Experts from different domains try to mine users? comments on weblogs for different reasons such as politics or commerce. All these needs necessitate automatically distinguishing subjective weblog contents from objective ones, namely subjectivity categorization. Since weblogs contain various topics from different domains, limited training data can hardly cover all the topics and "unseen words" becomes a serious problem for categorization tasks. In this paper, Part-Of-Speech (POS) based smoothing is proposed to alleviate the "unseen words" problem. In conjunction with a na?ve Bayes model constructed from limited training data, the probability of an unseen word in a new domain can be well smoothed by the probability of its POS result. Empirical studies on five datasets show that our approach consistently outperforms the basic na?ve Bayes with Laplace smoothing. In a cross-domain experiment, our approach achieves 22.0% improvement in Macro F1 and 24.4% in Micro F1 over basic na?ve Bayes. These verify that POS based smoothing can indeed benefit subjectivity categorization, especially in the cases with a large number of unseen words.