计算机科学
任务(项目管理)
互联网视频
多媒体
互联网
数据科学
视频编辑
比例(比率)
视频处理
万维网
人工智能
管理
量子力学
经济
物理
作者
Jasper Schwenzow,Jochen Hartmann,Amos Schikowsky,Mark Heitmann
标识
DOI:10.1016/j.jbusres.2020.09.059
摘要
Video content has become a major component of total internet traffic. Growing bandwidth and computational power conspire with an increasing number of video editing tools, smartphones, and online platforms that have facilitated video production, distribution, and consumption by businesses and consumers alike. This makes video content relevant across business research disciplines. However, analyzing videos can be a cumbersome manual task. Automated techniques are scattered across technical publications and are often not directly accessible to business researchers. This article synthesizes the current state of the art and provides a consolidated tool to efficiently extract 109 video-based variables, requiring no programming knowledge. The variables include structural video characteristics such as colorfulness as well as advanced content-related features such as scene cuts or human face detection. The authors discuss the research potential of video mining, the types of video features of likely interest, and illustrate application using a practical example.
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