In this paper, we focus on the tasks of cross-domain sentiment classification. We find across different domains, features with some types of part-of-speech (POS) tags are domain-dependent, while some others are domain-free. Based on this finding, we proposed a POS-based ensemble model to efficiently integrate features with different types of POS tags to improve the classification performance. Weights are trained by stochastic gradient descent (SGD) to optimize the perceptron and minimal classification error (MCE) criteria. Experimental results show that the proposed ensemble model is quite effective for the task of cross-domain sentiment classification.
REF proposed a POSbased ensemble model for cross-domain sentiment classification.
A POS-based Ensemble Model for Cross-domain Sentiment Classification
IJCNLP
Computer Science