Many product review websites have been established (e.g., epinion.com, Rateitall.com) for collecting user reviews for a variety of products. In addition, it has also become a common practice for merchants or product manufacturers to setup online forums that allow their customers to provide reviews or express opinions on products they are interested or have purchased. To facilitate merchants, product manufacturers, and customers in exploiting online product reviews for their marketing, product design, or purchasing decision making, classification of the products reviews into positive and negative categories is essential. In this study, we propose a Semantic-based Sentiment Classification (SSC) technique that constructs from a training set of precategorized product reviews a sentiment classification model on the basis of a collection of positive and negative cue features. Furthermore, the proposed SSC technique includes a semantic expansion mechanism that uses WordNet for expanding the given set of positive and negative cue features. On the basis of three product review corpora, our empirical evaluation results suggest that the proposed SSC technique achieves higher classification effectiveness than the traditional syntactic-level sentiment classification technique does. Moreover, the SSC technique with the use of few seed features (e.g., 10 or 20) can result in comparable classification effectiveness to that attained by the use of a comprehensive list of positive and negative cue features (a total of 4206 words) defined in the General Inquirer.