Sentiment classification is able to help people automatically analyze customers’ opinions from the large corpus. In this paper, we collect some Chinese movie reviews from Bulletin Board System and aim at making sentiment classification so as to extract several frequent opinion words in some movie elements such as plots, actors/actresses, special effects, and so on. Moreover, we result in a general recommendation grade for users. Focusing on the movie reviews in Chinese, we propose a novel procedure which can extract the pairs of opinion words and feature words according to dependency grammar graphs. This parsing-based approach is more suitable for review articles with plenty of words. The grading results will be presented by a 5-grade scoring system. The experimental results show that the accuracy of our system, with the deviation of grades less than 1, is 70.72%, and the Mean Reciprocal Rank (MRR) value is 0.61. When we change the 5-grade scoring system into producing two values: one for recommendation and the other for non-recommendation, we get precision rates 71.23% and 55.88%, respectively. The result shows an exhilarating performance and indicates that our system can reach satisfied expectancy for movie recommendation.