基于对抗式分布对齐的跨域方面级情感分析

Cross-Domain Aspect-Level Sentiment Analysis Based on Adversarial Distribution Alignment

跨领域情感分类任务旨在利用富含情感标签的源域数据对缺乏标签的目标域数据进行情感极性分析.由此,文中提出基于对抗式分布对齐的跨域方面级情感分类模型,利用方面词与上下文的交互注意力学习语义关联,基于梯度反转层的领域分类器学习共享的特征表示.利用对抗式训练扩大领域分布的对齐边界,有效缓解模糊特征导致错误分类的问题.在Semeval-2014、Twitter数据集上的实验表明,文中模型性能较优.消融实验进一步表明捕获决策边界的模糊特征并扩大样本与决策边界间距离的策略可提高分类性能.

The source domain data with rich sentiment labels is utilized to classify the aspect-level sentiment polarity for the target domain data without labels.Therefore,a cross-domain aspect-level sentiment classification model based on adversarial distribution alignment is proposed in this paper.The interactive attention of aspect words and context is employed to learn semantic relations,and the shared feature representations are learned by domain classifiers based on gradient reversal layers.The adversarial training is conducted to expand the alignment boundary of the domain distribution.And then the misclassification problem caused by fuzzy features is alleviated effectively.The experimental results on Semeval-2014 and Twitter datasets show that the performance of the proposed model is better than other classic aspect-level sentiment analysis models.The ablation experiment proves that the classification performance can be improved significantly by the strategy of capturing fuzzy features of decision boundary and expanding the distance between sample and decision boundary.