Enhancing cross-domain sentiment classification through multi-source collaborative training and selective ensemble methods

Abstract

Due to the varying data distributions in different domains, transferring sentiment classification models across domains is often infeasible. Additionally, labeling data in specific domains can be both costly and time-consuming. To address these challenges, multi-source cross-domain sentiment classification leverages knowledge from multiple source domains to aid in sentiment classification in the target domain, utilizing labeled data from these sources. This paper introduces a novel multi-source cross-domain sentiment classification method that leverages collaborative training and selective ensemble classification. By utilizing unlabeled data from the target domain and labeled data from multiple source domains, our method reduces the need for manual labeling and enhances classification accuracy. Empirical evaluations on the Amazon multi-domain review dataset show that our approach achieves an average accuracy of 0.8932 ± 0.012 (0.95 confidence interval), demonstrating significant improvements in robustness and efficiency.