Online reviews regarding purchasing services or products offered are the main source of users’opinions.To gain fame or profit,generally,spam reviews are written to demote or promote certain targeted products or servi...Online reviews regarding purchasing services or products offered are the main source of users’opinions.To gain fame or profit,generally,spam reviews are written to demote or promote certain targeted products or services.This practice is called review spamming.During the last few years,various techniques have been recommended to solve the problem of spam reviews.Previous spam detection study focuses on English reviews,with a lesser interest in other languages.Spam review detection in Arabic online sources is an innovative topic despite the vast amount of data produced.Thus,this study develops an Automated Spam Review Detection using optimal Stacked Gated Recurrent Unit(SRD-OSGRU)on Arabic Opinion Text.The presented SRD-OSGRU model mainly intends to classify Arabic reviews into two classes:spam and truthful.Initially,the presented SRD-OSGRU model follows different levels of data preprocessing to convert the actual review data into a compatible format.Next,unigram and bigram feature extractors are utilized.The SGRU model is employed in this study to identify and classify Arabic spam reviews.Since the trial-and-error adjustment of hyperparameters is a tedious process,a white shark optimizer(WSO)is utilized,boosting the detection efficiency of the SGRU model.The experimental validation of the SRD-OSGRU model is assessed under two datasets,namely DOSC dataset.An extensive comparison study pointed out the enhanced performance of the SRD-OSGRU model over other recent approaches.展开更多
In this study, methods to classify advertising reviews from shopping mall reviews are suggested. Advertising reviews are mostly written by companies and contain advertising contents. There are a few studies regarding ...In this study, methods to classify advertising reviews from shopping mall reviews are suggested. Advertising reviews are mostly written by companies and contain advertising contents. There are a few studies regarding the classification of opinion spam documents, which is very rare in foreign studies; however, there are no studies that classify advertising reviews from Korean reviews. In this study, the Naive Bayes Classifier was used to classify review documents and the POS （Part-of-Speech）-Tagging and bigram methods were used to extract specific words. The frequency calculation methods for the probability value of specific words were： （1） The general number of appearances of words （2） the frequency calculation of specific words through the suggested Latent Semantic Analysis （LSA）, and by recalculating the result from （1） in （2）, the performances of each method were compared. As a result, the methods from （2） showed 88.43% accuracy which is 8.89% higher than 79.54% which was the previous result from using the POS-Tagging ＋ Bigram method. Therefore, it was proved that the method suggested in this study is effective at classifying or extracting advertising reviews from Korean product review documents.展开更多
在Web2.0技术日益发达的今天,人们越来越多地依赖网络评论做出交易决策,由此,虚假评论的识别已经变为一种迫切的需求。本文通过分析研究虚假评论与真实评论的区别与联系,提出了基于条件随机场的虚假评论识别算法。该算法不同于已有的基...在Web2.0技术日益发达的今天,人们越来越多地依赖网络评论做出交易决策,由此,虚假评论的识别已经变为一种迫切的需求。本文通过分析研究虚假评论与真实评论的区别与联系,提出了基于条件随机场的虚假评论识别算法。该算法不同于已有的基于已有的真假评论集和语料库的虚假评论识别方法,而是通过对评论文本进行特征序列标注,再利用CRF(Conditional Random Fields, CRF)训练识别模型对虚假评论进行识别。实验结果表明,本文所提的算法在虚假评论识别效果上有着不错的表现。展开更多
着重梳理当前产品垃圾评论识别的国内外研究,总结研究特点与不足,发掘发展趋势。在中国知网、Web of Science上以'虚假评论''review spam'等为关键词检索并筛选得到54篇国内外相关文献,采用文献分析法对其进行分类分析...着重梳理当前产品垃圾评论识别的国内外研究,总结研究特点与不足,发掘发展趋势。在中国知网、Web of Science上以'虚假评论''review spam'等为关键词检索并筛选得到54篇国内外相关文献,采用文献分析法对其进行分类分析,重点阐述研究在识别特征和识别方法方面的优化创新,以及针对垃圾评论、垃圾评论发布者、发布群体等不同识别对象的方法差异。研究发现,当前垃圾评论识别的相关成果可以分为基于评论内容的方法和基于评论结构、评论者、被评论产品的方法,在未来的垃圾评论识别中,应根据数据集的特点,提取有效识别特征,选择优化识别方法。展开更多
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R263)PrincessNourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4310373DSR58The authors are thankful to the Deanship of ScientificResearch atNajranUniversity for funding thiswork under theResearch Groups Funding program grant code(NU/RG/SERC/11/7).
