• Journal of the Korean Applied Science and Technology
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• v.37 no.1
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• pp.76-82
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• 2020

Big data analysis in healthcare research seems to be a necessary strategy for the convergence of sports science and technology in the era of the Fourth Industrial Revolution. The purpose of this study is to provide the basic review to secure the diversity of big data and healthcare convergence by discussing the concept, analysis method, and application examples of big data and by exploring the application. Text mining, data mining, opinion mining, process mining, cluster analysis, and social network analysis is currently used. Identifying high-risk factor for a certain condition, determining specific health determinants for diseases, monitoring bio signals, predicting diseases, providing training and treatments, and analyzing healthcare measurements would be possible via big data analysis. As a further work, the big data characteristics provide very appropriate basis to use promising software platforms for development of applications that can handle big data in healthcare and even more in sports science.

• Asia pacific journal of information systems
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• v.29 no.4
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• pp.838-855
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• 2019

Online product reviews are a vital source for companies in that they contain consumers' opinions of products. The earlier methods of opinion mining, which involve drawing semantic information from text, have been mostly applied in one dimension. This is not sufficient in itself to elicit reviewers' comprehensive views on products. In this paper, we propose a novel approach in opinion mining by projecting online consumers' reviews in a multidimensional framework to improve review interpretation of products. First of all, we set up a new framework consisting of six dimensions based on a marketing management theory. To calculate the distances of review sentences and each dimension, we embed words in reviews utilizing Google's pre-trained word2vector model. We classified each sentence of the reviews into the respective dimensions of our new framework. After the classification, we measured the sentiment degrees for each sentence. The results were plotted using a radar graph in which the axes are the dimensions of the framework. We tested the strategy on Amazon product reviews of the iPhone and Galaxy smartphone series with a total of around 21,000 sentences. The results showed that the radar graphs visually reflected several issues associated with the products. The proposed method is not for specific product categories. It can be generally applied for opinion mining on reviews of any product category.

• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.189-198
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• 2019

This study aims to make meaning of the reactions of the Korean people through the text mining of comments on videos of the December 2018 MMA performance of intro on YouTube. For this, comments on 15 YouTube videos were collected over the past 10 months. With the collected data, a total of 5,135 comments were analyzed through crawling using the Python and BeautifulSoup programs, data was refined over a total of 3 sessions, and a final total of 5,080 comments were used as analysis material. A mining technique was used for data analysis and the process of refinement, analysis, and visualization was achieved using the Textom program. Research results showed that keyword analysis showed the keywords of 'performance', 'Korea', 'video', 'top', 'cool', 'dance', 'idol', 'legend', 'love', and 'gratitude' in that order and keywords such as 'patriotism' and 'Olympics' also appeared frequently. N-gram analysis showed that comments with contexts such as 'a top performance that will remain a legend among Korean idol performances', and 'an idol performance that displayed the traditional culture of Korea' were in higher ranks. Based on such keyword analysis results, topic modeling was applied and 5 top keywords were extracted from a total of 5 topics. Analysis results of topic contents and distribution showed that topics in the comments of this performance's videos largely consisted of the 3 reactions of 'high praise regarding the stage performance', 'affection towards the fusion and artistic sublimation of Korean traditional dance', and 'gratitude towards the uploading of cool dance videos'

• Information Systems Review
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• v.23 no.1
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• pp.155-172
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• 2021

With the recent increase in online product purchases, a recommender system that recommends products considering users' preferences has still been studied. The recommender system provides personalized product recommendation services to users. Collaborative Filtering (CF) using user ratings on products is one of the most widely used recommendation algorithms. During CF, the item-based method identifies the user's product by using ratings left on the product purchased by the user and obtains the similarity between the purchased product and the unpurchased product. CF takes a lot of time to calculate the similarity between products. In particular, it takes more time when using text-based big data such as review data of Amazon store. This paper suggests a hybrid recommendation system using a 2-phase methodology and text data mining to calculate the similarity between products easily and quickly. To this end, we collected about 980,000 online consumer ratings and review data from the online commerce store, Amazon Kinder Store. As a result of several experiments, it was confirmed that the suggested hybrid recommendation system reflecting the user's rating and review data has resulted in similar recommendation time, but higher accuracy compared to the CF-based benchmark recommender systems. Therefore, the suggested system is expected to increase the user's satisfaction and increase its sales.

