Adding Prior Knowledge in Hierarchical Attention Neural Network for Cross Domain Sentiment Classification

Abstract

Domain adaptation tasks have raised much attention in recent years, especially, the task of cross-domain sentiment classification (CDSC). Due to the domain discrepancy, a sentiment classifier trained in a source domain often performs less well, when directly applied to a target domain. Adversarial neural networks have been used in mainstream approaches for learning domain independent features, such as pivots, which are words with the same sentiment polarity in different domains. However, domain specific features can often determine sentiment in its context. In this paper, we propose a hierarchical attention network with prior knowledge information (HANP) for the CDSC task. Unlike other existing methods, the HANP can obtain both domain independent and domain specific features at the same time by adding prior knowledge. In addition, the HANP also includes a hierarchical representation layer with attention mechanism, so that the HANP can capture important words and sentences in relation to sentiment. Moreover, the proposed model can offer a direct visualization of the sentimental prior knowledge. The experiments on the Amazon review datasets demonstrate that the proposed HANP can significantly outperform the state-of-the-art methods.