International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Data Mining Method for Video Subscribers and Analysis of Youtube

Abstract

With a huge collection of videos attracting billions of views from users each month, the resulting data generated by YouTube is enormous. Data like view count, like count, dislike count, user comments, etc are all valuable data that can be extracted and analyzed to uncover insights about user preferences and sentiment towards a particular video or a particular cause e.g. the Ice Bucket Challenge, sometimes called the ALS Ice Bucket Challenge that went viral on YouTube few years ago. It also presents valuable information to marketers in their decision-making process of promoting a particular product or service. A fun example would be a movie studio, having uploaded a new movie trailer on their YouTube channel and would like to know about viewers' response towards the upcoming movie. Statistics on the movie trailer such as view count, like count and user comments can help marketers to gauge the market response to the movie and allocate their marketing budget accordingly. This is the primary motivation behind this project. In this project, I have extracted and analyzed some interesting statistics about popular superhero movies from both Disney/Marvel and Warner Bros/DC such as Infinity War, Justice League, Black Panther, Wonder Woman and Aquaman.

Country : India

1 Nadhiya S2 S. Dinesh Kumar3 Dr. N. Sudha4 Dr. G. Chitra Ganapathy

  1. PG Student, Department of Computer Science and Engineering, CMS College of Engineering, Tamilnadu, India
  2. Professor, Department of Computer Science and Engineering, CMS College of Engineering, Tamilnadu, India
  3. Principal, CMS College of Engineering, Tamilnadu, India
  4. HoD, Department of Computer Science and Engineering, CMS College of Engineering, Tamilnadu, India

IRJIET, Volume 8, Issue 2, February 2024 pp. 123-127

doi.org/10.47001/IRJIET/2024.802017

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References

  1. Bhattacharyya, A.: On a measure of divergence between two statistical populations defined by their probability distribution. Bull. Calcutta Math. Soc. 35, 99–109 (1943).
  2. Bonacich, P.: Factoring and weighting approach to status scores and clique identification. J. Math. Soc. 2, 112–120 (1972).
  3. Borgatti, S.P., Halgin, D.S.: Analyzing Affiliation Networks, pp. 417–433. Sage (2011). Brieger, R.L.: The duality of persons and groups. Soc. Forces 53(2), 181–190 (1974).
  4. Han, J., Kamber, M.: Data Mining Concepts and Techniques (2001).
  5. Chen, N., Lin, J., Hoi, S.C., Xiao, X., Zhang, B.: Ar-miner: mining informative reviews for developers from mobile app marketplace. In: Proceedings of the 36th International Conference on Software Engineering, pp. 767–778. ACM (2014).
  6. Cha, M., Kwak, H., Rodriguez, P., Ahn, Y.-Y., Moon, S.: Analyzing the video popularity characteristics of large-scale user-generated content systems. IEEE/ACM Trans. Netw. (2009).
  7. Brodersen, A., Scellato, S., Wattenhofer, M.: Youtube around the world: geographic popularity of videos. In: Proceedings of the 21st International Conference on world Wide Web. ACM (2012).
  8. De Pauw, W., Jensen, E., Mitchell, N., Sevitsky, G., Vlissides, J., Yang, J.: Visualizing the execution of Java programs. In Software Visualization, pp. 151–162. Springer (2002).
  9. Ghorashi, S.H., Ibrahim, R., Noekhah, S., Dastjerdi, N.S.: A frequent pattern mining algorithm for feature extraction of customer reviews. Int. J. Comput. Sci. Issues (IJCSI), 29–35 (2012).
  10. Bala, S., et al.: Int. J. Comput. Sci. Mob. Comput. 3(7), 960–967 (2014).
  11. Rui, Lau & Afif, Zehan&Saedudin, Rd & Mustapha, Aida & Razali, Nazim. (2019). A regression approach for prediction of Youtube views. Bulletin of Electrical Engineering and Informatics. 8. 10.11591/eei.v8i4.1630.
  12. Pinto, Henrique & Almeida, Jussara & Gonçalves, Marcos. (2013). Using early view patterns to predict the popularity of YouTube videos. WSDM 2013 - Proceedings of the 6th ACM International Conference on Web Search and Data Mining 10.1145/2433396.2433443.
  13. Bärtl M. YouTube channels, uploads and views: A statistical analysis of the past 10 years. Convergence. 2018;24(1):16-32. doi:10.1177/1354856517736979.
  14. Kousha, Kayvan &Thelwall, Mike & Abdoli, Mahshid. (2012). The role of online videos in research communication: A content analysis of YouTube videos cited in academic publications. Journal of the American Society for Information Science and Technology. 63. 1710-1727. 10.1002/asi.22717.
  15. Borghol, Y, Ardon, S, Carlsson, N. (2012) The Untold Story of the Clones: Content-Agnostic Factors that Impact Youtube Video Popularity. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, China, 12–16 August 2012, pp 1186–1194. New York: ACM.
  16. D.Nethra Pingala Suthishni, A Review on Fuzzy based packet dropping and collaborative attack detection in MANET using DSR protocol International Conference on ‘Social Mobile Analytics Cloud (SMAC’ 18)’ organized by the Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore and published in International Journal VOLUME 10.
  17. D Nethra Pingala Suthishni, KS Senthil Kumar, Fuzzy Logic Based Intrusion Detection System Using Nsl-Kdd Data Set.
  18. S. Gnanapriya, Genetic Algorithm Based Ant Colony Optimization (GA-ACO) For Cross Domain Opinion Mining, ARPN Journal of Engineering and Applied Sciences, VOL. 14, NO. 7, April 2019, ISSN 1819-6608.

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