Sentiment classification for stock news

Web news articles play an important role in stock market. Sentiment classification of news articles can help the investors make investment decisions more efficiently. In this paper, we implemented 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 was introduced into sentiment analysis and Naive Bay es, K-nearest Neighbor and Support Vector Machine were used as classification algorithms. Our method was used for a Chinese stock news data set. The best accuracy reaches 82.9% in all experiments. Additionally, we developed a prototype system to demonstrate our work.