Cross-Domain Discriminative Subspace Classification Algorithm for Review Text Sentiment Recognition Oriented E-Commerce Platforms

Online reviews on E-commerce platforms contain users’ opinions and emotions about the purchased products. How to enable machines to understand the semantics of review texts and judge their emotional tendencies has become a hot topic of common concern in academia and industry. However, review texts often come from various domains in practical applications, thus there are differences in the description and emotional vocabulary. In this case, traditional classification models trained on review texts from a single domain often have poor classification results. This paper proposes a cross-domain discriminative subspace classification (CDDSC) algorithm based on domain adaptation and subspace learning. First, we adopt the progressive data projection strategy from different domains sequentially into the low-rank subspace and label space, which can reduce the interference of useless information on classification task while mining the global structure of data in each domain. Second, to reduce domain differences, we adopt the dual metric techniques, i.e., maximum mean discrepancy (MMD) based on marginal distribution and MMD based on conditional distribution to approximate the inter-domain overall probability distributions and the inter-domain conditional probability distributions, respectively. Thirdly, we use the slack label and low-rank constraint technique to promote inter-class interval and intra-class tightness. We conduct experiments with several review text corpuses oriented Chinese E-commerce platforms. Experiments results show that the CDDSC algorithm obtains good sentiment classification accuracy on Chinese E-commerce platforms.