The paper aims to address an opinion mining problem: to find the helpful reviews from online consumer reviews before mining the detail. This task can benefit both the consumers and the companies. Consumers can read only the helpful opinions from helpful reviews before they purchase a product, while the companies can acquire the true reason why one product is liked or hated. A system is built to assess the difficulty of the problem. The experiment results show that helpful reviews can be identified with high precision from unhelpful ones.