Box office prediction based on microblog

• Published in: Expert systems with applications Vol. 41; no. 4; pp. 1680 - 1689
• Main Authors: Du, Jingfei, Xu, Hua, Huang, Xiaoqiu
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.03.2014; Elsevier
• Subjects: Applied sciences; Artificial intelligence; Biological and medical sciences; Box office; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Counting; Data processing. List processing. Character string processing; Digital media; Exact sciences and technology; Expert systems; Fundamental and applied biological sciences. Psychology; Learning and adaptive systems; Mathematical models; Memory organisation. Data processing; Microblog; Offices; Online; Prediction model; Psychology. Psychoanalysis. Psychiatry; Psychology. Psychophysiology; Semantics; Social media; Social networks; Social psychology; Social structure and organization; Software; Asia; China; Microblog; Social media; Box office; Prediction model; Mobile phone; Content management; Hierarchical classification; Knowledge representation; Social network; Modeling; Social power; Semantics; Learning (artificial intelligence); Vector support machine; Movies; Web site; Artificial intelligence; Pattern extraction; Content analysis
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2013.08.065

•Our method is one of the earliest methods who use machine learning in the question.•The proposed method makes use of information in the view of users’ influence.•Our method classifies microblogs according to their semantics rather sentiments.•The prediction model combines the count based and content based information. As the importance and popularity of online social media has become more obvious, there are more researches aiming at making use of information from them. One important topic of this is predicting the future with social media. This paper focuses on predicting box offices using microblog. Compared with previous work which makes use of the count of related microblogs simply, the information from social media has been utilized more deeply in this paper. Two sets of features have been extracted: count based features and content based features. For the former, the information in the aspect of users, which decrease the influence of garbage microblogs, has been exploited. For content based features, a new box office oriented semantic classification method has been provided to make the features more relative with box offices. Meanwhile, more complex machine learning models such as SVM and neutral network have been applied to the prediction method. Our prediction model is more accurate and reliable. With our prediction method, the data in Tencent microblog has been utilized to predict box offices of certain movies in China. With the results, the strength of our method and predictive power of online social media can be completely demonstrated.