文摘Online reviews regarding purchasing services or products offered are the main source of users’opinions.To gain fame or profit,generally,spam reviews are written to demote or promote certain targeted products or services.This practice is called review spamming.During the last few years,various techniques have been recommended to solve the problem of spam reviews.Previous spam detection study focuses on English reviews,with a lesser interest in other languages.Spam review detection in Arabic online sources is an innovative topic despite the vast amount of data produced.Thus,this study develops an Automated Spam Review Detection using optimal Stacked Gated Recurrent Unit(SRD-OSGRU)on Arabic Opinion Text.The presented SRD-OSGRU model mainly intends to classify Arabic reviews into two classes:spam and truthful.Initially,the presented SRD-OSGRU model follows different levels of data preprocessing to convert the actual review data into a compatible format.Next,unigram and bigram feature extractors are utilized.The SGRU model is employed in this study to identify and classify Arabic spam reviews.Since the trial-and-error adjustment of hyperparameters is a tedious process,a white shark optimizer(WSO)is utilized,boosting the detection efficiency of the SGRU model.The experimental validation of the SRD-OSGRU model is assessed under two datasets,namely DOSC dataset.An extensive comparison study pointed out the enhanced performance of the SRD-OSGRU model over other recent approaches.
文摘In this study, methods to classify advertising reviews from shopping mall reviews are suggested. Advertising reviews are mostly written by companies and contain advertising contents. There are a few studies regarding the classification of opinion spam documents, which is very rare in foreign studies; however, there are no studies that classify advertising reviews from Korean reviews. In this study, the Naive Bayes Classifier was used to classify review documents and the POS （Part-of-Speech）-Tagging and bigram methods were used to extract specific words. The frequency calculation methods for the probability value of specific words were： （1） The general number of appearances of words （2） the frequency calculation of specific words through the suggested Latent Semantic Analysis （LSA）, and by recalculating the result from （1） in （2）, the performances of each method were compared. As a result, the methods from （2） showed 88.43% accuracy which is 8.89% higher than 79.54% which was the previous result from using the POS-Tagging ＋ Bigram method. Therefore, it was proved that the method suggested in this study is effective at classifying or extracting advertising reviews from Korean product review documents.
文摘在Web2.0技术日益发达的今天,人们越来越多地依赖网络评论做出交易决策,由此,虚假评论的识别已经变为一种迫切的需求。本文通过分析研究虚假评论与真实评论的区别与联系,提出了基于条件随机场的虚假评论识别算法。该算法不同于已有的基于已有的真假评论集和语料库的虚假评论识别方法,而是通过对评论文本进行特征序列标注,再利用CRF(Conditional Random Fields, CRF)训练识别模型对虚假评论进行识别。实验结果表明,本文所提的算法在虚假评论识别效果上有着不错的表现。
文摘着重梳理当前产品垃圾评论识别的国内外研究,总结研究特点与不足,发掘发展趋势。在中国知网、Web of Science上以'虚假评论''review spam'等为关键词检索并筛选得到54篇国内外相关文献,采用文献分析法对其进行分类分析,重点阐述研究在识别特征和识别方法方面的优化创新,以及针对垃圾评论、垃圾评论发布者、发布群体等不同识别对象的方法差异。研究发现,当前垃圾评论识别的相关成果可以分为基于评论内容的方法和基于评论结构、评论者、被评论产品的方法,在未来的垃圾评论识别中,应根据数据集的特点,提取有效识别特征,选择优化识别方法。
