依据在线评论的商品排序方法
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摘要
提出了一种依据商品在线评论的基于多粒度情感强度分析和随机逼近理想点排序法的商品排序方法.使用爬虫软件和ICTCLAS对消费者关注的备选商品的在线评论进行获取和预处理.依据预处理后的评论,通过提出多粒度情感强度分析算法确定每条评论针对商品属性的情感强度值.通过对得到的情感强度值进行统计分析,得到备选商品针对商品属性的多粒度情感强度分布形式的属性值.最后,依据得到多粒度情感强度分布形式的属性值,采用随机逼近理想点排序法确定备选商品的排序.基于中关村在线中的数码相机在线评论,给出了提出方法应用的实例分析.
How to automatically analyze the huge amounts of online reviews and rank products is a new important research topic. This paper proposes a method based on multi-granularity sentiment strength analysis and stochastic technique to order preferences for products through online reviews according to the closeness to an ideal solution(TOPSIS). In this method, online reviews of alternative products are first crawled by web crawler software and processed by ICTCLAS software. Then, according to the processed online reviews, an algorithm is given to calculate the sentiment strengths of online reviews concerning product features. Furthermore, according to the results of sentiment strength analysis, the feature values in the form of distribution concerning multi-granularity sentiment strengths can be obtained by statistical analysis. According to the obtained feature values, the ranking of alternative products can be determined by stochastic TOPSIS method. Finally, based on the online reviews on digital camera from the Zhongguancun online, a case analysis is given to illustrate the proposed method.
How to automatically analyze the huge amounts of online reviews and rank products is a new important research topic. This paper proposes a method based on multi-granularity sentiment strength analysis and stochastic technique to order preferences for products through online reviews according to the closeness to an ideal solution(TOPSIS). In this method, online reviews of alternative products are first crawled by web crawler software and processed by ICTCLAS software. Then, according to the processed online reviews, an algorithm is given to calculate the sentiment strengths of online reviews concerning product features. Furthermore, according to the results of sentiment strength analysis, the feature values in the form of distribution concerning multi-granularity sentiment strengths can be obtained by statistical analysis. According to the obtained feature values, the ranking of alternative products can be determined by stochastic TOPSIS method. Finally, based on the online reviews on digital camera from the Zhongguancun online, a case analysis is given to illustrate the proposed method.
引文
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[19]Fan Z P,Zhang X,Liu Y,et al.A method for stochastic multiple attribute decision making based on concepts of ideal and anti-ideal points.Applied Mathematics and Computation,2013,219(24):11438–11450.
[2]刘洋,廖貅武,刘莹.在线评论对应用软件及平台定价策略的影响.系统工程学报,2014,29(4):560–570.Liu Y,Liao X W,Liu Y.The impact of online review on software and platform’s pricing strategies.Journal of System Engineering,2014,29(4):560–570.(in Chinese)
[3]Hennig-Thurau T,Gwinner K P,Walsh G,et al.Electronic word-of-mouth via consumer opinion platforms:What motivates consumers to articulate themselves on the internet.Journal of Interactive Marketing,2004,18(1):38–52.
[4]Liu Y,Bi J W,Fan Z P.Ranking products through online reviews:A method based on sentiment analysis technique and intuitionistic fuzzy set theory.Information Fusion,2017,36:149–161.
[5]Senecal S,Nantel J.The influence of online product recommendations on consumers’online choices.Journal of Retailing,2004,80(2):159–169.
[6]张紫琼,叶强,李一军.互联网商品评论情感分析研究综述.管理科学学报,2010,13(6):84–96.Zhang Z Q,Ye Q,Li Y J.Literature review on sentiment analysis of online product reviews.Journal of Management Sciences in China,2010,13(6):84–96.(in Chinese)
[7]Zhang W,Xu H,Wan W.Weakness finder:Find product weakness from Chinese reviews by using aspects based sentiment analysis.Expert Systems with Applications,2012,39(11):10283–10291.
[8]Zhang K,Narayanan R,Choudhary A.Mining Online Customer Reviews for Ranking Products.Technical Report,EECS Department,Northwestern University,2009.
[9]Zhang K,Narayanan R,Choudhary A.Voice of the customers:Mining online customer reviews for product feature-based ranking//Proceedings of the 3rd Conference on Online Social Networks.2010.
[10]Zhang K,Cheng Y,Liao W,et al.Mining millions of reviews:A technique to rank products based on importance of reviews//Proceedings of the 13th ACM International Conference on Electronic Commerce.2011.
[11]Peng Y,Kou G,Li J.A fuzzy PROMETHEE approach for mining customer reviews in Chinese.Arabian Journal for Science and Engineering,2014,39(6):5245–5252.
[12]Chen K,Kou G,Shang J,et al.Visualizing market structure through online product reviews:Integrate topic modeling,TOPSIS,And multi-dimensional scaling approaches.Electronic Commerce Research And Applications,2015,14(1):58–74.
[13]Najmi E,Hashmi K,Malik Z,et al.CAPRA:A comprehensive approach to product ranking using customer reviews.Computing,2015,97(8):843–866.
[14]Yang X,Yang G,Wu J.Integrating rich and heterogeneous information to design a ranking system for multiple products.Decision Support Systems,2016,84:117–133.
[15]Serrano-Guerrero J,Olivas J A,Romero F P,et al.Sentiment analysis:A review and comparative analysis of web services.Information Sciences,2015,311:18–38.
[16]Tang H,Tan S,Cheng X.A survey on sentiment detection of reviews.Expert Systems with Applications,2009,36(7):10760–10773.
[17]Liu Q,Li S.Word similarity computing based on How-Net//Proceedings of the 3th Chinese Lexical Semantic Workshop.2002.
[18]Huang S L,Cheng W C.Discovering Chinese sentence patterns for feature-based opinion summarization.Electronic Commerce Research and Applications,2015,14(6):582–591.
[19]Fan Z P,Zhang X,Liu Y,et al.A method for stochastic multiple attribute decision making based on concepts of ideal and anti-ideal points.Applied Mathematics and Computation,2013,219(24):11438–11450.