Active Learning for Cross-domain Sentiment Classification

In the literature, various approaches have been proposedto address the domain adaptation problem in sentiment classification (also called cross-domainsentiment classification). However, the adaptation performance normally much suffers when the data distributionsin the source and target domains differ significantly. In this paper, we suggest to perform activelearning for cross-domain sentiment classification by actively selecting a smallamount of labeled data in the target domain. Accordingly, we propose an novel activelearning approach for cross-domain sentiment classification. First, we traintwo individual classifiers, i.e., the source and target classifiers with thelabeled data from the source and target respectively. Then, the two classifiersare employed to select informative samples with the selection strategy of QueryBy Committee (QBC). Third, the two classifier is combined to make theclassification decision. Importantly, the two classifiers are trained by fullyexploiting the unlabeled data in the target domain with the label propagation(LP) algorithm. Empirical studies demonstrate the effectiveness of our active learning approach for cross-domainsentiment classification over some strong baselines.