Aspect-based sentiment classification (ABSC) is a crucial sub-task of fine-grained sentiment analysis, which aims to predict the sentiment polarity of the given aspects in a sentence as positive, negative, or neutral. Most existing ABSC methods are based on supervised learning. However, these methods rely heavily on fine-grained labeled training data, which can be scarce in low-resource domains, limiting their effectiveness. To overcome this challenge, we propose a low-resource cross-domain aspect-based sentiment classification (CDABSC) approach based on a pre-training and fine-tuning strategy. This approach applies the pre-training and fine-tuning strategy to an advanced deep learning method designed for ABSC, namely the attention-based encoding graph convolutional network (AEGCN) model. Specifically, a high-resource domain is selected as the source domain, and the AEGCN model is pre-trained using a large amount of fine-grained annotated data from the source domain. The optimal parameters of the model are preserved. Subsequently, a low-resource domain is used as the target domain, and the pre-trained model parameters are used as the initial parameters of the target domain model. The target domain is fine-tuned using a small amount of annotated data to adapt the parameters to the target domain model, improving the accuracy of sentiment classification in the low-resource domain. Finally, experimental validation on two domain benchmark datasets, restaurant and laptop, demonstrates significant outperformance of our approach over the baselines in CDABSC Micro-F1.

基于方面的情感分类（ABSC）是细粒度情感分析的一个重要子任务，旨在预测句子中给定方面的情感极性为积极、消极或中性。大多数现有的 ABSC 方法都是基于监督学习。然而，这些方法严重依赖细粒度的标记训练数据，这些数据在资源匮乏的领域可能很稀缺，从而限制了它们的有效性。为了克服这一挑战，我们提出了一种基于预训练和微调策略的低资源跨域基于方面的情感分类（CDABSC）方法。该方法将预训练和微调策略应用于为ABSC设计的高级深度学习方法，即基于注意力的编码图卷积网络（AEGCN）模型。具体来说，选择一个高资源域作为源域，并使用来自源域的大量细粒度注释数据来预训练AEGCN模型。保留模型的最佳参数。随后，将低资源域作为目标域，将预训练的模型参数作为目标域模型的初始参数。使用少量标注数据对目标域进行微调，使参数适应目标域模型，提高低资源域情感分类的准确性。最后，对两个领域基准数据集（餐厅和笔记本电脑）的实验验证表明，我们的方法明显优于 CDABSC Micro-F1 中的基线。