In natural language tasks like text categorization, we usually have an enormous amount of unlabeled data in addition to a small amount of labeled data. We present here a transductive boosting method for text categorization in order to make use of the large amount of unlabeled data efficiently. Our experiments show that the transductive method outperforms conventional boosting techniques that employ only labeled data.