Please wait a minute...
New Technology of Library and Information Service  2016, Vol. 32 Issue (4): 31-39    DOI: 10.11925/infotech.1003-3513.2016.04.04
Orginal Article Current Issue | Archive | Adv Search |
Predicting Opening Weekend Box Office Prediction Based on Microblog
Wang Xiaoyun,Yuan Yuan(),Shi Lingling
Management School, Hangzhou Dianzi University, Hangzhou 310012, China
Download: PDF(665 KB)   HTML ( 58
Export: BibTeX | EndNote (RIS)      

[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.

Key wordsSentiment dictionary      Sentiment classification      Opening weekend box office prediction      Neural network     
Received: 11 September 2015      Published: 13 May 2016

Cite this article:

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.

URL:     OR

[1] 尹裴, 王洪伟, 郭恺强. 中文产品评论的“特征观点对”识别: 基于领域本体的建模方法[J].系统工程, 2013, 31(1): 68-77.
[1] (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.)
[2] 张闯, 姜杨, 吴铭, 等. 基于社会化媒体节点属性的信息预测[J]. 北京邮电大学学报, 2012, 35(4): 24-27.
[2] (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.)
[15] 王炼, 贾建民. 基于网络搜索的票房预测模型——来自中国电影市场的证据[J]. 系统工程理论与实践, 2014, 34(12): 3079-3090.
[15] (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.
[19] 杜伟夫, 谭松波, 云晓春, 等. 一种新的情感词汇语义倾向计算方法[J]. 计算机研究与发展, 2009, 46(10): 1713-1720.
[19] (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.)
[21] 王铮, 许敏. 电影票房的影响因素分析——基于Logit 模型的研究[J]. 经济问题探索, 2013 (11): 96-102.
[21] (Wang Zheng, Xu Min.Analysis of the Influence Factors of Movie Box Office—Based on Logit Model[J]. Inquiry into Economic Issues, 2013 (11): 96-102.)
[22] 游建平. 基于语义情感空间模型的微博情感倾向性研究[D]. 广州: 暨南大学, 2012.
[22] (You Jianping.Micro-Blog Sentiment Analysis Based on Semantic Sentiment Space Model [D]. Guangzhou: Jinan University, 2012.)
[1] Zhenyu He,Xiangxiang Dong,Qinghua Zhu. Classifying Baidu Encyclopedia Entries with User Behaviors[J]. 数据分析与知识发现, 2019, 3(6): 117-122.
[2] Kan Liu,Lu Chen. Deep Neural Network Learning for Medical Triage[J]. 数据分析与知识发现, 2019, 3(6): 99-108.
[3] Wancheng Chen,Haoran Dai,Yinghan Jin. Appraising Home Prices with HEDONIC Model: Case Study of Seattle, U.S.[J]. 数据分析与知识发现, 2019, 3(5): 19-26.
[4] Qingqing Zhang,Xingshi He,Huimin Wang,Shengjun Meng. Text Sentiment Classification Based on Deep Belief Network[J]. 数据分析与知识发现, 2019, 3(4): 71-79.
[5] Hui Li,Yaqing Chai. Fine-Grained Sentiment Analysis Based on Convolutional Neural Network[J]. 数据分析与知识发现, 2019, 3(1): 95-103.
[6] Yuemei Xu,Sining Lv,Lianqiao Cai,Xiaoya Zhang. Analyzing News Topic Evolution with Convolutional Neural Networks and Topic2Vec[J]. 数据分析与知识发现, 2018, 2(9): 31-41.
[7] Xiaoyu Ma,Han Zhang,Yuhong Zhao. Building Childhood Asthma Prediction Model with Artificial Neural Network and BRFSS Database[J]. 数据分析与知识发现, 2018, 2(8): 10-15.
[8] Shuyi Wang,Huatao Liao,Chake Wu. Mining News on Competitors with Sentiment Classification[J]. 数据分析与知识发现, 2018, 2(3): 70-78.
[9] Hu Meng,Xiaobei Liang,Yixiong Yang,Min Li. Evaluating and Optimizing Supply Chains with LMBP Algorithm[J]. 数据分析与知识发现, 2018, 2(11): 37-45.
[10] Yuying Wu,Ping Sun,Xijun He,Guorui Jiang. Predicting Transactions Among Agents in Patent Transfer Weighted Networks for New Energy[J]. 数据分析与知识发现, 2018, 2(11): 73-79.
[11] Yanhui Xiao,Xin Wang,Wen’gang Feng,Huawei Tian,Shaozhong Wu,Lihua Li. Predicting Crime Locations Based on Long Short Term Memory and Convolutional Neural Networks[J]. 数据分析与知识发现, 2018, 2(10): 15-20.
[12] Xiaoxi Huang,Hanyu Li,Rongbo Wang,Xiaohua Wang,Zhiqun Chen. Recognizing Metaphor with Convolution Neural Network and SVM[J]. 数据分析与知识发现, 2018, 2(10): 77-83.
[13] Jiaheng Hu,Yonghua Cen,Chengyao Wu. Constructing Sentiment Dictionary with Deep Learning: Case Study of Financial Data[J]. 数据分析与知识发现, 2018, 2(10): 95-102.
[14] Erjing Chen,Enbo Jiang. Review of Studies on Text Similarity Measures[J]. 数据分析与知识发现, 2017, 1(6): 1-11.
[15] Qingqing Zhang,Xilin Liu. Classifying Sentiments Based on BPSO Random Subspace[J]. 数据分析与知识发现, 2017, 1(5): 71-81.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938