Title :
Sentiment classification for stock news
Author :
Gao, Yang ; Zhou, Li ; Zhang, Yong ; Xing, Chunxiao ; Sun, Yigang ; Zhu, Xianzhong
Author_Institution :
Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China
Abstract :
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 Bayes, 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.
Keywords :
Bayes methods; classification; natural language processing; stock markets; support vector machines; text analysis; Chinese new word detection; Chinese word segmentation; N-gram model; Web news article; appraisal theory; k-nearest neighbor; naive Bayes method; sentiment classification; stock market; stock news; support vector machine; Chinese new word detection; N-gram model; Sentiment classification;
Conference_Titel :
Pervasive Computing and Applications (ICPCA), 2010 5th International Conference on
Conference_Location :
Maribor
Print_ISBN :
978-1-4244-9144-5
DOI :
10.1109/ICPCA.2010.5704082
