Text-based video content classification for online video-sharing sites

  • Authors:

  • Chunneng Huang;Tianjun Fu;Hsinchun Chen

  • Affiliations:

  • AI Lab, Department of Management Information Systems, University of Arizona, Tucson, AZ 85721;AI Lab, Department of Management Information Systems, University of Arizona, Tucson, AZ 85721;AI Lab, Department of Management Information Systems, University of Arizona, Tucson, AZ 85721

  • Venue:
  • Journal of the American Society for Information Science and Technology
  • Year:
  • 2010

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Abstract

With the emergence of Web 2.0, sharing personal content, communicating ideas, and interacting with other online users in Web 2.0 communities have become daily routines for online users. User-generated data from Web 2.0 sites provide rich personal information (e.g., personal preferences and interests) and can be utilized to obtain insight about cyber communities and their social networks. Many studies have focused on leveraging user-generated information to analyze blogs and forums, but few studies have applied this approach to video-sharing Web sites. In this study, we propose a text-based framework for video content classification of online-video sharing Web sites. Different types of user-generated data (e.g., titles, descriptions, and comments) were used as proxies for online videos, and three types of text features (lexical, syntactic, and content-specific features) were extracted. Three feature-based classification techniques (C4.5, Naïve Bayes, and Support Vector Machine) were used to classify videos. To evaluate the proposed framework, user-generated data from candidate videos, which were identified by searching user-given keywords on YouTube, were first collected. Then, a subset of the collected data was randomly selected and manually tagged by users as our experiment data. The experimental results showed that the proposed approach was able to classify online videos based on users' interests with accuracy rates up to 87.2%, and all three types of text features contributed to discriminating videos. Support Vector Machine outperformed C4.5 and Naïve Bayes techniques in our experiments. In addition, our case study further demonstrated that accurate video-classification results are very useful for identifying implicit cyber communities on video-sharing Web sites. © 2010 Wiley Periodicals, Inc.