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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (8): 48-58    DOI: 10.11925/infotech.2096-3467.2017.08.06
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Predicting Online Users’ Ratings with Comments
Hongli Zhang,Jiying Liu,Sinan Yang,Jian Xu()
School of Information Management, Sun Yat-Sen University, Guangzhou 510006, China
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[Objective] This study aims to build an effective prediction mechanism for online ratings, with the help of Web surfers’ comments. [Methods] We proposed a model with the following modules: Web users’comment acquisition, predictive variable acquisition, prediction analysis and the prediction results evaluation. We retrieved 30 movies of different types and user’s comments from the Web. 27 movies were used to build the model, which were then examined with the remaining movies. [Results] We employed the stepwise regression to select variables, which included the number of raters, the number of participants posting comments, the number of people who wanted to watch the moive and the sentiment value of the positive comments. The prediction results were quite close to the IMDb scores, and the maximum and the minimum differences were 0.0644 and 0.0227. [Limitations] The sample size, the accuracy of sentiment features, and compatibility of the model could be improved. [Conclusions] The proposed model effectively predicts movie scores and detects the “water army” online.

Key wordsRating Prediction      Sentiment Analysis      Regression Analysis      Movie Rating      "Water Army" Detection     
Received: 31 May 2017      Published: 28 September 2017

Cite this article:

Hongli Zhang,Jiying Liu,Sinan Yang,Jian Xu. Predicting Online Users’ Ratings with Comments. Data Analysis and Knowledge Discovery, 2017, 1(8): 48-58.