• Culinary science and hospitality research
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• v.23 no.6
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• pp.1-11
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• 2017

The purpose of this study was to compare the cruise tours between China and U.S.A. through the semantic network analysis of big data by collecting online data with SCTM (Smart crawling & Text mining), a data collecting and processing program. The data analysis period was from January $1^{st}$, 2015 to August $15^{th}$, 2017, meanwhile, "cruise tour, china", "cruise tour, usa" were conducted to be as keywords to collet related data and packaged Netdraw along with UCINET 6.0 were utilized for data analysis. Currently, Chinese cruisers concern on the cruising destinations while American cruisers pay more attention on the onboard experience and cruising expenditure. After performing CONCOR (convergence of iterated correlation) analysis, for Chinese cruise tour, there were three clusters created with domestic destinations, international destinations and hospitality tourism. As for American cruise tour, four groups have been segmented with cruise expenditure, onboard experience, cruise brand and destinations. Since the cruise tourism of America was greatly developed, this study also was supposed to provide significant and social network-oriented suggestions for Chinese cruise tourism.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.1
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• pp.329-332
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• 2014

The Korean High Speed Electric Multiple Unit (HEMU) train system is soon being applicable to practical use. This new technology is expected not only to reshape the domestic market but also to be exported to overseas markets for high-speed train system. This study aims to prospect demands on the HEMU train technology and to formulate marketing strategies using a text-mining technique, therefore, providing a foundation for successful commercialization of the HEMU train system.

• Journal of the Korean Society for information Management
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• v.23 no.3 s.61
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• pp.147-165
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• 2006

In this study, experiments for selection of association terms were conducted in order to discover the optimum method in selecting additional terms that are related to an initial query term. Association term sets were generated by using support, confidence, and lift measures of the Apriori algorithm, and also by using the similarity measures such as GSS, Jaccard coefficient, cosine coefficient, and Sokal & Sneath 5, and mutual information. In performance evaluation of term selection methods, precision of association terms as well as the overlap ratio of association terms and relevant documents' indexing terms were used. It was found that Apriori algorithm and GSS achieved the highest level of performances.

• International Journal of Contents
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• v.14 no.3
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• pp.27-31
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• 2018

Social network service (SNS) data related to green tourism were used to estimate preferred tour sites and users' interests. Keywords related with green tour activities were employed to search the SNS data. SNS data were collected from Korean blogs such as Naver and Daum from June $1^{st}$ to August $31^{st}$ between 2015 and 2017 using text-mining solution. During the study period, seven hundred and five posts were analyzed. Associated words that frequently co-occurred with keywords were classified into different categories depending on the nature of associated words. Associated words included swimming pools and camping sites (location); experience and swimming pools (attribute); and water play and culture (culture/leisure). Our data suggest that SNS users with experience of green tourism in Korea exhibited interest in green tourism with swimming pools, camping sites, experience, water play and/or culture rather than particular popular sites. Based on the findings, it is recommended that preferred facilities such as swimming pools should be provided at green tourism sites to meet the users' needs and to facilitate green tourism.

• The Journal of Information Systems
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• v.20 no.3
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• pp.63-79
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• 2011

Deducting the risk level of infrastructure and buildings based on past human disaster risk cases and implementing prevention measures are important activities for disaster prevention. The object of this study is to measure the confidence to proceed quantitative analysis of various disaster risk cases through text mining methodology. Indeed, by examining confidence calculation process and method, this study suggests also a basic quantitative framework. The framework to measure the confidence is composed into four stages. First step describes correlation by categorizing basic elements based on human disaster ontology. Secondly, terms and cases of Term-Document Matrix will be created and the frequency of certain cases and terms will be quantified, the correlation value will be added to the missing values. In the third stage, association rules will be created according to the basic elements of human disaster risk cases. Lastly, the confidence value of disaster risk cases will be measured through association rules. This kind of confidence value will become a key element when deciding a risk level of a new disaster risk, followed up by preventive measures. Through collection of human disaster risk cases related to road infrastructure, this study will demonstrate a case where the four steps of the quantitative framework and process had been actually used for verification.

• Culinary science and hospitality research
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• v.23 no.8
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• pp.195-205
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• 2017

The purpose of this study was to compare the Starbucks in South Korea with Starbucks in U.S.A through the semantic network analysis of big data by collecting online data with SCTM(Smart Crawling & Text Mining) program which was developed by big data research institute at Kyungsung University, a data collecting and processing program. The data collection period was from January 1st 2014 to December 7th 2017, and packaged Netdraw along with UCINET 6.0 were utilized for data analysis and visualization. After performing CONCOR(convergence of iterated correlation) analysis and centrality analysis, this study illustrated the current characteristics of Starbucks for Korea and U.S.A reflected by the social network and the differences between Korea and U.S.A. Since the Starbucks was greatly developed, especially in Korea. this study also was supposed to provide significant and social-network oriented suggestions for Starbucks USA, Starbucks Korea and also the whole coffee industry. Also this study revealed that big data analytics can generate new insights into variables that have been extensively studied in existing hospitality literature. In addition, implications for theory and practice as well as directions for future research are discussed.