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| 面向股票新闻的情感分类方法 |
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高旸1, 周莉1, 张勇1,2,3, 邢春晓1,2,3, 孙一钢4, 朱先忠4
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1.清华大学 计算机科学与技术系,北京 100084;2.清华大学 信息技术研究院,北京 100084;3.清华大学 清华信息科学与技术国家实验室(筹),北京 100084;4.国家图书馆,北京 100084
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| 摘要: |
| 互联网新闻资讯对证券市场和投资者有举足轻重的影响,新闻进行情感分类后再展示给用户,可以帮助投资者迅速做出投资决定.从文本分类的基本方法出发,实现了基于N-gram 统计模型的新词发现方法,并将所得结果用于构建中文分词词典和情感词典.同时引入评价理论,并用朴素贝叶斯、K 近邻和支持向量机3 种方法进行股票新闻标题的情感分类实验.所用实验数据来自2009 年“新浪财经”共计23 万余条的新闻标题,结果表明二分类的准确率最高可达82.9%.此外,还实现了一个原型系统用于展示股票新闻的分类结果. |
| 关键词: 中文新词发现 N-gram 模型 情感分类 |
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| 基金项目:Supported by the National High-Tech Research and Development Plan of China under Grant No.2009AA01Z143 (国家高技术研究发展计划(863)); the Research Foundation of the National Railway Ministry of China under Grant No.20091111068 (铁道部研究基金) |
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| Sentiment Classification for Stock News |
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GAO Yang1, ZHOU Li1, ZHANG Yong1,2,3, XING Chun-Xiao1,2,3, SUN Yi-Gang4, ZHU Xian-Zhong4
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1.Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;2.Research Institute of Information Technology, Tsinghua University, Beijing 100084, China;3.Tsinghua National Laboratory for Information Science and Technology, Tsing;4.National Library of China, Beijing 100084, China
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| 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. 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. |
| Key words: Chinese new word detection N-gram model sentiment classification |
