Advertisement

Concept Discovery in Youtube.com Using Factorization Method

  • Janice Kwan-Wai Leung
  • Chun Hung Li
Chapter

Abstract

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.

Keywords

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.

References

  1. 1.
  2. 2.
  3. 3.
  4. 4.
  5. 5.
  6. 6.
  7. 7.
    Usa today. youtube serves up 100 million videos a day onlineGoogle Scholar
  8. 8.
    F. Benevenuto, F. Duarte, T. Rodrigues, V. A. Almeida, J. M. Almeida, and K. W. Ross. Understanding video interactions in youtube. In MM ’08: Proceeding of the 16th ACM international conference on Multimedia, pages 761–764, ACM, NY, 2008Google Scholar
  9. 9.
    D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993–1022, 2003MATHCrossRefGoogle Scholar
  10. 10.
    X. Cheng, C. Dale, and J. Liu. Understanding the characteristics of internet short video sharing: Youtube as a case study. In CoRR abs, Jul 2007Google Scholar
  11. 11.
    G. Geisler and S. Burns. Tagging video: conventions and strategies of the youtube community. In JCDL ’07: Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries, pages 480–480, ACM, NY, 2007Google Scholar
  12. 12.
    L. Guo, S. Jiang, L. Xiao, and X. Zhang. Fast and low-cost search schemes by exploiting localities in p2p networks. J. Parallel Distrib. Comput., 65(6):729–742, 2005CrossRefGoogle Scholar
  13. 13.
    M. J. Halvey and M. T. Keane. Exploring social dynamics in online media sharing. In WWW ’07: Proceedings of the 16th international conference on World Wide Web, pages 1273–1274, ACM, NY, 2007Google Scholar
  14. 14.
    M. Heckner, T. Neubauer, and C. Wolff. Tree, funny, to read, google: what are tags supposed to achieve? a comparative analysis of user keywords for different digital resource types. In SSM ’08: Proceeding of the 2008 ACM workshop on Search in social media, pages 3–10, ACM, NY, 2008Google Scholar
  15. 15.
    J. Heer and D. Boyd. Vizster: Visualizing online social networks. IEEE Symposium on Information Visualization, 2005Google Scholar
  16. 16.
    C. M. C. Y. Julia Stoyanovich, Sihem Amer-Yahia. Leveraging tagging to model user interests in del.icio.us. In AAAI ’08: Proceedings of the 2008 AAAI Social Information Spring Symposium. AAAI, 2008Google Scholar
  17. 17.
    P. P. Kotsiantis S., Kanellopoulos D. Multimedia mining. In WSEAS Trans. Syst., 3(10): 3263–3268, 2004Google Scholar
  18. 18.
    J. K.-W. Leung, C. H. Li, and T. K. Ip. Commentary-based video categorization and concept discovery. In Proceeding of the 2nd ACM Workshop on Social Web Search and Mining (Hong Kong, China, November 02 - 02, 2009), SWSM ’09, pages 49–56, ACM, New York, NY, 2009Google Scholar
  19. 19.
    X. Li, L. Guo, and Y. E. Zhao. Tag-based social interest discovery. In WWW ’08: Proceeding of the 17th international conference on World Wide Web, pages 675–684, ACM, NY, 2008Google Scholar
  20. 20.
    N. Oza, J. P. Castle, and J. Stutz. Classification of aeronautics system health and safety documents. Trans. Sys. Man Cyber Part C, 39(6):670–680, 2009CrossRefGoogle Scholar
  21. 21.
    J. Z. Pan, S. Taylor, and E. Thomas. Reducing ambiguity in tagging systems with folksonomy search expansion. In ESWC 2009 Heraklion: Proceedings of the 6th European Semantic Web Conference on The Semantic Web, pages 669–683, Springer, Berlin, 2009Google Scholar
  22. 22.
    C. G. R. A. A. F. L. Rodrygo L. T. Santos, Bruno P. S. Rocha. Characterizing the youtube video-sharing community. 2007Google Scholar
  23. 23.
    M. F. Schwartz and D. C. M. Wood. Discovering shared interests using graph analysis. Commun. ACM, 36(8):78–89, 1993CrossRefGoogle Scholar
  24. 24.
    A. S. Sharma and M. Elidrisi. Classification of multi-media content (video’s on youtube) using tags and focal points. Unpublished manuscriptGoogle Scholar
  25. 25.
    K. Sripanidkulchai, B. Maggs, and H. Zhang. Efficient content location using interest-based locality in peer-to-peer systems. In INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications Societies. IEEE, 3:2166–2176, 2003Google Scholar
  26. 26.
    S. Tsekeridou and I. Pitas. Content-based video parsing and indexing based on audio-visual interaction, 2001Google Scholar
  27. 27.
    W. Xu, X. Liu, and Y. Gong. Document clustering based on non-negative matrix factorization. In SIGIR ’03: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pages 267–273, ACM, NY, 2003Google Scholar
  28. 28.
    L. Yang, J. Liu, X. Yang, and X.-S. Hua. Multi-modality web video categorization. In MIR ’07: Proceedings of the international workshop on Workshop on multimedia information retrieval, pages 265–274, ACM, NY, 2007Google Scholar
  29. 29.
    O. R. Zaïane, J. Han, Z.-N. Li, S. H. Chee, and J. Y. Chiang. Multimediaminer: a system prototype for multimedia data mining. In SIGMOD ’98: Proceedings of the 1998 ACM SIGMOD international conference on Management of data, pages 581–583, ACM, NY, 1998Google Scholar
  30. 30.
    L. Zunxiong, Z. Lihui, and Z. Heng. Appearance-based subspace projection techniques for face recognition. Intelligent Interaction and Affective Computing, International Asia Symposium on, pages 202–205, 2009Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

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

Personalised recommendations