[Objective] This study aims to solve the problems of the existing pre-release box office prediction models due to data constraints and other factors. [Methods] We first retrieved microblog comments, and then used SVM to identify explicit consumer intention, namely strong positive comments. Second, we modified the traditional sentiment classification schemes to build a Chinese microblog sentiment dictionary based on HowNet. Finally, we defined a new user influence feature and used the BP neural network to predict box office. [Results] The proposed model could forecast the opening box office more accuately. [Limitations] Due to inadequate corpus, the sentiment dictionary may not work well for all microblog movie comments. A dynamic forecasting model was not established between the pre-release and post-release period. [Conclusions] The proposed model can effectively predict opening box office.
王晓耘,袁媛,史玲玲. 基于微博的电影首映周票房预测建模*[J]. 现代图书情报技术, 2016, 32(4): 31-39.
Wang Xiaoyun,Yuan Yuan,Shi Lingling. Predicting Opening Weekend Box Office Prediction Based on Microblog. New Technology of Library and Information Service, 2016, 32(4): 31-39.
(Yin Pei, Wang Hongwei, Guo Kaiqiang.Feature- opinion Pair Identification in Chinese Online Reviews Based on Domain Ontology Modeling Method[J]. Systems Engineering, 2013, 31(1): 68-77.)
(Zhang Chuang, Jiang Yang, Wu Ming, et al.Information Predictions Based on Node Attributes of Social Media[J]. Journal of Beijing University of Posts and Telecommunications, 2012, 35(4): 24-27.)
[3]
Liu Y, Huang X, An A, et al.ARSA: A Sentiment-aware Model for Predicting Sales Performance Using Blogs [C]. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2007: 607-614.
[4]
Neelamegham R, Chintagunta P.A Bayesian Model to Forecast New Product Performance in Domestic and International Markets[J]. Marketing Science, 1999, 18(2): 115-136.
[5]
Elberse A, Eliashberg J.Demand and Supply Dynamics for Sequentially Released Products in International Markets: The Case of Motion Pictures[J]. Marketing Science, 2003, 22(3): 329-354.
[6]
Liu Y.Word of Mouth for Movies: Its Dynamics and Impact on Box Office Revenue[J]. Journal of Marketing, 2006, 70(3): 74-89.
[7]
Sawhney M S, Eliashberg J.A Parsimonious Model for Forecasting Gross Box-office Revenues of Motion Pictures[J]. Marketing Science, 1996, 15(2): 113-131.
[8]
Eliashberg J, Shugan S M.Film Critics: Influencers or Predictors?[J]. Journal of Marketing, 1997, 61(2): 68-78.
[9]
Krider R E, Weinberg C B.Competitive Dynamics and the Introduction of New Products: The Motion Picture Timing Game[J]. Journal of Marketing Research, 1998, 35(1): 1-15.
[10]
Asur S, Huberman B A.Predicting the Future with Social Media [C]. In: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. IEEE Computer Society, 2010: 492-499.
[11]
Du J, Xu H, Huang X.Box Office Prediction Based on Microblog[J]. Expert Systems with Applications, 2014, 41(4): 1680-1689.
[12]
Eliashberg J, Jonker J J, Sawhney M S, et al.MOVIEMOD: An Implementable Decision-Support System for Prerelease Market Evaluation of Motion Pictures[J]. Marketing Science, 2015, 19(3): 226-243.
[13]
Shugan S M, Swait J.Enabling Movie Design and Cumulative Box Office Predictions Using Historical Data and Consumer Intent-to-View [R]. University of Florida, 2000.
[14]
陈晓东. 基于情感词典的中文微博情感倾向分析研究[D]. 武汉: 华中科技大学, 2012.
[14]
(Chen Xiaodong.Research on Sentiment Dictionary Based Emotional Tendency Analysis of Chinese Microblog [D]. Wuhan: Huazhong University of Science & Tchnology, 2012.)
(Wang Lian, Jia Jianmin.Forecasting Box Office Performance Based on Online Search:Evidence from Chinese Movie Industry[J]. Systems Engineering-Theory & Practice, 2014, 34(12): 3079-3090.)
[16]
Fu B, Liu T.Weakly-supervised Consumption Intent Detection in Microblogs[J]. Journal of Computational Information Systems, 2013, 6(9): 2423-2431.
[17]
陈浩辰. 基于微博的消费意图挖掘[D]. 哈尔滨: 哈尔滨工业大学, 2014.
[17]
(Chen Haochen.Consumption Intention Mining Based on Microblog [D]. Harbin: Harbin Institute of Technology, 2014.)
[18]
Vapnik V N, Vapnik V.Statistical Learning Theory[M]. New York: Wiley, 1998.
(Du Weifu, Tan Songbo, Yun Xiaochun, et al.A New Method to Compute Semantic Orientation[J]. Journal of Computer Research and Development, 2009, 46(10): 1713-1720.)
[20]
郭叶. 中文句子情感倾向分析[D]. 北京: 北京邮电大学, 2010.
[20]
(Guo Ye.Sentiment Orientation Analysis of Chinese Sentences [D]. Beijing: Beijing University of Posts and Telecommunications, 2010.)
