基于KNN和Bayes算法的组合分类器的垃圾评论识别研究

产品垃圾评论在一定程度上影响了评论信息的参考价值,本文旨在建立识别模型将垃圾评论从评论文本中剔除,保留真实的产品评论。首先,分析了产品评论的特点,从数据搜集、文本预处理、互信息检验、文本表示4个模块提取了14个特征。然后,利用高互补性建立了基于KNN和Bayes算法的组合分类器模型。最后,利用交叉验证对iPhone 6Plus的产品评论进行检验,得到评价指标分别为:正确识别率75.3%、召回率82.1%以及F1值77.5%.

Research on Identifying Product Review Spam Based on Combination Classification of KNN and Bayesian Algorithms

Product review spam affects the reference value of information to a certain extent.The purpose of this paper was to set up a model to remove the product review spam,and retained the real product reviews.Firstly,this paper analyzed the characteristics of the product reviews,and abstracted 14 features from Data collecting,text preprocessing,mutual informa-tion inspecting,and text representing.Secondly,we established a model of combination classifications based on KNN and Bayes algorithm by using the biggest complementarity.Finally,we made cross validating to the product review for iPhone 6 Plus.This model gets a higher correct recognition rate of 75.3%,the recall rate of 82.1%,and F1 value 77.5%.