{"references": ["I. H. Witten, Text Mining, Practical Handbook of Internet Computing,\nCRC Press, 2004.", "J. Hong, H. Choi, H. Han, J. Kim, E. Yu, S. Lim, and N. Kim, \"A Data\nAnalysis-based Hybrid Methodology for Selecting Pending National\nIssue Keywords,\" Entrue Journal of Information Technology, vol. 13, pp.\n97-111, Jun. 2014.", "R. J. Mooney, and R. Bunescu, \"Mining Knowledge from Text Using\nInformation Extraction,\" ACM SIGKDD Explorations, vol. 7, pp. 3-10,\nJun. 2006.", "S. Song, J. Yu, and E. Kim, \"Offering System for Major Article Using\nText Mining and Data Mining,\" Proceedings of the 32th annual\nconference on Korea Information Processing Society, pp. 733-734, 2009.", "E. Yu, J. Kim, C. Lee, and N. Kim, \"Using Ontologies for Semantic Text\nMining,\" The Journal of Information Systems, vol. 21, pp. 137-161, Sep.\n2012.", "D. Metzler, Y. Bernstein, W. B. Croft, A. Moffat, and J. Zobel,\n\"Similarity Measures for Tracking Information Flow,\" Proceedings of\nCIKM, Bremen, Germany, 2005.", "C. J. V. Rijsbergen, Information Retrieval, 2nd edition, Butterworth,\n1979.", "F. Sebastiani, Classification of Text, Automatic, The Encyclopedia of\nLanguage and Linguistics 14, 2nd edition, Elsevier Science Pub, 2006.", "W. Fan, L. Wallace, S. Rich, and Z. Zhang, \"Tapping the Power of Text\nMining,\" Communications of the ACM, vol. 49, pp. 76-82, Sep. 2006.\n[10] S. M. Weiss, N. Indurkhya, and T. Zhang, Fundamentals of Predictive\nText Mining, Springer, 2010.\n[11] G. Salton, A. Wong, and C. S. Yang, \"A Vector Space Model for\nAutomatic Indexing,\" Communications of the ACM, vol. 18, pp. 613-620,\nNov. 1975.\n[12] R. Albright, Taming Text with the SVD, SAS Institute Inc., 2006.\n[13] G. Salton, and M. J. McGill, Introduction to Modern Information\nRetrieval, McGraw Hill, 1983."]}

Recently, many users have begun to frequently share their opinions on diverse issues using various social media. Therefore, numerous governments have attempted to establish or improve national policies according to the public opinions captured from various social media. In this paper, we indicate several limitations of the traditional approaches to analyze public opinion on science and technology and provide an alternative methodology to overcome these limitations. First, we distinguish between the science and technology analysis phase and the social issue analysis phase to reflect the fact that public opinion can be formed only when a certain science and technology is applied to a specific social issue. Next, we successively apply a start list and a stop list to acquire clarified and interesting results. Finally, to identify the most appropriate documents that fit with a given subject, we develop a new logical filter concept that consists of not only mere keywords but also a logical relationship among the keywords. This study then analyzes the possibilities for the practical use of the proposed methodology thorough its application to discover core issues and public opinions from 1,700,886 documents comprising SNS, blogs, news, and discussions.