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[1] 楼旭东, 刘萍. “网络水军”的传播学分析[J]. 当代传播, 2011(4): 76-77.
[1] (Lou Xudong, Liu Ping.A Communicational Analysis of the “Water-forces in the Network”[J]. Contemporary Communication, 2011(4): 76-77.)
[2] Mudambi S M, Schuff D.What Makes a Helpful Online Review? A Study of Customer Reviews on[J]. MIS Quarterly, 2010, 34(1): 185-200.
[3] Chen Y, Chai Y, Liu Y, et al.Analysis of Review Helpfulness Based on Consumer Perspective[J]. Tsinghua Science & Technology, 2015, 20(3): 293-305.
[4] 吴江, 刘弯弯. 基于信息采纳理论的在线商品评论有用性影响因素研究[J]. 信息资源管理学报, 2017, 7(1): 47-55.
[4] (Wu Jiang, Liu Wanwan.A Research of Factors Affecting the Perceived Helpfulness of Online Product Based on the Information Adoption Theory[J]. Journal of Information Resources Management, 2017, 7(1): 47-55.)
[5] Kuan K K, Hui K, Prasarnphanich P, et al.What Makes a Review Voted? An Empirical Investigation of Review Voting in Online Review Systems[J]. Journal of the Association for Information Systems, 2015, 16(1): 48-71.
[6] 王文君, 张静中. 电子商务网站在线评论对手机销量影响的实证研究[J]. 河北工业科技, 2016, 33(3): 188-193.
[6] (Wang Wenjun, Zhang Jingzhong.An Empirical Study of the Impact of Online Reviews on Mobile Phone Sales in E-commerce[J]. Hebei Journal of Industrial Science and Technology, 2016, 33(3): 188-193. )
[7] 龚诗阳, 刘霞, 赵平. 线上消费者评论如何影响产品销量?——基于在线图书评论的实证研究[J]. 中国软科学, 2013(6): 171-183.
[7] (Gong Shiyang, Liu Xia, Zhao Ping.How do Online Consumer Reviews Influence Product Sales? —An Empirical Study Based on Online Book Reviews.[J] China Soft Science, 2013(6): 171-183.)
[8] Torres E N, Singh D, Robertson-Ring A.Consumer Reviews and the Creation of Booking Transaction Value: Lessons from the Hotel Industry[J]. International Journal of Hospitality Management, 2015, 50: 77-83.
[9] Chintagunta P K, Gopinath S, Venkataraman S, et al.The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets[J]. Marketing Science, 2010, 29(5): 944-957.
[10] Liu B, Hu M, Cheng J.Opinion Observer: Analyzing and Comparing Opinions on the Web[C]////Proceedings of the 14th International Conference on World Wide Web, Chiba, Japan. New York, USA: ACM, 2005: 342-351.
[11] 杜思奇, 李红莲, 吕学强. 汉语组块分析在产品特征提取中的应用研究[J]. 现代图书情报技术, 2015(9): 26-30.
[11] (Du Siqi, Li Honglian, Lv Xueqiang.Research of Chinese Chunk Parsing in Application of the Product Feature Extraction[J]. New Technology of Library and Information Service, 2015(9): 26-30.)
[12] 单晓红, 杨柳. 网络产品评论挖掘研究[J]. 计算机系统应用, 2014, 23(2): 1-6.
[12] (Shan Xiaohong, Yang Liu.Research on Online Product Review Mining[J]. Computer Systems & Applications, 2014, 23(2): 1-6.)
[13] 吴维芳, 高宝俊, 杨海霞, 等. 评论文本对酒店满意度的影响: 基于情感分析的方法[J]. 数据分析与知识发现, 2017, 1(3): 62-71.
[13] (Wu Weifang, Gao Baojun, Yang Haixia, et al.The Impacts of Reviews on Hotel Satisfaction: A Sentiment Analysis Method[J]. Data Analysis and Knowledge Discovery, 2017, 1(3): 62-71.)
[14] 马春平, 陈文亮. 基于评论主题分析的评分预测方法研究[J]. 中文信息学报, 2017, 31(2): 204-211.
[14] (Ma Chunping, Chen Wenliang.A Review Topic Analysis Method for Rating Prediction[J]. Journal of Chinese Information Processing, 2017, 31(2): 204-211.)
[15] Kamath R, Ochi M, Matsuo Y. Understanding Rating Behaviour and Predicting Ratings by Identifying Representative Users[OL]. arXiv PrePrint, arXiv:1604.05468v1.
[16] Titov I, McDonald R. Modeling Online Reviews with Multi-grain Topic Models[C]//// Proceedings of the 17th International Conference on World Wide Web. ACM, 2008: 111-120.
[17] 马松岳, 许鑫. 基于评论情感分析的用户在线评价研究——以豆瓣网电影为例[J]. 图书情报工作, 2016, 60(10): 95-102.
[17] (Ma Songyue, Xu Xin.Study on User Online Evaluation Based on Sentiment Analysis of Comments: Taking Movie as an Example[J]. Library and Information Service, 2016, 60(10): 95-102.)
[18] 程翠琼, 徐健. 面向网络游记时间特征的情感分析模型[J]. 数据分析与知识发现, 2017, 1(2): 87-95.
[18] (Cheng Cuiqiong, Xu Jian.A Sentiment Analysis Model Based on Temporal Characteristics of Travel Blogs[J]. Data Analysis and Knowledge Discovery, 2017, 1(2): 87-95.)
[19] 吴应良, 黄媛, 王选飞. 在线中文用户评论研究综述: 基于情感计算的视角[J]. 情报科学, 2017, 35(6): 159-163.
[19] (Wu Yingliang, Huang Yuan, Wang Xuanfei.Research on Online Users’ Reviews in Chinese: Basing on the Perspective of Affective Computing[J]. Information Science, 2017, 35(6): 159-163.)
[20] 冷建飞, 高旭, 朱嘉平. 多元线性回归统计预测模型的应用[J]. 统计与决策, 2016(7): 82-85.
[20] (Leng Jianfei, Gao Xu, Zhu Jiaping.Application of Multivariate Linear Regression Statistical Prediction Model[J]. Statistics and Decision, 2016(7): 82-85.)
[21] 王伟. 美国电影网站IMDb的榜单文化研究[D]. 长春: 东北师范大学, 2016.
[21] (Wang Wei.An Empirical Analysis of Factors Influencing the Helpfulness of Online Consumer Reviews[D]. Changchun: Northeast Normal University, 2016.)
[22] GooSeeker集搜客网络爬虫, 简单高效的网页采集器[EB/OL]. [2017-03-20]. .
[22] (GooSeeker Web Crawler, Simple and Efficient Web Collector[EB/OL]. [2017-03-20].
[23] 徐琳宏, 林鸿飞, 潘宇, 等. 情感词汇本体的构造[J]. 情报学报, 2008, 27(2): 180-185.
[23] (Xu Linhong, Lin Hongfei, Pan Yu, et al.Constructing the Affective Lexicon Ontology[J]. Journal of the China Society for Scientific and Technical Information, 2008, 27(2): 180-185.)
[24] Ray S. 7 Types of Regression Techniques You Should Know! [EB/OL]. [2017-03-20]. .
[25] Abyaneh H Z.Evaluation of Multivariate Linear Regression and Artificial Neural Networks in Prediction of Water Quality Parameters[J/OL]. Iranian Journal of Environmental Health Science & Engineering, 2014. DOI: 10.1186/2052-336x-12-40.
[26] Yu T, Yu G, Li P Y, et al.Citation Impact Prediction for Scientific Papers Using Stepwise Regression Analysis[J]. Scientometrics, 2014, 101(2): 1233-1252.
[27] Wan S, Mak M, Kung S, et al.R3P-Loc: A Compact Multi-label Predictor Using Ridge Regression and Random Projection for Protein Subcellular Localization[J]. Journal of Theoretical Biology, 2014, 360: 34-45.
[28] Buccheri S, Capodanno D, Barbanti M, et al.A Risk Model for Prediction of 1-Year Mortality in Patients Undergoing MitraClip Implantation[J]. American Journal of Cardiology, 2017, 119(9): 1443-1449.
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