Concept Discovery in Using Factorization Method

  • Janice Kwan-Wai LeungEmail author
  • Chun Hung Li


Social media are not limited to text but also multimedia. Dailymotion, YouTube, and MySpace are examples of successful sites which allow users to share videos and interact among themselves. Due to the huge amount of videos, categorizing videos with similar contents can help users to search videos more efficiently. Unlike the traditional approach to group videos into some predefined categories, we propose to facilitate video searching with clustering from comment-based matrix factorization and to improve indexing via the generation of new concept words. Factorized component entropies are introduced for handling the difficult problem of vocabulary construction for concept discovery in social media. Since the categorization is learnt from users feedback, it can accurately represent the user sentiment on the videos. Experiments conducted by using empirical data collected from YouTube shows the effectiveness of our proposed methodologies.


Video Content Latent Dirichlet Allocation User Comment User Interest Music Video 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Computer ScienceHong Kong Baptist UniversityKowloonHong Kong

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