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Perception and Appraisal of Urban Park Users Using Text Mining of Google Maps Review - Cases of Seoul Forest, Boramae Park, Olympic Park -

구글맵리뷰 텍스트마이닝을 활용한 공원 이용자의 인식 및 평가 - 서울숲, 보라매공원, 올림픽공원을 대상으로 -

  • Lee, Ju-Kyung (Interdisciplinary Program in Landscape Architecture, Seoul National University) ;
  • Son, Yong-Hoon (Graduate School of Environment Studies, Seoul National University)
  • 이주경 (서울대학교 환경대학원 협동과정 조경학) ;
  • 손용훈 (서울대학교 환경대학원 환경조경학과)
  • Received : 2021.05.21
  • Accepted : 2021.07.07
  • Published : 2021.09.02

Abstract

The study aims to grasp the perception and appraisal of urban park users through text analysis. This study used Google review data provided by Google Maps. Google Maps Review is an online review platform that provides information evaluating locations through social media and provides an understanding of locations from the perspective of general reviewers and regional guides who are registered as members of Google Maps. The study determined if the Google Maps Reviews were useful for extracting meaningful information about the user perceptions and appraisals for parks management plans. The study chose three urban parks in Seoul, South Korea; Seoul Forest, Boramae Park, and Olympic Park. Review data for each of these three parks were collected via web crawling using Python. Through text analysis, the keywords and network structure characteristics for each park were analyzed. The text was analyzed, as were park ratings, and the analysis compared the reviews of residents and foreign tourists. The common keywords found in the review comments for the three parks were "walking", "bicycle", "rest" and "picnic" for activities, "family", "child" and "dogs" for accompanying types, and "playground" and "walking trail" for park facilities. Looking at the characteristics of each park, Seoul Forest shows many outdoor activities based on nature, while the lack of parking spaces and congestion on weekends negatively impacted users. Boramae Park has the appearance of a city park, with various facilities providing numerous activities, but reviewers often cited the park's complexity and the negative aspects in terms of dog walking groups. At Olympic Park, large-scale complex facilities and cultural events were frequently mentioned, emphasizing its entertainment functions. Google Maps Review can function as useful data to identify parks' overall users' experiences and general feelings. Compared to data from other social media sites, Google Maps Review's data provides ratings and understanding factors, including user satisfaction and dissatisfaction.

본 연구의 목적은 Google Maps에서 제공하는 장소에 대한 리뷰를 활용하여 실제로 공원을 방문한 이용자의 인식과 평가를 파악하는 것이다. 구글맵리뷰는 Social Network Service(SNS)를 통해 장소에 대한 인식과 평가에 관한 정보를 얻는 온라인 리뷰이며, 일반 리뷰어와 구글맵의 회원으로 등록된 지역 가이드의 관점에서 장소에 대한 이해를 볼 수 있는 서비스이다. 본 연구에서는 구글맵리뷰 분석이 공원 관리에 필요한 이용자들의 인식과 평가를 추출하는데 활용될 수 있는지를 살펴보고자 하였다. 서로 다른 공간특징과 시설을 가지는 3개의 공원(서울숲, 보라매공원, 올림픽공원)을 대상으로 파이썬을 활용한 웹 크롤링을 통해서 구글맵리뷰 내용을 수집하였다. 그리고 텍스트 분석을 통해 공원별 주요 키워드 분석과 네트워크 구조에 따른 특성을 분석하고, 이와 함께 구글맵리뷰에서 제공하는 별점 평갓값과 외국인 리뷰 데이터에 대한 분석도 수행했다. 연구 결과, 3개의 공원에서 공통으로 나타나는 특성으로는 이용목적으로 '산책', '자전거', '휴식', '피크닉'이 있었으며, 동반유형으로 '가족', '아이', '애견'이, 인프라로는 '놀이터', '산책로'가 있었다. 공원별 특색을 보면 서울숲은 자연을 기반으로 하는 야외활동이 많이 나타났고 반면, 주차공간 부족과 주말 혼잡은 공원 이용자에게 부정적인 영향을 미치고 있었다. 보라매공원은 수많은 활동을 제공하는 다양한 시설을 갖춘 도시공원의 모습을 가지고 있었다. 리뷰어들은 반려견을 동반하는 이용자 그룹과 그렇지 않은 다른 이용자 그룹 간의 갈등과 공원의 복잡함에 대한 부정적인 측면을 언급했다. 올림픽공원에는 대형 복합시설이 있으며, 커뮤니티, 문화예술공연과 같은 대규모 문화 이벤트가 많이 언급되었고, 레크리에이션 기능이 강조되었다. 구글맵리뷰는 공원에 대한 이용자의 전반적 경험과 이미지에 대한 특징을 파악하는 유용한 자료라고 할 수 있다. 또한, 다른 소셜미디어 데이터와 비교할 때 특히 구글맵리뷰는 공원에 대한 이용자 평갓값과 만족 및 불만족 요인을 이해할 수 있는 데이터를 제공한다.

