ABSTRACT Sentiment analysis is an important but challenging task. Remarkable success has been achieved on domains where sufficient labeled training data is available. Nevertheless, annotating sufficient data is labor-intensive and time-consuming, establishing significant barriers for adapting the sentiment classification systems to new domains. In this paper, we introduce a Capsule network for sentiment analysis in domain adaptation scenario with semantic rules (CapsuleDAR). CapsuleDAR exploits capsule network to encode the intrinsic spatial part-whole relationship constituting domain invariant knowledge that bridges the knowledge gap between the source and target domains. Furthermore, we also propose a rule network to incorporate the semantic rules into the capsule network to enhance the comprehensive sentence representation learning. Extensive experiments are conducted to evaluate the effectiveness of the proposed CapsuleDAR model on a real world data set of four domains. Experimental results demonstrate that CapsuleDAR achieves substantially better performance than the strong competitors for the cross-domain sentiment classification task. INDEX TERMS Cross-domain sentiment classification, capsule network, semantic rules, deep learning.
Zhang et al. REF introduced a Capsule network for sentiment analysis in domain adaptation scenario with semantic rules, which can enhance the comprehensive sentence representation learning.
Cross-Domain Sentiment Classification by Capsule Network With Semantic Rules
IEEE Access
IEEE Access
Computer Science