DOI QR코드

DOI QR Code

Movie Popularity Classification Based on Support Vector Machine Combined with Social Network Analysis

  • Dorjmaa, Tserendulam (Department of Management Information Systems, Yonsei University) ;
  • Shin, Taeksoo (Management Information Systems at Division of Business Administration, College of Government and Business, Yonsei University Wonju Campus)
  • Received : 2016.01.30
  • Accepted : 2017.07.07
  • Published : 2017.09.30

Abstract

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.

Keywords

References

  1. Amatriain, X. and J. Basilico, "Netflix Recommendations : Beyond the 5 stars (Part 1)", The Netflix Tech Blog, 2012, Available at https://medium.com/netflix-techblog/netflix-recommendations-beyond-the-5-stars-part-1-55838468f429 (Accessed April 20. 2017).
  2. Asad, K.I., T. Ahmed, and M.S. Rahman, "Movie Popularity Classification Based on Inherent Movie Attributes Using C4.5, PART and Correlation Coefficient", International Conference on Informatics, Electronics and Vision (ICIEV2012), 2012, 747-752.
  3. Bao, Z. and H. Xia, "Movie Rating Estimation and Recommendation", CS229 2012 Project, Stanford University.
  4. Bhave, A., H. Kulkarni., V. Biramane, and P. Kosamkar, "Role of Different Factors in Predicting Movie Success", International Conference On Pervasive Computing, India, 2015, 911-915.
  5. Chandra, B. and M. Gupta, "Robust Approach For Estimating Probabilisties In Niave Bayesian Classifier for Gene Expression Data", Expert Systems with Applications, Vol.38, No.3, 2011, 1293-1298. https://doi.org/10.1016/j.eswa.2010.06.076
  6. Chaovalit, P. and L. Zhou, "Movie Review Mining : A Comparison between Supervised and Unsupervised Classification Approaches", Proceeding of the 38th Hawaii International Conference on System Sciences(HICSS 2005), 2005, 1-9.
  7. Christakou, C. and A. Stafylopatis, "A Hybrid Movie Recommender System Based on Neural Networks", In Proceedings of the 2005, 5th International Conference on Intelligent Systems Design and Applications, 2005, 500-505.
  8. Fan, L., K.L. Poh, and P. Zhou, "Partition-Conditional ICA for Bayesian Classification of Micro Array Data", Expert Systems with Applications, Vol.37, 2010, 8188-8192. https://doi.org/10.1016/j.eswa.2010.05.068
  9. Farid, D.M., L. Zhang, M.C. Rahman, M.A. Hossain, and R. Strachan, "Hybrid Decision Tree and Naïve Bayes Classifier for Multi-Class Classification Tasks", Expert Systems with Applications, Vol.41, No.4, 2014, 1937-1946. https://doi.org/10.1016/j.eswa.2013.08.089
  10. Frank, O., "Using Centrality Modeling in Network Surveys", Social Networks, Vol.24, No.4, 2002, 385-394. https://doi.org/10.1016/S0378-8733(02)00014-X
  11. Freeman, L.C., "Centrality in Social Networks : Conceptual Clarification", Social Networks, Vol.1, No.3, 1979, 215-239. https://doi.org/10.1016/0378-8733(78)90021-7
  12. Friedman, N., D. Geiger, and M. Goldszmidt, "Bayesian Network Classifiers", Machine Learning, Vol.29, No.2-3, 1997, 131-163. https://doi.org/10.1023/A:1007465528199
  13. Ghazanfar, M.A. and A. Prügel-Bennett, "Leveraging Clustering Approaches to Solve the Grey-Sheep Users Problem in Recommender Systems", Expert Systems with Applications, Vol.41, No.7, 2014, 3261-3275. https://doi.org/10.1016/j.eswa.2013.11.010
  14. Girvan, M. and M.E. Newman, "Community Structure in Social and Biological Networks", Proceedings of the National Academy of Sciences of the United States of America, Vol.99, No.12, 2002, 7821-7826. https://doi.org/10.1073/pnas.122653799
  15. Guyon, I., J. Weston, S. Barnhill, and V. Vapnik, "Gene Selection for Cancer Classification Using Support Vector Machines", Machine Learning, Vol.46, No.1-3, 2002, 389-422. https://doi.org/10.1023/A:1012487302797
  16. Huang, W., Y. Nakamori, and S.Y. Wang, "Forecasting Stock Market Movement Direction with Support Vector Machine", Computer and Operations Research, Vol.32, No.10, 2005, 2513-2522. https://doi.org/10.1016/j.cor.2004.03.016
  17. Hsu, C.C., Y.P. Huang, and K.W. Chang, "Extended Naïve Bayes Classifier for Mixed Data", Expert Systems with Applications, Vol.35, No.3, 2008, 1080-1083. https://doi.org/10.1016/j.eswa.2007.08.031
