both efficiency and execution effectiveness.

基于方面的情感分析近年来受到了相当多的关注，因为它可以提供更详细、更具体的用户意见信息。大多数现有的基于循环神经网络的方法通常存在两个缺点：长序列的信息丢失和高时间消耗。为了解决这些问题，本文提出了一种基于方面的情感分析的混合注意力模型，该模型仅利用注意力机制而不是循环或卷积结构。在该模型中，设计了自我注意机制和方面注意机制，分别在单词和句子级别生成语义表示。还为所提出的模型探索了单词位置和词性的两个辅助特征，以增强句子的语义表示。在三个基准数据集上进行了一系列实验，用于基于方面的情感分析。实验结果证明了所提出的模型在效率和执行有效性方面的优势。