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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Usa today. youtube serves up 100 million videos a day online
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, 2008
D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993–1022, 2003
X. Cheng, C. Dale, and J. Liu. Understanding the characteristics of internet short video sharing: Youtube as a case study. In CoRR abs, Jul 2007
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, 2007
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, 2005
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, 2007
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, 2008
J. Heer and D. Boyd. Vizster: Visualizing online social networks. IEEE Symposium on Information Visualization, 2005
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, 2008
P. P. Kotsiantis S., Kanellopoulos D. Multimedia mining. In WSEAS Trans. Syst., 3(10): 3263–3268, 2004
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, 2009
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, 2008
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, 2009
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, 2009
C. G. R. A. A. F. L. Rodrygo L. T. Santos, Bruno P. S. Rocha. Characterizing the youtube video-sharing community. 2007
M. F. Schwartz and D. C. M. Wood. Discovering shared interests using graph analysis. Commun. ACM, 36(8):78–89, 1993
A. S. Sharma and M. Elidrisi. Classification of multi-media content (video’s on youtube) using tags and focal points. Unpublished manuscript
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, 2003
S. Tsekeridou and I. Pitas. Content-based video parsing and indexing based on audio-visual interaction, 2001
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, 2003
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, 2007
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, 1998
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, 2009
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Leung, J.KW., Li, C.H. (2010). Concept Discovery in Youtube.com Using Factorization Method. In: Furht, B. (eds) Handbook of Social Network Technologies and Applications. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7142-5_13
Download citation
DOI: https://doi.org/10.1007/978-1-4419-7142-5_13
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-7141-8
Online ISBN: 978-1-4419-7142-5
eBook Packages: Computer ScienceComputer Science (R0)