Web news articles play an important role in stock market. Sentiment classification of news articles can help the investors make investment decisions more efficiently. This paper implements an approach of Chinese new words detection by using N-gram model and applied the result for Chinese word segmentation and sentiment classification. Appraisal theory is introduced into sentiment analysis and Na?ve Bayes, K-nearest Neighbor and Support Vector Machine are used as classification algorithms. This method is used for a Chinese stock news data set. The best accuracy reaches 82.9% in all experiments. Additionally, it develops a prototype system to demonstrate this work.