摘要
给出一种基于累积反馈学习的简单贝叶斯舆情信息分类方法;引入领域专家经验设置普通关键词,通过提供学习样本,运用简单贝叶斯方法,对样本加入领域规则进行累积反馈学习.实验结果表明累积反馈学习对提高舆情信息挖掘和分类的准确率是必要的.
One kind is given for public opinion information classification technique which is based on Naive Bayesian accumulative feedback learning. Introduction of expert experience common keywords is set, through the provision of learning samples, using simple Bayesian method, and the rules for cumulative feedback learning samples are added. The experimental results show that the cumulative feedback learning to improve the public opinion information mining and classification accuracy is necessary.
出处
《嘉应学院学报》
2014年第5期18-22,共5页
Journal of Jiaying University
基金
广东省教育科学规划课题(2010tjk180)
关键词
简单贝叶斯
累积反馈学习
舆情信息分类
文本挖掘
Naive Bayesian
accumulative feedback learning
public opinion information classification
text mining
