Contrastive transformer based domain adaptation for multi-source cross-domain sentiment classification

Abstract

Cross-domain sentiment classification aims to predict the sentiment tendency in unlabeled target domain data using labeled source-domain data. The wide range of data sources has motivated research into multi-source cross-domain sentiment classification tasks. Conventional domain adaptation methods focus on reducing the domain difference between the source and target domains to realize sentiment migration, which ignores the selection of effective sources and fails to deal with negative transfer, leading to limited performance. To address these problems, we propose a contrastive transformer-based domain adaptation (CTDA) method, which not only develops a multi-source domain selection strategy, but also improves the problem of negative transfer from the perspective of data quality. Specifically, the proposed CTDA includes four stages: (1) designing a mixed selector to weight all related sources or pick out the Top-K sources according to the spatial similarity between both domains, (2) building an adaptor to extract domain-invariant information of features by minimizing the Wasserstein distance between both domains, (3) constructing a discriminator to capture the domain-private information of features by contrastive learning, and (4) performing a weighted classifier to predict the sentiment tendency of the target domain according to multiple trained source classifiers. Extensive experiments were performed on two public benchmarks, and the results demonstrated that our CTDA model significantly outperforms state-of-the-art approaches.