Keywords

References

  1. Bae, J. K.(2016) The effects of post-adoption beliefs of Chinese social commerce consumers on user satisfaction and continuous usage intention : Focused on the three major social commerce services in China. The Korean Association of Logos Management 14(2): 115-134.
  2. Bark, S. H. and Y. G. Kim(2011) The landscape of Seonyoo-do park captured in one-person media focusing on blogs. Journal of the Korean Institute of Landscape Architecture 39(3): 64-73. https://doi.org/10.9715/KILA.2011.39.3.064
  3. Buneman, P.(1997) Semistructured data. In Proceedings of the Sixteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (pp. 117-121).
  4. Byun, S., D. Lee and N. Kim(2016) Methodology for identifying issues of user reviews from the perspective of evaluation criteria: Focus on a hotel information site. Journal of Intelligence and Information Systems 2 (3): 23-43. https://doi.org/10.13088/jiis.2016.22.3.023
  5. CABE(2010). Urban Green Nation: Building the Evidence Base.
  6. Chang, B. H., and S. H. Nam(2012) Social media and network journalism: Focusing on audiences' evaluations on journalism functions of social media. Korea Regional Communication Research Association 12(4): 457-496.
  7. Chae, S. H., J. I. Lim and J. Kang(2015) A comparative analysis of social commerce and open market using user reviews in Korean mobile commerce. Journal of Intelligence and Information Systems 21(4): 53-77. https://doi.org/10.13088/jiis.2015.21.4.053
  8. Chae, I. Y.(2017) A method for analysis of preferences of places based on sentimental analysis using SNS data: Case study on theme parks in Seoul, South Korea. Seoul University, 1-92.
  9. Chae, I. Y., J. Y. Kim, and K. Y. Yu(2017) Place Preference Analysis Using Twitter Data and Google Reviews.
  10. Cho, S. Y., H. K., Kim, B., Kim, and H. W. Kim(2014) Predicting movie revenue by online review mining: Using the opening week online review. Information Systems Review 16(3): 113-134. https://doi.org/10.14329/isr.2014.16.3.113
  11. Choi, J., H. Ryu, D. Yu, N. Kim and Y. Kim(2016) System design for analysis and evaluation of e-commerce products using review sentiment word analysis. KIISE Transactions on Computing Practices 22(5): 209-217. https://doi.org/10.5626/KTCP.2016.22.5.209
  12. Daniel, B.(2015) Big data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology 46(5): 904-920. https://doi.org/10.1111/bjet.12230
  13. Feldman, R. and J. Sanger(2007) The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press.
  14. Guerrero, P., M. S. Mol er, A. S. Olafsson, and B. Snizek(2016) Revealing cultural ecosystem services through Instagram images: The potential of social media volunteered geographic information for urban green infrastructure planning and governance. Urban Planning 1(2): 1-17. https://doi.org/10.17645/up.v1i2.609
  15. Hearst, M. A.(1999) Untangling text data mining. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics (pp. 3-10).
  16. Hearst, M.(2003) What is Text Mining. SIMS, UC Berkeley, 5.
  17. Hoaglin, D. C., F. Mosteller, and J. W. Tukey(2000) Understanding Robust and Exploratory Data Analysis (No. Sirsi) i9780471384915).
  18. Jeon, B. and H. Ahn(2015) A collaborative filtering system combined with users' review mining: Application to the recommendation of smartphone apps. Journal of Intelligence and Information Systems 21(2): 1-18. https://doi.org/10.13088/jiis.2015.21.2.01
  19. Jin, S. A., G. E. Heo, Y. K. Jeong, and M. Song(2013) Topic-network based topic shift detection on twitter. Journal of the Korean Society for Information Management 30(1): 285-302. https://doi.org/10.3743/KOSIM.2013.30.1.285
  20. Kavaratzis, M., and G. J. Ashworth(2005) City branding: An effective assertion of identity or a transitory marketing trick?. Tijdschrift voor economische en sociale geografie 96(5): 506-514. https://doi.org/10.1111/j.1467-9663.2005.00482.x
