This paper researches the newly emerging user reviews (referred here as "light reviews") generated from smart mobile devices. The similarities and differences between this research and the early studies are pointed out. The unique characteristics of the light review can be summarized as having shorter texts, bigger span, and in most cases fewer words per review. The review length and scale also meet the power-law distribution. A series of experiments are studies based on light reviews, resulting in some interesting findings: (1) There is an inverse relationship between classification accuracy and review length; (2) The traditional classical feature selection and feature weight method do not perform well enough on light reviews; (3) The polar word ratio in short reviews, which is the most important feature in sentiment analysis, is higher than in long reviews; (4) There is a higher shared feature term proportion between short review and long review. Based on above studies, the paper puts forward a feature selection method based on short text co-occurrence feature. By combining the information advantages in short reviews with the traditional feature selection methods, the presented method preserves useful information and details as much as possible while removing noise. The results of experiment show that the method is effective and the classification rate is higher.