Abstract
In this article, we discuss various elements contributing to exerting influence on box office in China, which are divided into internal and external factors. Since these factors could merely be quantified by online data sources partially or inaccurately, we propose that relativity analysis is more reasonable than precise revenue prediction. Trailer is selected as the combination of movie content and online behavior prior to releasing. Indexes from seven mainstream video websites are retrieved by the designed big data system which is integrated with the Internet of things technology. Correlation coefficients of different time periods are calculated. We apply multiple linear regression with stepwise method in modeling and prove that watching counts of 1 week before releasing on Youku is the barometer of market performance, especially the first week revenues. We also manifest the power of influential users through constructing Sina Weibo acquisition and analysis system.
Highlights
The Internet of things (IoT) and big data[1] have gotten more and more attention with the development of social and economy in China
Taking the sphere of entertainment as an example, big data integrated with IoT technology[3] and the people behind are broadly applicated in industry upgrading and optimization
As A Bhave et al.[21] and MT Lash and K Zhao[22] had proposed, both internal and external factors of films were crucial in predicting revenues based on papers listed above, which are exactly what we suggest and analyze below
Summary
The Internet of things (IoT) and big data[1] have gotten more and more attention with the development of social and economy in China. Their combination[2] is opening splendid opportunities for a large number of interesting fields, especially everyday objects. Is revenue the most important indicator of the success of a film, and the profit of the investors. This article discusses the complexity of box-office prediction in detail and focuses more on the relativity analysis rather than the accuracy of forecast. We select trailers in seven domestic video websites as a combination of the three features mentioned in the last paragraph and collect relevant indexes with the designed big data
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