基于特征观点对语义匹配的产品评论可信度研究
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摘要
[目的/意义]针对产品评论中的复合句式,实现特征观点对的语义匹配及提取,并明确评论可信度的识别因素及权重,对产品可信评论进行筛选和分析。[方法/过程]基于特征观点对的语义匹配算法实现评论语义指标的量化计算,并采用模糊层次分析法确定可信度指标权重。[结果/结论]实验表明相较于单句提取特征观点对方法,特征观点对的语义匹配算法在召回率、准确率和F-score等性能方面均有较大优势。依据可信度指标对网站产品评论进行筛选,不仅可以评估产品整体的评论可信度,还可以细化到产品特征级别的可信度分析,为用户筛选可信的评论信息并提升购物决策效率。
[Purpose/Significance]In view of the compound sentence pattern in the product reviews,this paper realized the semantic matching and extraction of the feature opinion pairs,and made clear the indicators and weights of the reviews credibility so as to select and analyze the trusted reviews of the products.[Method/Process]Based on semantic matching algorithm of feature opinion pairs,we extracted the feature opinion pairs and calculated the semantic indicator of reviews,then used Fuzzy Analytic Hierarchy Process to determine the weight of indicators.[Result/Conclusion]The experiment showed that semantic matching algorithm of the feature opinion pairs had a great advantage on the performance of the recall,accuracy and F-score,compared with the method of extracting feature points from the single sentence.It could not only evaluate the credibility of the overall review of the product,but also could be refined to the reliability analysis of the product feature level.Meanwhile,it could screen credible reviews for users and improve the efficiency of shopping decisions.
[Purpose/Significance]In view of the compound sentence pattern in the product reviews,this paper realized the semantic matching and extraction of the feature opinion pairs,and made clear the indicators and weights of the reviews credibility so as to select and analyze the trusted reviews of the products.[Method/Process]Based on semantic matching algorithm of feature opinion pairs,we extracted the feature opinion pairs and calculated the semantic indicator of reviews,then used Fuzzy Analytic Hierarchy Process to determine the weight of indicators.[Result/Conclusion]The experiment showed that semantic matching algorithm of the feature opinion pairs had a great advantage on the performance of the recall,accuracy and F-score,compared with the method of extracting feature points from the single sentence.It could not only evaluate the credibility of the overall review of the product,but also could be refined to the reliability analysis of the product feature level.Meanwhile,it could screen credible reviews for users and improve the efficiency of shopping decisions.
引文
[1]中国互联网络信息中心.2015年中国互联网络发展状况统计报告[EB/OL].http://www.cnnic.net.cn,2016-06-22.
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[3]Bickart B,Schindler R M.Internet Forums as Influential Sources of Consumer Information[J].Journal of Interactive Marketing,2001,15(3):31-40.
[4]张薇薇,柏露.网络评论可信度影响因素研究述评[J].情报理论与实践,2016,39(6):131-138.
[5]王倩倩.一种在线商品评论信息可信度的排序方法[J].情报杂志,2015,34(3):181-185.
[6]陈燕方.基于DDAG- SVM 的在线商品评论可信度分类模型[J].情报理论与实践,2017,40(7):132-137.
[7]陈燕方,李志宇.基于评论产品属性情感倾向评估的虚假评论识别研究[J].现代图书情报技术,2014,(9):81-90.
[8]吴江,刘弯弯.什么样的评论更容易获得有用性投票——以亚马逊网站研究为例[J].数据分析与知识发现,2017,(9):16-27.
[9]王忠群,吴东胜,蒋胜.一种基于主流特征观点对的评论可信性排序研究[J].现代图书情报技术,2017,1(10):32-42.
[10]Weathers D,Swain S D,Grover V.Can Online Product Reviews Be More Helpful?Examining Characteristics of Information Content By Product Type[J].Decision Support Systems,2015,79:12-23.
[11]Mackiewicz J,Yeats D,Thornton T.The Impact of Review Environment on Review Credibility[J].IEEE Transactions on Professional Communication,2016,59 (2):71-88.
[12]Jindal N,Liu B.Review Spam Detection[C].16th International World Wide Web Conference,WWW2007,Banff,Alberta,Canada,2007:1189-1190.
[13]Racherla P,Friske W.Perceived“Usefulness”of Online Consumer Reviews:An Exploratory Investigation Across Three Services Categories[J].Electronic Commerce Research & Applications,2012,11(6):548-559.
[14]Mukherjee A,Venkataraman V,Liu B,et al.What Yelp Fake Review Filter Might Be Doing?[C].In:Proceedings of the 7th International Conference on Weblogs and Social Media.Palo Alto:AAAI Press,2013:409-418.
[15]Peng Q,Zhong M.Detecting Spam Review Through Sentiment Analysis[J].Journal of Software,2014,9(8):2065-2072.
[16]孟美任,丁晟春.在线中文商品评论可信度研究[J].现代图书情报技术,2013,(9):60-66.
[17]Lee S,Choeh J Y.The Determinants of Helpfulness of Online Reviews[J].Behavior & Information Technonogy,2016,35(10):853-863.
[18]Li F,Huang M,Yang Y,et al.Learning to Identify Review Spam[C].In:Proceedings of the 22nd International Joint Conference on Artificial Intelligence.AAAI Press,2011:2488-2493.
