计算机应用 ›› 2014, Vol. 34 ›› Issue (2): 481-485.

• 人工智能 • 上一篇    下一篇

基于语义规则的Web金融文本情感分析

吴江1,唐常杰2,李太勇1,崔亮3   

  1. 1. 西南财经大学 经济信息工程学院,成都 610074
    2. 四川大学 计算机学院,成都 610064;
    3. 西南财经大学 统计学院,成都 610074
  • 收稿日期:2013-08-20 修回日期:2013-10-23 出版日期:2014-02-01 发布日期:2014-03-01
  • 通讯作者: 吴江
  • 作者简介:吴江(1980-),男,浙江衢州人,副教授,博士,主要研究方向:数据库、知识工程; 唐常杰(1946-),男,重庆人,教授,博士生导师,主要研究方向:数据库、知识工程;李太勇(1979-),男,四川安岳人,副教授,博士,主要研究方向:数据挖掘; 崔亮(1981-),男,山东潍坊人,博士,主要研究方向:金融统计。
  • 基金资助:
    教育部人文社会科学研究青年基金资助项目;贵州省自然科学基金资助项目;贵州省自然科学基金资助项目

Sentiment analysis on Web financial text based on semantic rules

WU Jiang1,TANG Chang-jie2,LI Taiyong1,CUI Liang3   

  1. 1. School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu Sichuan 610074, China;
    2. College of Computer Science, Sichuan University, Chengdu Sichuan 610064, China;
    3. School of Statistics, Southwestern University of Finance and Economics, Chengdu Sichuan 610074, China
  • Received:2013-08-20 Revised:2013-10-23 Online:2014-02-01 Published:2014-03-01
  • Contact: WU Jiang

摘要: 为有效提高非结构化Web金融文本情感倾向和强度分析的精度,提出了基于语义规则的Web金融文本情感分析算法(SAFT-SR)。该算法基于Apriori算法对金融文本进行属性抽取,构建金融情感词典和语义规则识别情感单元及强度,进而得到文本的情感倾向和强度。实验结果表明,与Ku提出的算法相比,在情感倾向分类方面,算法SAFT-SR情感分类性能良好,提高了分类器的F值、查全率和查准率;在情感强度计算方面,算法SAFT-SR的误差更小,更接近真实评分,证明了SAFT-SR是一种有效的金融文本情感分析算法。

关键词: Web金融文本, 情感词典, 语义规则, 情感分析, 情感倾向

Abstract: In order to effectively improve the accuracy of sentiment orientation and intensity analysis of unstructured Web financial text, a sentiment analysis algorithm for Web financial text based on semantic rule (SAFT-SR) was proposed. The algorithm extracted features of financial text based on Apriori, constructed financial sentiment lexicon and semantic rules to recognize sentiment unit and intensity, and figured out the sentiment orientation and intensity of text. Experiment results demonstrate that SAFT-SR is a promising algorithm for sentiment analysis on financial text. Compared with Ku’s algorithm, in sentiment orientation classification, SAFT-SR has better classification performance and increases F-measure, recall and precision; in sentiment intensity analysis, SAFT-SR reduces error and is closer to expert mark.

Key words: Web financial text, sentiment lexicon, semantic rule, sentiment analysis, sentiment orientation

中图分类号: