Utilize Novel Algorithms to Acquire, Analyze, and Extract Data from TikTok Discover Page and Education-Related Topics
Article Sidebar
Main Article Content
Abstract
Due to the swift advancement of research and technology, particularly in the fields of computer science and data science, individuals are progressively employing these technologies, along with others, in the realm of education. This study encompasses the development, creation, and utilization of a comprehensive range of techniques, spanning from data collecting to data analysis and mining. It introduces a novel algorithm and methodology for acquiring and refining data, as well as three innovative algorithms for data analysis and exploration. This project collects data on the topics featured on the TikTok Discover page for the purpose of doing data analysis and data mining. The research methodologies employed in this work encompass empirical research, experimental verification, algorithm design and optimization, system design, and implementation. Our study examined and extracted educational content from TikTok Discover pages. We studied the popularity of this data from various perspectives and levels. This allows users to easily and efficiently locate the specific information they are interested in for further investigation. Analysis, sentiment analysis, and potential anomalous data were discovered. The analysis and extraction of this data offer educational practitioners’ significant insights that can be utilized to inform and direct educational practice.
Article Details
References
R. S. J. D. Baker and K. Yacef. "The state of educational data mining in 2009: A review and future visions." Journal of Educational Data Mining, Vol. 1, No. 1, pp. 3-17, 2009.
G. Siemens and R. S. J. d. Baker. "Learning analytics and educational data mining: towards communication and collaboration." Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, Vancouver, Canada, pp. 252-254, 2012.
R. S. J. d. Baker. "Data mining for education." In: McGaw, B., Baker, E., Peterson, P. (eds.) International Encyclopedia of Education, 3rd edn., Vol. 7, Elsevier, Oxford, pp. 112-118, 2010.
P. Baepler and C. J. Murdoch. "Academic analytics and data mining in higher education." International Journal for the Scholarship of Teaching & Learning, Vol. 4, No. 2, 2010.
R. S.Baker, A. T. Corbett, and K. R. Koedinger. "Detecting student misuse of intelligent tutoring systems. P" roceedings of 7th International Conference (ITS2004), Maceió, Alagoas, Brazil, pp. 531-540, 2004.
P. Long and G. Siemens. "Penetrating the Fog: Analytics in Learning and Education." EDUCAUSE Review, Vol. 46, No. 5, pp. 30-40, 2011.
K. E.Arnold and M. D. Pistilli. "Course Signals at Purdue: Using Learning Analytics to Increase Student Success." Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 267-270, 2012.
D. Gašević, S. Dawson, and G. Siemens. "Let's Not Forget: Learning Analytics Are About Learning." TechTrends, Vol. 59, No. 1, pp. 64-71, 2015.
C. Silverstein, H. Marais, M. Henzinger, and M. Moricz. "Analysis of a Very Large Web Search Engine Query Log." ACM SIGIR Forum, Vol. 33, No. 1, pp. 6-12, 1999.
R. Jones and K. L. Klinkner. "Beyond the Session Timeout: Automatic Hierarchical Segmentation of Search Topics in Query Logs." Proceedings of the 17th ACM conference on Information and knowledge management, pp. 699-708, 2008.
T. Y. Liu. "Learning to Rank for Information Retrieval." Foundations and Trends® in Information Retrieval, Vol. 3, No. 3, pp. 225-331, 2009.
S. Brin and L. Page. "The Anatomy of a Large-Scale Hypertextual Web Search Engine." Computer Networks and ISDN Systems, Vol. 30, pp. 107-117, 1998.
S. Bird, E. Klein, and E. Loper. Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O'Reilly Media, Inc., 2009.
J. Han, M. Kamber, and J. Pei. Data Mining: Concepts and Techniques. Third Edition, Morgan Kaufmann Publishers is an imprint of Elsevier, 2011.
H. Chen, R. H. Chiang, and V. C. Storey. "Business intelligence and analytics: From big data to big impact." MIS Quarterly, Vol. 36, No. 4, pp. 1165-1188, 2012.
U. Fayyad, G. P. Shapiro, and P. Smyth. "From data mining to knowledge discovery in databases." AI Magazine, Vol. 17, No. 3, pp. 37-54, 1996.
E. Brynjolfsson and A. McAfee. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company, New York, London, 2014.
K. S. Jones. "A statistical interpretation of term specificity and its application in retrieval." Journal of Documentation, Vol. 28, No. 1, pp. 11-21, 1972.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin. "Attention is All You Need." Advances in Neural Information Processing Systems, pp. 5998-6008, 2017.
T. Bayes. "An essay towards solving a problem in the doctrine of chances." Biometrika, Vol. 45, No. 3-4, pp. 296-315, 1958.
D. M. Blei, A. Y. Ng, and M. I. Jordan. "Latent Dirichlet Allocation." Journal of Machine Learning Research, Vol. 3, pp. 993-1022, 2003.
M. Hoffman, F. Bach, and D. Blei. "Online learning for latent dirichlet allocation." Advances in Neural Information Processing Systems, Vol. 23, pp. 1-9, 2010.
J. MacQueen. "Some methods for classification and analysis of multivariate observations." Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, California, USA: University of California Press, pp. 281-297, 1967.
L. v. d. Maaten and G. Hinton. "Visualizing Data using t-SNE." Journal of Machine Learning Research, Vol. 9, pp. 2579-2605, November, 2008.
W. Xue and T. Li. "Aspect Based Sentiment Analysis with Gated Convolutional Networks." Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, pp. 2514-2523, 2018.
H. E. Williams, J. Zobel, and D. Bahle. "Fast Phrase Querying With Combined Indexes." ACM Transactions on Information Systems, Vol. 22, No. 4, pp. 573-594, 2004. doi.org/10.1145/1028099.1028102.
V. Chandola, A. Banerjee, and V. Kumar. "Anomaly detection: A survey." ACM Computing Surveys (CSUR), Vol. 41, No. 3, pp. 1-58, 2009.
I. Goodfellow, J. P. Abadie, M. Mirza, B. Xu, D. W. Farley, S. Ozair, A. Courville, and Y. Bengio. "Generative adversarial nets." Advances in Neural Information Processing Systems, Vol. 27, 2014.
S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman. "Indexing by latent semantic analysis." Journal of the American Society for Information Science, Vol. 41, No. 6, pp. 391-407, 1990.
C. D. Manning, P. Raghavan, and H. Schütze. An Introduction to Information Retrieval. Cambridge University Press, Cambridge, 2009.
