A Study on the Decision-Making of Effective S/W Education based on Opinion Mining Analysis

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    The Currently, along with the advent of the web 2.0 era, due to the continuous expansion of social media service infrastructures, the shares of conventional public opinion evaluation functions have been gradually shifting from the existing mass media to social media. This phenomenon is attributable to the two-way communication and convenience unique to social media and social media are now in charge of an axis of public opinion evaluation standards. In particular, since diverse interests conflict in education policies and countless conflicts of opinions occur in the process of setting up policy agendas, in establishing education policies, accurately analyzing reputations among the public, who are the targets of education policies, in order to set up effective policy agendas, is the most important issue. Therefore, in this study, the resultant values of huge unstructured data on the positive and negative reputations of past policy agendas related to the mandatory software education that has been organized as a regular curriculum of middle/high schools from 2018 in Korea, which have been addressed by the Ministry of Education, the Ministry of Science, ICT and Future Planning, and the Korea Foundation for the Advancement of Science and Creativity, felt and judged by the general public on social media such as blogs and Twitter and on online media including  portal news were visualized through opinion mining analysis techniques to derive more effective software education related policy agendas. In addition, based on the foregoing, a Korean style software education system that fits circumstances was constructed and the system is expected to become an important measure that provides guidelines for setting mid/long-term road maps for the fostering of creative and convergent talented persons equipped with international competitiveness and software education in Korea.



  • Keywords

    S/W Education Policy, Opinion Mining, Social Media, Unstructured Data

  • References

      [1] Sun, Y. and K. Jia. 2009. Research of word sense disambiguation based on mining association rules, In: Third International Symposium on Intelligent Information Technology Application workshops, November 21-22, NanChang, China, pp. 86-88.B. Sklar, Digital Communications, Prentice Hall, pp. 187, 1998.J. Breckling, Ed., The Analysis of Directional Time Series: Applications to Wind Speed and Direction, ser. Lecture Notes in Statistics. Berlin, Germany: Springer, 1989, vol. 61.

      [2] Xindong Wu, Xingquan Zhu, Gong-Qing Wu and Wei Ding, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, "Data Mining with Big Data", Vol.26, No.1, pp. 97-107, 2014, January.)

      [3] Tang, C. and C. Liu.2008. Method of Chinese grammar rules automatically access based on association rules, In: Proceedings of the. Computer Science and Computational Technology volume, 1 pp. 265-268 (ISCSCT, Shanghai, Dec. 20-22, 2008).

      [4] Irfan Ajmal Khan, Jin Tak Choi, International Journal of Software Engineering and Its Applications, “An Application of Educational Data Mining (EDM) Technique for Scholarship Prediction” , Vol. 8, No. 12 (2014), pp. 31-42

      [5] Xu, Yue, Li, Yuefeng, & Shaw, Gavin, Reliable representations for association rules. Data & Knowledge Engineering, Volume 70 Issue 6, pp. 555-575. June, 2011.

      [6] Bo pang, Lillian Lee and Shivakumar Vaithyanathan, 2002, “Thumbs up?: sentiment classification using machine learning techniques”, Proceedings of the ACL-02 Conference on Empirical methods in Natural Language Processing, Vol.10, pp.79-86

      [7] Seo Ji Hoon, “Design of Opinion Sensitivity Dictionary Model for Big Data Management”, 2015.

      [8] Khan, I.A. and J.T. Choi. 2015. An application of educational data mining (EDM) technique for scholarship prediction. International Journal of Software Engineering and its Applications, Vol.8 No.12 [2014], pp 31-42.

      [9] Ghose, P. G. Ipeirotis and A. Sundararajan, 2007, "Opinion Mining Using Econometrics: A Case Study on Reputation System" Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, Prague, Czech Republic, pp.416-423.

      [10] Pang and L. Lee, 2008, "Opinion Mining and Sentiment Analysis", Foundation and Trends in Information Retrieval, 2(1-2), pp.1-135.

      [11] S. Shin, Read Emotions in the Article! Understanding Emotional Analysis, IDG Korea, pp. 1-11, 2014.

      [12] E. Courses and T, Surveys, (2008), “Using Sentiment SentiWordNet for multilingual sentiment analysis”, IEEE 24th International Conference on Data Engineering Workshop (2008), Cancun, Mexico, pp.507-512




Article ID: 21769
DOI: 10.14419/ijet.v7i4.16.21769

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.