  18. Hsu, C.W., C.C. Chang, and C.J. Lin, "Practical Guide to Support Vector Classification", Department of Computer Science National University, Taipei 106, Taiwan, 2010.
  19. Kabinsingha, S., S. Chindasorn, and C. Chantrapornchai, "A Movie Rating Approach and Application Based on Data Mining", International Journal of Engineering and Innovative Technology (IJEIT), Vol.2, No.1, 2012, 77-83.
  20. Kim, M. and I. Im, "Resolving the Gray Sheep Problem Using Social Network Analysis (SNA) in Collaborative Filtering (CF) Recommender Systems", Journal of Intelligent Information Systems, Vol.20, No.2, 2014, 137-148. https://doi.org/10.13088/jiis.2014.20.2.137
  21. Koc, L., T.A. Mazzuchi, and S. Sarkani, "A Network Instruction Detection System Based on Hidden Naïve Bayes Multiclass Classifier", Expert Systems with Applications, Vol.39, No.18, 2012, 13492-13500. https://doi.org/10.1016/j.eswa.2012.07.009
  22. Kossinets, G. and D.J. Watts, "Origins of Homophily in an Evolving Social Network", American Journal of Sociology, Vol.115, No.2, 2009, 405-500. https://doi.org/10.1086/599247
  23. Lash, M., S. Fu., S. Wang, and K. Zhao, "Early Prediction of Movie Success-What, Who and When", LNCS 9021, Springer International Publishing Switzerland, 2015, 345-349.
  24. Lee, H.S. and J.H. Kwon, "Similar User Clustering Based on Movielens Data Set", Advanced Science and Technology Letters, Vol.51, No.8, 2014, 32-35.
  25. Leem, B. and H. Chun, "An Impact of Online Recommendation Network on Demand", Expert Systems with Applications, Vol.41, No.4, 2014, 1723-1729. https://doi.org/10.1016/j.eswa.2013.08.071
  26. Lin, A.J., C.L. Hsu, and B.Y. Li, "Improving the Effectiveness of Experiential Decisions by Recommendation Systems", Expert Systems with Applications, Vol.41, No.10, 2014, 4904-4914. https://doi.org/10.1016/j.eswa.2014.01.035
  27. Ma, L., R. Krishnan, and A.L. Montgomery, "Latent Homophily or Social Influence? An Empirical Analysis of Purchase within a Social Network", Management Science, Vol. 61, No.2, 2015, 454-473. https://doi.org/10.1287/mnsc.2014.1928
  28. Manek, A.S., R.P. Pallavi, H.V. Bhat, P.D. Shenoy, M.C. Mohan, K.R. Venugopal, and L.M. Patnaik, "SentReP : Sentiment Classification of Movie Reviews Using Efficient Repetitive Pre-Processing", TENCON 2013, IEEE Region 10 Conference, 2013, 348-353.
  29. Oh, Y.J. and S.H. Chae, "Movie Rating Inference by Construction of Movie Sentiment Sentence using Movie Comments and Rating", Journal of Internet Computing and Services, Vol.16, No.2, 2015, 41-48. https://doi.org/10.7472/jksii.2015.16.2.41
  30. Park, S.H., S.Y. Huh, W. Oh, and S.P. Han, "A Social Network-Based Inference Model For Validating Customer Profile Data", MIS Quarterly, Vol.36, No.4, 2012, 1217-1237.
  31. Parvin, H., M. MirnabiBaboli, and H.A. Rokny, "Proposing a Classifier Ensemble Framework based on Classifier Selection", Engineering Application of Artificial Intelligence, Vol.37, 2015, 34-42. https://doi.org/10.1016/j.engappai.2014.08.005
  32. Santos, E.E., L. Pan, D. Arendt, and M. Pittkin, "An Effective Anytime Anywhere Parallel Approach for Centrality Measurement in Social Network Analysis", IEEE International Conference on Systems, Man, and Cybernetics, Taipei, Taiwan, Vol.6, 2006, 4693-4698.
  33. Symeonidis, P., A. Nanopoulos, and Y. Manopoulos, "Moviexplain : A Recommender System with Explanations", In Proceedings of the 2009 ACM Conference on Recommender Systems, 2009, 317-320.
  34. Tiwari, A. and A. Prakash, "Improving Classification of J48 Algorithm Using Bagging, Boosting and Blending Ensemble Methods on SONAR Dataset Using Weka", International Journal of Engineering and Technical Research, Vol.2, 2014, 207-209.
  35. Wu, T., P. Karsmakers, H. Vanhamme, and D.V. Compernolle, "Comparison of Variable Selection Methods and Classifiers for Native Accent Identification", 9th Annual Conference of the International Speech Communication Association, 2008, 305-308.
  36. Xu, Y., J. Ma, Y. Sun, J. Hao, Y. Sun, and D. Zhao, "Using Social Network Analysis as a Strategy for E-Commerce Recommendation", Pacific Asia Conference on Information Systems (PASIC), India, 2009.
  37. Zafarani, R., M.A. Abbasi, and H. Liu, Social Media Mining : An Introduction, Cambridge University Press, 2014.