基于主题模型的垃圾邮件过滤系统的设计与实现

doi:10.11959/j.issn.1000-0801.2017313

Abstract

Abstract:

Spam filtering technology plays a key role in many areas including information security,transmission efficiency,and automatic information classification.However,the emergence of spam affects the user's sense of experience,and can cause unnecessary economic and time loss.The deficiency of spam filtering technology was researched,and a method of spam classification based on naive Bayesian was put forward based on multiple keywords.In the subject of mail,the theme model was used by LDA to get the related subject and keyword of the message,and Word2Vec was further used to search keyword synonyms and related words,extending the keyword collection.In the classification of mails,the transcendental probability of the words in the training dataset was obtained by statistical learning.Based on the extended keyword collection and its probability,the joint probability of a subject and a message was deduced by the Bayesian formula as a basis for the spam judgment.At the same time,the spam filtering system based on topic model was simple and easy to apply.By comparing experiments with other typical spam filtering method,it is proved that the method of spam classification based on theme model and the improved method based on Word2Vec can effectively improve the accuracy of spam filtering.

Key words: text classification, spam, topic model, Bayesian theory

CLC Number:

TP393

Xiaohuai KOU,Hua CHENG. Design and implementation of spam filtering system based on topic model[J]. Telecommunications Science, 2017, 33(11): 73-82.

Figures/Tables 13

References 18

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	实际为垃圾邮件	实际为正常邮件
判定为垃圾邮件	A	B
判定为正常邮件	C	D

主题	主题词	词与主题的相关度
主题1	贵公司	0.090
	运输	0.089
	负责人	0.089
	发票	0.086
	财务	0.086
	代	0.085
	有限公司	0.084
	实业	0.084
	开	0.083
	广告	0.083
主题2	帐	0.088
	出任	0.081
	总裁	0.081
	恪守	0.081
	微软公司	0.081
	相违	0.081
	北京时间	0.081
	全文	0.081
	新浪	0.081
	微软	0.081
主题3,4,…,20	…	…

	阈值设定	召回率	正确率	F1值
1	0.40	86%	85%	85%
2	0.44	79%	94%	85%
3	0.45	76%	95%	84%
4	0.42	88%	92%	89%
5	0.44	86%	88%	86%
平均	0.43	83%	90%	86%

方法	召回率	正确率	F1值
Na?ve Bayes^[15]	66%	90%	76%
SVM^[17]	81%	88%	84%
kNN^[18]	73%	80%	76%
MTM^[3]	80%	82%	77%
决策树^[4]	79%	81%	79%
SVM-NB^[5]	85%	87%	82%
本文提出的 LDA方法	83%	90%	86%

主题个数	阈值设定	召回率	正确率	F1值
10	0.32%	80%	80%	80%
15	0.47%	85%	84%	84%
20	0.40%	86%	85%	85%
25	0.47%	90%	88%	88%

Design and implementation of spam filtering system based on topic model

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Figures/Tables 13

References 18

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原主题词	词与主题相关度	对应的扩展主题词	词与主题相关度
帐	0.088
出任	0.081
总裁	0.081	职位	0.081
恪守	0.081
微软公司	0.081
相违	0.081
北京时间	0.081
全文	0.081
新浪	0.081
微软	0.081	科技	0.081

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