Fine-Grained Domain Adaptation for Aspect Category Level Sentiment Analysis

Mengting Hu, Hang Gao, Yike Wu, Zhong Su, Shiwan Zhao

Abstract: Aspect category level sentiment analysis aims to identify the sentiment polarities towards the aspect categories discussed in a sentence. It usually suffers from a lack of labeled data. A popular solution is to transfer knowledge from a labeled source domain to an unlabeled target domain by unsupervised domain adaptation. However, most domain adaptation methods in sentiment analysis are coarse-grained, considering the source or target domain as a whole during the adaptation. We argue that these single-source single-target methods are inefficient since they ignore the difference between different aspect categories. In this article, we propose a fine-grained domain adaptation method to address the aspect category level sentiment analysis task by considering the adaptation between subdomains. Specifically, the source/target domain is divided into multiple subdomains according to the hierarchical structure of the aspect categories. We then design a multi-source multi-target transfer network to achieve fine-grained transfer. Extensive experimental results demonstrate the effectiveness of our fine-grained domain adaptation method on aspect category level sentiment analysis.