[19]Gorla N,Somers T M,Wong B.Organizational Impact of System Quality,Information Quality,and Service Quality[J].Journal of Strategic Information Systems,2010,19(3):207-228.
[20]Cheung C M K,Thadani D R.The Impact of Electronic Word-of-mouth Communication:A Literature Analysis and Integrative Model[J].Decision Support Systems,2012,54:461-470.
[21]Zhang R,Gao M,He X,et al.Learning User Credibility for Product Ranking[J].Knowledge & Information Systems,2016,46 (3):679-705.
[22]Qiu L,Pang J,Kai H L.Effects of Conflicting Aggregated Rating on eWOM Review Credibility and Diagnosticity:The Moderating Role of Review Valence[J].Decision Support Systems,2012,54(1):631-643.
[23]王宇,李秀秀.基于电子商务评论的商家信誉维度构建[J].数据分析与知识发现,2017,(8):59-67.
[24]Yin P,Wang,H W,Guo K Q.Feature-opinion Pair Identification of Product Reviews in Chinese:A Domain Ontology Modeling Method[J].New Review of Hypermedia and Multimedia,2013,19(1):3-24.
[25]姚敏,黄燕君.模糊决策方法研究[J].系统工程理论与实践,1999,(11):61-70.
[2]Lee M,Youn S.Electronic Word of Mouth(eWOM):How eWOM Platforms Influence Consumer Product Judgement[J].International Journal of Advertising,2009,28 (3):473-499.
[3]Bickart B,Schindler R M.Internet Forums as Influential Sources of Consumer Information[J].Journal of Interactive Marketing,2001,15(3):31-40.
[4]张薇薇,柏露.网络评论可信度影响因素研究述评[J].情报理论与实践,2016,39(6):131-138.
[5]王倩倩.一种在线商品评论信息可信度的排序方法[J].情报杂志,2015,34(3):181-185.
[6]陈燕方.基于DDAG- SVM 的在线商品评论可信度分类模型[J].情报理论与实践,2017,40(7):132-137.
[7]陈燕方,李志宇.基于评论产品属性情感倾向评估的虚假评论识别研究[J].现代图书情报技术,2014,(9):81-90.
[8]吴江,刘弯弯.什么样的评论更容易获得有用性投票——以亚马逊网站研究为例[J].数据分析与知识发现,2017,(9):16-27.
[9]王忠群,吴东胜,蒋胜.一种基于主流特征观点对的评论可信性排序研究[J].现代图书情报技术,2017,1(10):32-42.
[10]Weathers D,Swain S D,Grover V.Can Online Product Reviews Be More Helpful?Examining Characteristics of Information Content By Product Type[J].Decision Support Systems,2015,79:12-23.
[11]Mackiewicz J,Yeats D,Thornton T.The Impact of Review Environment on Review Credibility[J].IEEE Transactions on Professional Communication,2016,59 (2):71-88.
[12]Jindal N,Liu B.Review Spam Detection[C].16th International World Wide Web Conference,WWW2007,Banff,Alberta,Canada,2007:1189-1190.
[13]Racherla P,Friske W.Perceived“Usefulness”of Online Consumer Reviews:An Exploratory Investigation Across Three Services Categories[J].Electronic Commerce Research & Applications,2012,11(6):548-559.
[14]Mukherjee A,Venkataraman V,Liu B,et al.What Yelp Fake Review Filter Might Be Doing?[C].In:Proceedings of the 7th International Conference on Weblogs and Social Media.Palo Alto:AAAI Press,2013:409-418.
[15]Peng Q,Zhong M.Detecting Spam Review Through Sentiment Analysis[J].Journal of Software,2014,9(8):2065-2072.
[16]孟美任,丁晟春.在线中文商品评论可信度研究[J].现代图书情报技术,2013,(9):60-66.
[17]Lee S,Choeh J Y.The Determinants of Helpfulness of Online Reviews[J].Behavior & Information Technonogy,2016,35(10):853-863.
[18]Li F,Huang M,Yang Y,et al.Learning to Identify Review Spam[C].In:Proceedings of the 22nd International Joint Conference on Artificial Intelligence.AAAI Press,2011:2488-2493.
[19]Gorla N,Somers T M,Wong B.Organizational Impact of System Quality,Information Quality,and Service Quality[J].Journal of Strategic Information Systems,2010,19(3):207-228.
[20]Cheung C M K,Thadani D R.The Impact of Electronic Word-of-mouth Communication:A Literature Analysis and Integrative Model[J].Decision Support Systems,2012,54:461-470.
[21]Zhang R,Gao M,He X,et al.Learning User Credibility for Product Ranking[J].Knowledge & Information Systems,2016,46 (3):679-705.
[22]Qiu L,Pang J,Kai H L.Effects of Conflicting Aggregated Rating on eWOM Review Credibility and Diagnosticity:The Moderating Role of Review Valence[J].Decision Support Systems,2012,54(1):631-643.
[23]王宇,李秀秀.基于电子商务评论的商家信誉维度构建[J].数据分析与知识发现,2017,(8):59-67.
[24]Yin P,Wang,H W,Guo K Q.Feature-opinion Pair Identification of Product Reviews in Chinese:A Domain Ontology Modeling Method[J].New Review of Hypermedia and Multimedia,2013,19(1):3-24.
[25]姚敏,黄燕君.模糊决策方法研究[J].系统工程理论与实践,1999,(11):61-70.