Incorporating Sample Filtering into Subject-Based Ensemble Model for Cross-Domain Sentiment Classification

Recently, cross-domain sentiment classification is becoming popular owing to its potential applications, such as marketing et al. It seeks to generalize a model, which is trained on a source domain and using it to label samples in the target domain. However, the source and target distributions differ substantially in many cases. To address this issue, we propose a comprehensive model, which takes sample filtering and labeling adaptation into account simultaneously, named joint Sample Filtering with Subject-based Ensemble Model (SF-SE). Firstly, a sentence level Latent Dirichlet Allocation (LDA) model, which incorporates topic and sentiment together (SS-LDA) is introduced. Under this model, a high-quality training dataset is constructed in an unsupervised way. Secondly, inspired by the distribution variance of domain-independent and domain-specific features related to the subject of a sentence, we introduce a Subject-based Ensemble model to efficiently improve the classification performance. Experimental results show that the proposed model is effective for cross-domain sentiment classification.