Domain attention model for multi-domain sentiment classification

Abstract

Sentiment classification is widely known as a domain-dependent problem. In order to learn an accurate domain-specific sentiment classifier, a large number of labeled samples are needed, which are expensive and time-consuming to annotate. Multi-domain sentiment analysis based on multi-task learning can leverage labeled samples in each single domain, which can alleviate the need for large amount of labeled data in all domains. In this paper, we propose a domain attention model for multi-domain sentiment analysis. In our approach, the domain representation is used as attention to select the most domain-related features in each domain. The domain representation is obtained through an auxiliary domain classification task, which works as domain regularizer. In this way, both shared and domain-specific features for sentiment classification are extracted simultaneously. In contrast with existing multi-domain sentiment classification methods, our approach can extract the most discriminative features from a shared hidden layer in a more compact way. Experimental results on two multi-domain sentiment datasets validate the effectiveness of our approach.