  21. Kim, J. E., C. Park, A. Y. Kim, and H. G. Kim(2019) Analysis of behavioral characteristics by park types displayed in 3rd generation SNS. Journal of the Korean Institute of Landscape Architecture 47(2): 49-58.
  22. Kim, S. R., Y. Choi, and H. Yoon(2019) The analysis of the visitors' experiences in Yeonnam-dong before and after the Gyeongui line park project-a text mining approach. Journal of the Korean Institute of Landscape Architecture 47(4): 33-49. https://doi.org/10.9715/KILA.2019.47.4.033
  23. Ko, H. J.(2019) Evaluation of Ecosystem Services of Urban Green Spaces Using Text Mining Techniques: Focused on Cultural Ecosystem Services in Gwacheon and Ansan, Republic of Korea. Doctoral Dissertation. Seoul National University.
  24. Lee, J. I.(2013) A Study on the Micro-Discourse about Seoul Forest in Personal Media: Focused on Sense of Place. Master's Thesis. Seoul National University.
  25. Lee, J. H.(2013) Understanding customer values by analyzing the contents of online hotel reviews. The Journal of the Korea Contents Association 13(10): 533-546. https://doi.org/10.5392/JKCA.2013.13.10.533
  26. Lee, S. H., J. Cui, and J. W. Kim(2016) Sentiment analysis on movie review through building modified sentiment dictionary by movie genre. Journal of Intelligence and Information Systems 2 (2): 97-113.
  27. Lee, Y. J., J. H. Ji, G. Woo and H. G. Cho(2009) TRIB: A clustering and visualization system for responding comments on blogs. The KIPS Transactions: PartD 16(5): 817-824.
  28. Lee, Y. M., P. Kwon, K. Y. Yu, and J. Y. Kim(2017) Method for spatial sentiment lexicon construction using Korean place reviews. Journal of Korean Society for Geospatial Information System 25(2): 3-12.
  29. Litvin, S. W., R. E. Goldsmith and B. Pan(2008) Electronic word-of-mouth in hospitality and tourism management. Tourism Management 29(3): 458-468. https://doi.org/10.1016/j.tourman.2007.05.011
  30. Lund, N. F., S. A. Cohen, and C. Scarles(2018) The power of social media storytelling in destination branding. Journal of Destination Marketing & Management 8: 271-280. https://doi.org/10.1016/j.jdmm.2017.05.003
  31. Matsuoka Keisuke, 「Google Maps, the Birth of a New World」, Hong Sung Min, Wisdom House(2017), p165.
  32. Munawir, M., D. Koerniawan and B. J. Dewancker(2019) Visitor perceptions and effectiveness of place branding strategies in thematic parks in Bandung City using text mining based on Google maps user reviews. Sustainability 11(7): 2123. https://doi.org/10.3390/su11072123
  33. Nam, J., and N. Dempsey(2019) Place-keeping for health? Charting the challenges for urban park management in practice. Sustainability 11(16): 4383. https://doi.org/10.3390/su11164383
  34. Park, J. D.(2015) A study on mapping users' topic interest for question routing for community-based Q&A service. Journal of the Korean Society for Information Management 32(3): 397-412. https://doi.org/10.3743/KOSIM.2015.32.3.397
  35. Richards, D. R. and D. A. Friess(2015) A rapid indicator of cultural ecosystem service usage at a fine spatial scale: Content analysis of social media photographs. Ecological Indicators 53: 187-195. https://doi.org/10.1016/j.ecolind.2015.01.034
  36. Son, Y. H., and Y. J. Kim(2019) The image of Ruralism in Korea through a text mining for online news media analysis. Journal of Korean Society of Rural Planning 25(4): 13-26. https://doi.org/10.7851/Ksrp.2019.25.4.013
  37. Tukey, J. W.(1977) Exploratory Data Analysis 2: 131-160.
  38. Warnock, S., and G. Griffiths(2015) Landscape characterisation: The living landscapes approach in the UK. Landscape Research 40(3): 261-278. https://doi.org/10.1080/01426397.2013.870541
  39. Woo, K. S., and J. H. Suh(2018) Time series analysis of park use behavior utilizing big data-targeting Olympic Park. Journal of the Korean Institute of Landscape Architecture 46(2): 27-36. https://doi.org/10.9715/KILA.2018.46.2.027
  40. Xianghua, F., L. Guo, G. Yanyan, and W. Zhiqiang(2013) Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon. Knowledge-Based Systems 37, 186-195. https://doi.org/10.1016/j.knosys.2012.08.003