Blog’s content filtering based on Bayes method and information fingerprint

doi:10.3778/j.issn.1002-8331.2008.24.048

Computer Engineering and Applications ›› 2008, Vol. 44 ›› Issue (24): 159-161.DOI: 10.3778/j.issn.1002-8331.2008.24.048

• 数据库、信号与信息处理 • Previous Articles Next Articles

Blog’s content filtering based on Bayes method and information fingerprint

MA Ru-lin,JIANG Hua,ZHANG Qing-xia

School of Computer and Control，Guilin University of Electronic Technology，Guilin，Guangxi 541004，China

Received:2007-10-26 Revised:2008-01-16 Online:2008-08-21 Published:2008-08-21
Contact: MA Ru-lin

基于贝叶斯方法和信息指纹的博客评论过滤

马如林,蒋华,张庆霞

桂林电子科技大学计算机与控制学院，广西桂林 541004

通讯作者: 马如林

Abstract

Abstract: The appearance of blog enriches and changes the network’s connotation，and influences the ways of information-delivering.Blog criticism，as an exchanging way，has been widely used in blog and thus brings new problems to information warding.This paper on one hand，applies Bayes of text filtering in blog criticism by analysis of blog filtering system in hand；On the other hand，because of the specific features of robot widely existing in blog criticism，this paper recognizes and filters the criticism combining the information fingerprint.Moreover，this paper analyzes and discusses the fingerprint functions that influence blog-filtering’s effect and carrying-out speed.The result of this experiment shows that this blog-filtering is effective，based on Bayes and information fingerprint，and is more advanced than the only Bayes in improving system running efficiency and finding out the phenomenon of advertisement robot.

Key words: blog, Bayes, comments, information fingerprint

摘要： 博客的出现丰富和改变了网络的内涵，影响了人们的信息传递方式，同时博客评论作为一种交互方式在博客中广泛存在，给信息监管带来了新的问题。通过分析现有的博客过滤系统，将广泛应用于文本过滤的贝叶斯方法应用到博客评论中，针对博客评论中广泛存在的广告机器人特点，结合信息指纹对其进行识别和过滤。同时对影响博客评论过滤效果和执行速度的指纹函数进行了分析讨论和实验对比，实验结果表明基于贝叶斯方法和信息指纹相结合的博客评论过滤是行之有效的，而且相对于单独的贝叶斯方法更有利于提高系统运行效率和发现广告机器人现象。

关键词: 博客, 贝叶斯, 评论, 信息指纹

MA Ru-lin,JIANG Hua,ZHANG Qing-xia. Blog’s content filtering based on Bayes method and information fingerprint[J]. Computer Engineering and Applications, 2008, 44(24): 159-161.

马如林,蒋华,张庆霞. 基于贝叶斯方法和信息指纹的博客评论过滤[J]. 计算机工程与应用, 2008, 44(24): 159-161.

[1]	ZHAO Yuanli, LIANG Zhijian. Research on Stance Detection Based on Dual Attention Mechanism of Heteronuclear Convolution [J]. Computer Engineering and Applications, 2021, 57(8): 119-125.
[2]	WU Di, ZHANG Mengtian, SHENG Long, HUANG Zhuyun, GU Mingxing. Microblog Hot Topic Evolution Based on Improved On-Line Biterm Topic Model [J]. Computer Engineering and Applications, 2021, 57(24): 179-184.
[3]	ZHAO Yu, ZHU Yi, YU Qiao, CHEN Xiaoying. Cross Project Defect Prediction Method Based on Hierarchical Data Screening [J]. Computer Engineering and Applications, 2021, 57(20): 279-286.
[4]	LI Jie, LI Miao, YUAN Xiguo. Detection Algorithm?of Pathogenic Microbes from Next-Generation Sequencing Data [J]. Computer Engineering and Applications, 2021, 57(19): 282-289.
[5]	GUO Xuekun, QIN Yuanhui, DU Liang, LUO Yongliang, WANG Hongan. Flight Deck Aviation Support Job Detection Based on Multi-entity Bayesian Networks [J]. Computer Engineering and Applications, 2021, 57(1): 242-253.
[6]	CHEN Haiyang, LIU Xiqing, HUAN Xiaomin. One-step Prediction SVDDBN Missing Data Interpolation Algorithm [J]. Computer Engineering and Applications, 2020, 56(7): 81-87.
[7]	AN Weipeng, CHENG Xiaobo, LIU Yu. Application of Fleiss’ Kappa Coefficient in Bayesian Decision Tree Algorithm [J]. Computer Engineering and Applications, 2020, 56(7): 137-140.
[8]	ZHANG Suisui, HUANG Lixia, WANG Jie, ZHANG Xueying. Localization of Sound Source with Classification of Cross-Correlation Function Within Microphone Array [J]. Computer Engineering and Applications, 2020, 56(4): 128-133.
[9]	LI Donghao, YANG Wenzhong, ZHONG Lijun, ZHANG Zhihao, WANG Xueying. Bursty Event Detection Method Based on Key Microblog [J]. Computer Engineering and Applications, 2020, 56(4): 175-183.
[10]	DU Jing, TENG Jing, MA Bian, ZHANG Aoxin. Breast Cancer Patients Prognostic Analysis Using Bayesian Method [J]. Computer Engineering and Applications, 2020, 56(3): 146-151.
[11]	ZHANG Songhui, XIONG Hanjiang. Point-of-Interest Recommendation Algorithm Based on Poisson Factorization and Neural Network [J]. Computer Engineering and Applications, 2020, 56(21): 176-186.
[12]	HAN Suqing, CHENG Huiwen, WANG Baoli. Novel Naïve Bayesian Incremental Learning Algorithm Based on Three-Way Decisions [J]. Computer Engineering and Applications, 2020, 56(18): 42-49.
[13]	YU Qiancheng, YU Zhiwen, WANG Zhu. Estimating Model Posterior Inference Quality by Using Adversarial Samples to Train Graph Classifier [J]. Computer Engineering and Applications, 2020, 56(17): 142-149.
[14]	DING Na, ZHONG Baojiang. Handwritten Transistor Numerals and Recognition [J]. Computer Engineering and Applications, 2020, 56(16): 97-104.
[15]	DING Qiliang, SHI Zezhi, LI Jianhua. Drug-Target Interaction Prediction Based on Grouped Bayesian Ranking Approach [J]. Computer Engineering and Applications, 2020, 56(15): 185-190.

Blog’s content filtering based on Bayes method and information fingerprint

基于贝叶斯方法和信息指纹的博客评论过滤

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics