Abstract 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.
Fund:National Key Research and Development Program of China(No.2019YFC1906002), Research Program of State Language Commission(No.YB135-89)
Corresponding Authors:
DU Yongping, Ph.D., professor. Her research interests include information retrieval, information extraction and natural language processing.
About author:: LIU Yang, master student. His research interests include natural language processing and sentiment analysis. HE Meng, master student. Her research interests include natural language processing and sentiment analysis.
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2021, Vol. 34 
