Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification
This work addresses the problem of sentiment classification across different domains for applications like review analysis, but it is incremental as it builds on existing semi-supervised and domain adaptation techniques.
The paper tackles cross-domain sentiment classification by minimizing the distance between source and target domains in an embedded feature space and using semi-supervised learning with entropy minimization and self-ensemble bootstrapping to refine the classifier with unlabeled target data, achieving substantial improvements over baseline methods.
We consider the cross-domain sentiment classification problem, where a sentiment classifier is to be learned from a source domain and to be generalized to a target domain. Our approach explicitly minimizes the distance between the source and the target instances in an embedded feature space. With the difference between source and target minimized, we then exploit additional information from the target domain by consolidating the idea of semi-supervised learning, for which, we jointly employ two regularizations -- entropy minimization and self-ensemble bootstrapping -- to incorporate the unlabeled target data for classifier refinement. Our experimental results demonstrate that the proposed approach can better leverage unlabeled data from the target domain and achieve substantial improvements over baseline methods in various experimental settings.