Abstract：This paper proposes a new approach to detect internet deceptive opinion based on Support Vector Machine(SVM). According to the different features in different opinions or sentiments, the authors firstly evaluate the effect or importance of various features. Then a deception detection model is developed with SVM. This model adopts polynomial kernel function and RBF kernel function after optimization to generate the classifier. The results of the experiment show that the proposed method is effective in identifying the deceptive opinion.
刘勘, 朱怀萍, 刘秀芹. 基于支持向量机的网络伪舆情识别研究[J]. 现代图书情报技术, 2013, 29(11): 75-80.
Liu Kan, Zhu Huaiping, Liu Xiuqin. Detection of Internet Deceptive Opinion Based on SVM. New Technology of Library and Information Service, 2013, 29(11): 75-80.
[1] 周亚东,孙钦东,管晓宏,等. 流量内容词语相关度的网络热点话题提取[J]. 西安交通大学学报,2007, 41(10): 1142-1146.(Zhou Yadong,Sun Qindong, Guan Xiaohong,et al. Internet Popular Topics Extraction of Traffic Content Words Correlation[J]. Journal of Xi'an Jiaotong University, 2007, 41(10):1142-1146.) [2] 廖祥文,曹冬林,方滨兴,等. 基于概率推理模型的博客倾向性检索研究[J]. 计算机研究与发展, 2009, 46(9): 1530-1536. (Liao Xiangwen, Cao Donglin, Fang Binxing,et al. Research on Blog Opinion Retrieval Based on Probabilistic Inference Model[J]. Journal of Computer Research and Development, 2009, 46(9): 1530-1536.) [3] 朱恒民,苏新宁,张相斌. 基于链接网络图的互联网舆情话题跟踪方法[J]. 情报学报, 2011,30(12)：1235-1241. (Zhu Hengmin, Su Xinning, Zhang Xiangbin. A Topic Tracking Method of Internet Public Opinion Based on Link Network Diagram[J]. Journal of the China Society for Scientific and Technical Information, 2011, 30(12):1235-1241.) [4] 宋常青. “网络伪舆情”干扰政府决策[J]. 半月谈, 2010(3):6-7. (Song Changqing. Raise Guard on Fake Internet Opinion Interfere with Government Decisions[J]. China Comment, 2010(3):6-7.) [5] 刘勘,朱怀萍,胡航. 网络伪舆情的特征研究[J]. 情报杂志, 2011,30(11):57-60. (Liu Kan, Zhu Huaiping, Hu Hang. Study on Characteristics of Internet Fake Public Opinion[J]. Journal of Intelligence, 2011, 30(11):57-60.) [6] 李兰玉. 网络伪舆情的成因及对策分析[J]. 人民论坛,2013,22(17):42-43. (Li Lanyu. The Reasons and Strategies for Internet Fake Public Opinion[J]. People's Tribune, 2013, 22(17):42-43.) [7] 张一文,齐佳音,方滨兴,等. 非常规突发事件网络舆情指标体系建立初探——概念界定与基本维度[J]. 北京邮电大学学报:社会科学版, 2010, 12(4): 6-14.( Zhang Yiwen, Qi Jiayin, Fang Binxing,et al. Research on the Index System of Public Opinion on Internet for Abnormal Emergency: Concept Definition and Basic Dimensionality [J]. Journal of Beijing University of Posts and Telecommunications: Social Sciences Edition, 2010, 12(4):6-14.) [8] 王青,成颖,巢乃鹏. 网络舆情监测及预警指标体系构建研究[J]. 图书情报工作, 2011,55(8): 54-57.(Wang Qing, Cheng Ying, Chao Naipeng. On the Construction of Internet Public Opinion Index System for Monitoring and Early Warning[J]. Library and Information Service, 2011, 55(8):54-57.) [9] 张玉亮. 基于发生周期的突发事件网络舆情风险评价指标体系[J]. 情报科学, 2012,30(7): 1034-1038.(Zhang Yuliang. Network Opinion Risk Evaluation Index System Based on the Cycle of Emergency[J]. Information Science, 2012, 30(7): 1034-1038.) [10] 陈新杰,呼雨,兰月新. 网络舆情监测指标体系构建研究[J]. 现代情报, 2012, 32(5): 4-7.(Chen Xinjie, Hu Yu, Lan Yuexin. Index Systems of Network Public Sentiment for Monitoring[J]. Modern Information, 2012, 32(5):4-7.) [11] Vapnik V N. The Nature of Statistical Learning Theory [M]. New York, NY, USA: Springer-Verlag, 2000:138-156. [12] 冯维扬. 反竞争情报对策研究:虚假信息与信息可靠性定量分析[J]. 情报学报, 2001, 20(6):728-732.(Feng Weiyang. Study on Strategy Against Counterintelligence: False Information and Quantitative Analysis of Reliability of Information[J]. Journal of the China Society for Scientific and Technical Information, 2001, 20(6):728-732.) [13] Cortes C, Vapnik V. Support-vector Networks [J]. Machine Learning, 1995,20(3):273-297. [14] Amari S, Wu S. Improving Support Vector Machine Classifiers by Modifying Kernel Functions [J]. Neural Networks, 1999, 12(6): 783-789. [15] Lin H T, Lin C J. A Study on Sigmoid Kernels for SVM and the Training of Non-PSD Kernels by SMO-type Methods[EB/OL].[2013-10-05].http://www.csie.ntu.edu.tw/~cjlin/papers/tanh.pdf. [16] Hsu C W, Chang C C, Lin C J. A Practical Guide to Support Vector Classification [EB/OL].[2013-10-05]. http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf. [17] Huang C L, Wang C J. A GA-based Feature Selection and Parameters Optimization for Support Vector Machines [J]. Expert Systems with Applications, 2006, 31(2):231-240. [18] Paquet U, Engelbrecht A P. Training Support Vector Machines with Particle Swarms [C]. In: Proceedings of the IEEE International Joint Conference on Neural Network. New Jersey: IEEE Press, 2003: 1593-1598.
