Detection model of effectiveness of Chinese online reviews based on logistic regression

被引：3
|
作者：
Wu, Hanqian [1 ]
Zhu, Yunjie [1 ]
Xie, Jue [2 ]
机构：
[1] School of Computer Science and Engineering, Southeast University, Nanjing
[2] Southeast University-Monash University Joint Graduate School, Suzhou
来源：
Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition) | 2015年 / 45卷 / 03期
关键词：
Association rule; Effectiveness of online review; Logistic regression;
D O I：
10.3969/j.issn.1001-0505.2015.03.004
中图分类号：
学科分类号：
摘要：
In order to realize automated detection of the effectiveness of Chinese online reviews in the context of e-commerce and social networks, a spam detection model based on logistic regression to solve single topic classification problem is proposed. The detection of effectiveness of Chinese online reviews can be regarded as a classification problem. According to the characteristics of Chinese online reviews, nine features are extracted to build the classification model. In order to extract the core feature-topic relevance, an association rule based review term mode is utilized to optimize the topics identification in ICTCLAS(Institute of Computing Technology, Chinese Lexical Analysis System). The cross language model is then used to retrieve relevancy between online review topics. In the experiment, a sample of 1 000 human-labeled reviews is used, and the support vector machine (SVM) classification model is adopted as a comparison. The calculation results of the data mining tool Weka demonstrate that the accuracy rate of the proposed logistic regression classification model based on the optimized review term classification mode is 83.54%, which is 2.10% higher than that of the SVM classification model. ©, 2015, Southeast University. All right reserved.
引用
收藏
页码：433 / 437
页数：4
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