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Keyword Analysis of Arboretums and Botanical Gardens Using Social Big Data

  • Received : 2020.02.05
  • Accepted : 2020.03.05
  • Published : 2020.04.30

Abstract

This study collects social big data used in various fields in the past 9 years and explains the patterns of major keywords of the arboretums and botanical gardens to use as the basic data to establish operational strategies for future arboretums and botanical gardens. A total of 6,245,278 cases of data were collected: 4,250,583 from blogs (68.1%), 1,843,677 from online cafes (29.5%), and 151,018 from knowledge search engine (2.4%). As a result of refining valid data, 1,223,162 cases were selected for analysis. We came up with keywords through big data, and used big data program Textom to derive keywords of arboretums and botanical gardens using text mining analysis. As a result, we identified keywords such as 'travel', 'picnic', 'children', 'festival', 'experience', 'Garden of Morning Calm', 'program', 'recreation forest', 'healing', and 'museum'. As a result of keyword analysis, we found that keywords such as 'healing', 'tree', 'experience', 'garden', and 'Garden of Morning Calm' received high public interest. We conducted word cloud analysis by extracting keywords with high frequency in total 6,245,278 titles on social media. The results showed that arboretums and botanical gardens were perceived as spaces for relaxation and leisure such as 'travel', 'picnic' and 'recreation', and that people had high interest in educational aspects with keywords such as 'experience' and 'field trip'. The demand for rest and leisure space, education, and things to see and enjoy in arboretums and botanical gardens increased than in the past. Therefore, there must be differentiation and specialization strategies such as plant collection strategies, exhibition planning and programs in establishing future operation strategies.

Keywords

References

  1. Chang, J.Y. 2015. Convergence of education and information & communication technology: A study on the communication characteristics of SNS affecting relationship development between professor and student. J. Korea Converg. Soc. 6(6):213-219. https://doi.org/10.15207/JKCS.2015.6.6.213
  2. Choi, S. and K.H. Choi. 2015. Achievement and satisfaction research of the undergraduate orchestra club activities: A convergent aspects of statistical method and opinion mining. J. Korea Converg. Soc. 6(4):25-31. https://doi.org/10.15207/JKCS.2015.6.4.025
  3. Choi, Y.S. and H.M. Kim. 2016. The influence of public diplomacy with social media on country image and country brands: Focusing on cultural contents. J. Korea Contents Assoc. 16(3):426-438. https://doi.org/10.5392/JKCA.2016.16.03.426
  4. Chun, H. 2015. The comparison of coauthor networks of two statistical journals of the Korean Statistical Society using social network analysis. J. Korean Data Inf. Sci. Soc. 26(2):335-346. https://doi.org/10.7465/jkdi.2015.26.2.335
  5. Hong, J.S. and I.K. Oh. 2016 Image difference of before and after an incident using social big data analysis: Focusing on a ramp return of 'K' airline. Int. J. Tour. Hosp. Res. 30(6):119-133. https://doi.org/10.21298/IJTHR.2016.06.30.6.119
  6. Hong, Y. 2014. A study on the invigorating strategies for open government data. J. Korean Data Inf. Sci. Soc. 25(4):769-777. https://doi.org/10.7465/jkdi.2014.25.4.769
  7. Jang, J.Y. 2013. Automatic retrieval of SNS opinion document using machine learning technique. J. Inst. Internet Broadcast. Commun. 13(5):27-35. https://doi.org/10.7236/JIIBC.2013.13.5.27
  8. Kang, S.G. 2012. A study on the classification and management measures of arboreta in Korea. Doctoral dissertation, Kyungpook National University, Daegu, Korea.
  9. Kang, S.N., Y.S. Kim, and S.H. Choi. 2015. Study on the social issue sentiment classification using text mining. J. Korean Data Inf. Sci. Soc. 26(5):1167-1173. https://doi.org/10.7465/jkdi.2015.26.5.1167
  10. Kim, D.Y., J.W. Park, and J.H. Choi. 2014. A comp arative study between stock price prediction models using sentiment analysis and machine learning based on SNS and news articles. J. Inf. Technol. Serv. 13(3):221-233. https://doi.org/10.9716/KITS.2014.13.3.221
  11. Lee, A.R., J.S. Bang, and Y.H. Kim. 2015. A design of a TV advertisement effectiveness analysis system using SNS big-data. KIISE Trans. Comput. Pract. 21(9):579-586. https://doi.org/10.5626/KTCP.2015.21.9.579
  12. Lee, Y.H. 2008. A study on the landscape management methods by the characteristics of the visual preference of forest landscape: Focused on Korea National Arboretum. Master's thesis, Kookmin University, Seoul, Korea.
  13. Lee, Y.J., J.H. Seo, and J.T. Choi. 2014. Fashion trend marketing prediction analysis based on opinion mining applying SNS text contents. J. Korean Inst. Inf. Technol. 12(12):163-170. https://doi.org/10.14801/jkiit.2014.12.12.163
  14. Lim, H.J. and S.H. Park. 2015. A tentative approach for regional futures strategy with big data, - through the analysis using the data of SNS and newpaper. J. Korean Cadastre Inf. Assoc. 17(1):75-90.
  15. Oh, H.J. 2016. A study on the effects of choice attributes for healing tourism on value, attitude and satisfaction; Focus on the scale development for healing tourism. Doctoral dissertation, Kyungsung University, Pusan, Korea.
  16. Oh, I.K., T.S. Lee, and C.N. Chon. 2015. A study on awareness of Korea tourism through big data analysis. J. Tour. Sci. 39(10):107-126. https://doi.org/10.17086/JTS.2015.39.10.107.126
  17. Seo, J.Y. and C. Koh. 2014. Big data analysis by sensitivity analysis. J. Soc. Converg. Knowl. 2(1):15-21.
  18. Song, K.S., H.Y. Noh, and S.J. Lee. 2015. Recommendation of emotion-based service by using SNS: A case of movie industry. J. Korea Manag. Eng. Soc. 20(2):91-104.
  19. Song, T.M., J. Song, and M.K. Cheon. 2015. Predicting tobacco risk factors by using social big data. J. Korean Data Inf. Sci. Soc. 26(5):1047-1059. https://doi.org/10.7465/jkdi.2015.26.5.1047
  20. Woo, K.S. and J.H. Suh. 2017. Urban landscape image study by text mining and factor analysis: Focused on Lotte World Tower. J. Korean Inst. Landsc. Archit. 45(4):104-117. https://doi.org/10.9715/KILA.2017.45.4.104