Improving the Support System of Public Sports Facilities Applying Text Mining and Multiple Focused on the support facilities for the National Sports Promotion Fund
Il-Gwang Kim1, Mi-Suk Kim2, Su-Sun Park3, Jialei Jiang4, Seong-Taek Park5
1Il-Gwang Kim, Department of Leisure Sport Industy, Korea national Sport University, Yangjae-daero, Songpa-gu, Seoul, South Korea, East Asian.
2Mi-Suk Kim, Korea Institute of Sport Science, Hwarang-ro, Nowon-gu, Seoul, South Korea, East Asian.
3Su-Sun Park, Department of Social Welfare, Seowon University, Musimseo-ro, Seowon-gu, Cheongju-si, Chungbuk, South Korea, East Asian.
4Jialei Jiang, Department of MIS, Chungbuk National University, Chungdae-Ro, Seowon-Gu, Cheongju, Chungbuk, South Korea, East Asian.
5Seong-Taek Park, Department of MIS, Chungbuk National University, Chungdae-Ro, Seowon-Gu, Cheongju, Chungbuk, South Korea, East Asian.
Manuscript received on 08 June 2019 | Revised Manuscript received on 14 June 2019 | Manuscript Published on 22 June 2019 | PP: 173-179 | Volume-8 Issue-8S2 June 2019 | Retrieval Number: H10330688S219/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Background/Objectives: This study used text mining to analyze the user complaints about public sports facilities supported by the Korea Sports Promotion Fund and seek measures for improvement. Methods/Statistical analysis: A framework for sports texts should be designed to include diverse features for collecting and analyzing sports-related texts. Among other methods of topic modelling, this study used the most widely used probability model, LDA(Latent Dirichlet Allocation). Word2vec models are applicable for different purposes. This study used Word2vec to identify key words highly associated to relevant key words. Findings: The analysis highlighted the following. First, the LDA topic clustering analysis by type identified 4 important key words (instructors, members, swimming and failure), which were in turn explored further with Word2Vec. Second, the analysis of associated words found such salient words as swimming, members, time, center, class and fitness acceptance in relation to the general type, whereas members, swimming, time, center, exercise, class and lesson proved important in the complex type. Third, as for the frequency of words, swimming, members and center frequently appeared in the general type in the order named, whereas the complex and gymnasium types were associated with the importance of swimming, members and time, in the order named. Improvements/Applications: The present findings may serve as a guideline for public sports facilities as public goods to improve the quality of service for users based on the user complaints.
Keywords: Public Sports Facilities, Text Mining, Multiple Comparative Study, National Sports Promotion Fund, Bigdata.
Scope of the Article: Big Data Analytics and Business Intelligence