Aspect-level sentiment analysis aims at identifying the sentiment polarity of specific aspect words in a given sentence. Existing studies mostly use recurrent neural network (RNN) -based models. However, truncated backpropagation, gradient vanishing, and exploration problems often occur during the training process. To address these issues, this paper proposed a novel network with multiple attention mechanisms for aspect-level sentiment analysis. First, we apply the bidirectional encoder representations from transformers (BERT) model to construct word embedding vectors. Second, multiple attention mechanisms, including intra- and inter-level attention mechanisms, are used to generate hidden state representations of a sentence. In the intra-level attention mechanism, multi-head self-attention and point-wise feed-forward structures are designed. In the inter-level attention mechanism, global attention is used to capture the interactive information between context and aspect words. Furthermore, a feature focus attention mechanism is proposed to enhance sentiment identification. Finally, several classic aspect-level sentiment analysis datasets are used to evaluate the performance of our model. Experiments demonstrate that the proposed model can achieve state-of-the-art results compared to baseline models.

方面级情感分析旨在识别给定句子中特定方面词的情感极性。现有研究大多使用基于循环神经网络 (RNN) 的模型。然而，在训练过程中经常会出现截断的反向传播、梯度消失和探索问题。为了解决这些问题，本文提出了一种具有多种注意力机制的新型网络，用于方面级情感分析。首先，我们应用来自 Transformers (BERT) 模型的双向编码器表示来构建词嵌入向量。其次，使用多种注意机制，包括层内和层间注意机制，来生成句子的隐藏状态表示。在级内注意力机制中，设计了多头自注意力和逐点前馈结构。在层间注意力机制中，使用全局注意力来捕获上下文和方面词之间的交互信息。此外，提出了一种特征焦点注意机制来增强情感识别。最后，使用几个经典的方面级情感分析数据集来评估我们模型的性能。实验表明，与基线模型相比，所提出的模型可以实现最先进的结果。