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Journal of Educational Innovation Research 2018, Vol. 28, No. 4, pp.461-487 DOI: http://dx.doi.org/10.21024/pnuedi.28.4.201812.461 * - 2008 2018 - A Study on the Change of Issues with Adolescent Problem by Using Text Mining - The Internet News Articles for the Years 2008 to 2018 - Purpose: The purpose of this study was to identify issues with adolescent problem, as well as the trend on such issues based on internet articles. Method: The data was collected from Daily Chosun(chosun.com), Dong-A Daily(donga.com), and JoongAng Daily(news.joins.com) which were named as Korea s top 3 news media in 2018 and 8,110 articles from 2008 to march 2018 on the issues of adolescent problems were analyzed. For collecting data and topic analysis, Python 3.6 program and Latent Dirichlet Allocation(LDA) were utilized. Results: Firstly, between 2008 and 2012, the main issues were 'literature contents', education policy, popular culture contents, and 'career counselling programs'. This was a period when the commercialization of cultural contents was accelerating, leading to appearance of entertainment topic, and career problems emerged with cultural keywords. Secondly, between 2013 and 2018, the main issues were 'juvenile sex crime, communication between parents and children, social contribution activities and so on. As aspect of this period, topics were evenly distributed whilst social issues and adolescent issues moved in the same trend. As well, adolescent issues were diversified compared to the past and each problems were being dealt with similar proportion. Conclusion: This research focused on identifying which topics were covered in news articles and understand that the change in social-cultural environment and education policies have to be considered in identifying adolescent issues. Key words : adolescent problem, adolescent issue, textmining, big data, internet news article * 1 2018. Corresponding Author: Kim, Hyun-Sook. Silla University, Dept. of Education, Baegyang-daero 700, Sasang-gu, Busan, Korea, e-mail: khs@silla.ac.kr

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lda.collapsed.gibbs.sampler(documents = ldaform$documents, K = 17, # vocab = ldaform$vocab, # vocabulary num.iterations = 5000, # update burnin = 1000, # alpha = 0.01, # eta = 0.01) # 20, 4. 1 2 3 4 45 48 40 44 perplexity 3 331.99 13.27 4 318.72 14.17 5 304.54 7.96 6 296.59 8.43 7 288.16 6.34 8 281.82 3.85 9 277.98 3.79 10 274.19 4.57 11 269.61 3.00 12 266.62 1.94 13 264.68 1.76 14 262.92 2.33 15 260.59 2.29 16 258.30 0.76 17 257.54 0.24 18 257.30 1.75 19 255.56 1.39 20 254.17 1.60 21 252.57 1.92 22 250.65 0.17 23 250.48 1.20 24 249.27 2.25 25 247.02

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