Sentiment classification has received increasing attention in recent years. Supervised learning methods for sentiment classification require considerable amount of labeled data for training purposes. As the number of domains increases, the task of collecting data becomes impractical. Therefore, domain adaptation techniques are employed. However, most of the studies dealing with the domain adaptation problem demand a few amount of labeled data or lots of unlabeled data belonging to the target domain, which may not be always possible. In this work, a novel method for sentiment classification, which does not require labeled and/or unlabeled data from the target domain, is proposed. The propose method mainly consists of two stages. At first, the target domain is predicted even if it is not among the source domains in hand. Then, sentiment is classified as either positive or negative using the sentiment classifier specifically trained for the predicted domain. Extensive experimental analysis on two different datasets with distinct languages and domains verifies that the proposed method is superior to the domain independent sentiment classification approach at each case considered.