This paper describes the sentiment classification with review extraction. Whole process can be illustrated logically as: (1) extract the review expressions on specific subjects and attach sentiment tag and weight to each expression; (2) calculate the sentiment indicator of each tag by accumulating the weights of all the expression with the corresponding tag; (3) given the indicators on different tags, use a classifier to predict the sentiment label of the text. A system Approximate Text Analysis (ATA) is used for review extraction in stage 1. It follows the idea of Super Parsing, which enables non-adjacent constituents to be merged to deduce a new one. To traverse the valid constituent combinations in Super Parsing, an algorithm named Candidate List Algorithm (CLA) is proposed. Then the performance of three kinds of classifiers (a simple linear classifier, SVM and decision tree) in stage 3 is studied. The experiments on on-line documents show that the SVM algorithm achieves the best performance.