[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.
(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 Amazon.com[J]. MIS Quarterly, 2010, 34(1): 185-200.
doi: 10.1007/s10107-008-0244-7
[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.
doi: 10.1109/TST.2015.7128942
(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.
(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. )
doi: 10.7535/hbgykj.2016yx03002
(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.
doi: 10.1016/j.ijhm.2015.07.012
[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.
doi: 10.2139/ssrn.1331124
[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.
(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.)
(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.)
(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.
(Ma Songyue, Xu Xin.Study on User Online Evaluation Based on Sentiment Analysis of Comments: Taking Douban.com Movie as an Example[J]. Library and Information Service, 2016, 60(10): 95-102.)
doi: 10.13266/j.issn.0252-3116.2016.10.013
(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.)
(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.)
(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.)
doi: 10.3969/j.issn.1000-0135.2008.02.004
[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.
doi: 10.1186/2052-336X-12-40
pmid: 3906747
[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.
doi: 10.1007/s11192-014-1279-6
[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.
doi: 10.1016/j.jtbi.2014.06.031
pmid: 24997236
[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.
doi: 10.1016/j.amjcard.2017.01.024
pmid: 28274574