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http://dx.doi.org/10.14352/jkaie.2017.21.1.89

Sentimental Analysis of SW Education News Data  

Park, SunJu (Dept. of Computer Science Education, Gwangju National University of Education)
Publication Information
Journal of The Korean Association of Information Education / v.21, no.1, 2017 , pp. 89-96 More about this Journal
Abstract
Recently, a number of researches actively focus on the contents and sensitivity of information distributed through SNS as smartphones and SNS gained its popularity. In this paper, we collected online news data about SW education, extracted words after morphological analysis, and analyzed emotions of collected news data by calculating sentimental score of each news datum. Also, the accuracy of the calculated sentimental score was examined. As a result, the number of news related to 'SW education' in the collection period was about 189 per month, and the average of sentimental score was 0.7, which signifies the news related to 'SW education' was emotionally positive. We were positive about the importance of SW education and the policy implementation, but there were negative views on the specific method for the realization. That is, a lack of SW education environment and its education method, a problem related to improvement of SW developers and improvement of their labor conditions, and increase of private education in coding were the factors for the negative viewers.
Keywords
Big data; Text analysis; Sentimental analysis; R. SW education;
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Times Cited By KSCI : 6  (Citation Analysis)
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1 An, Jungkook, Kim, HeeWoong(2015), Building a Korean Sentiment Lexicon Using Collective Intelligence, Journal of Intelligence and Information Systems, 21(2), 49-67.   DOI
2 Bollen, J., A. Pepe, and H. Mao(2009), "Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena," arXiv preprintarXiv: 0911.1583.
3 Cha, EunJeong, Hong, TaeHo(2016), Stock Index Prediction Using SVM and News Sentimental Analysis, Proceedings of the Korean Society of Management Information Systems Conference, 2016(6).
4 Cho, S. Y., Kim, H. K., Kim, B. and Kim, H. W.(2014), "Predicting Movie Revenue by Online Review Mining: Using the Opening Week Online Review," Information Systems Review, 16(3), 111-132.
5 Choi Sukjae, Lee Jaewoong, Kwon Ohbyung(2015), A Morphological Analysis Method of Predicting Place-Event Performance by Online News Titles, The Jounal of Society for e-Business Studies, 21(1), 15-32.
6 Jang, J.-Y.(2009), "A Sentiment Analysis Algorithm for Automatic Product Reviews Classification in On-Line Shopping Mall," The Journal of Society for e-Business Studies, 14(4), 19-33.
7 Jin W., H. H. Ho and R. K. Srihari(2009),, OpinionMiner: A Novel Machine Learning System for Web Opinion Mining and Extraction, KDD Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining 1195-1204.
8 Jo H. J., Seo, J. H., and Choi, J. T.(2015), OAR Algorithm Technology Based on Opinion Mining Utilizing Stock News Contents, Journal of Korean Institute of Information Technology, 13(2), 111-119.
9 Khan, F. H., S. Bashir, and U. Qamar(2014), "TOM:Twitter opinion mining framework using hybrid classification scheme," Decision Support Systems, 57, 245-257.   DOI
10 Jwa, BoKyung, Paek, HyeJin, Seol, Pil Kyo(2014), A Content Analysis of Online News and Comments about Anti-smoking Policy, Journal of Public Relations, 18(3).
11 Kim J. H., Oh, Y. J. and Chae, S. H.(2015), The Construction of a Domain-Specific Sentiment Dictionary Using Graph-based Semisupervised Learning Method, Korean Journal of the Science of Emotion and Sensibility, 18(4), 97-104.
12 Kim, Jungho, Chae, Soohoan(2014), Automatic Construction of Korean Polarity Dictionary using Graph-based Semi-supervised Learning, Proceedings of the Korean Society for Internet Information Conference, 2014(5).
13 Pang, B., and L. Lee(2008), Opinion mining and sentiment analysis, Foundations and trends in information retrieval, 2(1-2), 1-135.   DOI
14 Kim, Yoosin, Kim, Namgyu, Jeong, SeongRyoul(2012), Stock-Index Invest Model Using News Big Data Opinion Mining, Journal of Intelligence and Information Systems, 18(2).
15 Lee, SangHoon, Choi, Jung, Kim, JongWoo(2016), Sentiment analysis on movie review through building modified sentiment dictionary, Journal of Intelligence and Information Systems 22(2), 97-113.   DOI
16 Moon, Kwangsu, Kim, Seul, Oah, Shezeen(2013). An Effect of the Valence of Best Reply on the Conformity of General Reply, Journal of the Korea Contents Association, 13(12), 201-211.   DOI
17 Turney P. D. and M.L. Littman(2002), Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus, National Research Council, Institute for Information Technology, Technical Report, ERB-1094.
18 Park, SungGeon, Won, GyuSik, Lee, SooWon(2015), Web News Comment-based Sentiment Analysis of the South Korean National Team Members in the 2014 Brazil World Cup, Korean Journal of Sport Management, 20(2).
19 Song S. I., Lee, D. J. and Lee, S. G.(2010), Identifying Sentiment Polarity of Korean Vocabulary Using PMI, Proceedings of the Korean Information Science Society Conference, 37(1), 260-265.
20 Sung, JunMo(2015), A study on the convergence of SNS and storytelling emotional marketing, Master's dissertation, Graduate school in Hanyang University.
21 Yu E. J., Kim, Y. S., Kim, N. Y. and Jeong, S. R.(2013), Predicting the Direction of the Stock Index by Using a Domain-Specific Sentiment Dictionary, Journal of Intelligent Information Systems, 19(1), 95-10.   DOI