• International journal of advanced smart convergence
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• v.13 no.3
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• pp.300-305
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• 2024

Smart tourism enhances communication between tourists and residents, improves quality of life, increases the utilization of local tourism resources, and helps manage cities efficiently. This paper analyzes recent issues and trends in smart tourism, derives key factors for activating smart tourism based on the analyzed data, and conducts research on promoting smart tourism. Using smart tourism as a keyword, data was collected through Textom. The collection scope included a total of 33,588 pieces of data related to smart tourism over the past year, from May 1, 2023, to May 1, 2024. The data was analyzed using text mining and social network analysis techniques. Through this analysis, the paper suggests directions for the development of smart tourism, enabling the activation of local tourism and effective urban management.

• Journal of Information Technology Services
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• v.14 no.2
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• pp.143-158
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• 2015

Companies and governments in an era of big data have been tried to create new values with their data resources. Among many data resources, many companies especially pay attention to data which is obtained from Social Network Service (SNS) because it reveals precise opinion of customers and can be used to estimate profiles of them from their social relationships. However, it is not only hard to collect, store, and analyze the data, but system applications are also insufficient. Therefore, this study proposes a S-POS (Social POS) system which consists of three parts; Twitter Side, POS Side and TPAS (Twitter&POS Analysis System). In this system, SNS data and POS data which are collected from Twitter Side and POS Side are stored in Mongo D/B. And it provides several services with POS terminal based on analysis and matching results which are generated from TPAS. Through S-POS system, we expect to efficient and effective store and sales managements of system users. Moreover, they can provide some differentiated services such as cross-selling and personalized recommendation services.

• 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.

• The Journal of Information Systems
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• v.26 no.4
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• pp.419-442
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• 2017

Purpose The purpose of this study is to explore the pattern of user response and it's duration time through social media content response analysis. We also analyze the characteristics of content quality factors which are associate with the user response pattern. The analysis results will provide some implications to develop strategies and schematic plans for the operator of regional marketing on the SNS. Design/methodology/approach This study used mixed methods to verify the effects and responses of social media contents on the users who have concerns about regional events such as local festival, cultural events, and city tours etc. Big data analysis was conducted with the quantitative data from regional government SNSs. The data was collected through web crawling in order to analyze the social media contents. We especially analyzed the contents duration time and peak level time. This study also analyzed the characteristics of contents quality factors using expert evaluation data on the social media contents. Finally, we verify the relationship between the contents quality factors and user response types by cross correlation analysis. Findings According to the big data analysis, we could find some content life cycle which can be explained through empirical distribution with peak time pattern and left skewed long tail. The user response patterns are dependent on time and contents quality. In addition, this study confirms that the level of quality of social media content is closely relate to user interaction and response pattern. As a result of the contents response pattern analysis, it is necessary to develop high quality contents design strategy and content posting and propagation tactics. The SNS operators need to develop high quality contents using rich-media technology and active response contents that induce opinion leader on the SNS.

• Convergence Security Journal
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• v.22 no.1
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• pp.55-60
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• 2022

This study is to analyze trends and relational structures in the energy sector related to renewable energy. For this reason, in this study, we focused on big data including SNS data. SNS utilizes the Instagram platform to collect renewable energy hash tags and use them as a word embedding method for big data analysis and social network analysis, and based on the results derived from this research, it will be used for the development of the renewable energy industry. It is expected that it can be utilized.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.93-96
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• 2018

Big datatics technology that can store and analyze data and obtain new knowledge has been adjusted for importance in many fields of the society. Big data is emerging as an important problem in the field of information and communication technology, but the mind of continuous technology is rising. R, a tool that can analyze big data, is a language and environment that enables information analysis of statistical bases. In this thesis, we use this to analyze the Bible data. R is used to investigate the frequency of what text is distributed and analyze the Bible through analysis of social network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2194-2210
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• 2018

In the era of big data, consumers capture more and more economic surplus yet the seed of distrust also grows with the fast-spreading of social commerce, this paper began with the idea that product types may determine the degree of consumers' distrust even when identical discounts are offered for those products on Chinese social commerce websites. We also attempted to determine if distrust negatively affected consumers' purchase attitudes. 20 representative products that are commonly sold on social commerce websites in China were chosen to examine the relationships among product types, discount rates, distrust levels, and purchase attitudes. Inductive interview was used to collect the data as well as consumers' perceptions of the relationships. Data analysis results suggested that consumers like deep discounts, but their distrust levels increase along with the discount rates, however, the levels of increasing distrust vary according to product types. High, medium, and low discount rate categorizations were made and three propositions were suggested. This paper will contribute to the body of knowledge on online social commerce market and provide valuable implications for e-retailers and general consumers in online social commerce websites in China.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.353-360
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• 2023

This paper explores notable shifts in the restaurant startup market following the lifting of social distancing measures. Key trends identified include an escalated interest in startups, a heightened focus on the quality and diversity of food, a relative decline in the importance of delivery services, and a growing interest in specific industry sectors. The study's data collection spanned three years, from April 2021 to May 2023, encompassing the period before and after social distancing. Data were sourced from a range of online platforms, including blogs, news sites, cafes, web documents, and intellectual forums, provided by Naver, Daum, and Google. From this collected data, the top 50 words were identified through a refinement process. The analysis was structured around the social distancing application period, comparing data from April 2021 to April 2022 with data from May 2022 to May 2023. These observed trend changes provide founders with valuable insights to seize new market opportunities and formulate effective startup strategies. In summary, We offer crucial insights for founders, enabling them to comprehend the evolving dynamics in food service startups and to adapt their strategies to the current market environment.

• The Korean Journal of Community Living Science
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• v.27 no.spc
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• pp.665-679
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• 2016

This study examined the main factors for activating rural tourism of Jeollabuk-do using big data analysis. The tourism big data was gathered from public open data sources and social network services (SNS), and the analysis tools, 'Opinion Mining', 'Text Mining', and 'Social Network Analysis(SNA)' were used. The opinion mining and text mining analysis identified the key local contents of the 14 areas of Jeollabuk-do and the evaluations of customers on rural tourism. Social network analysis detected the relationships between their contents and determined the importance of the contents. The results of this research showed that each location in Jeollabuk-do had their specific contents attracting visitors and the number of contents affected the scale of tourists. In addition, the number of visitors might be large when their tourism contents were strongly correlated with the other contents. Hence, strong connections among their contents are a point to activate rural tourism. Social network analysis divided the contents into several clusters and derived the eigenvector centralities of the content nodes implying the importance of them in the network. Tourism was active when the nodes at high value of the eigenvector centrality were distributed evenly in every cluster; however the results were contrary when the nodes were located in a few clusters. This study suggests an action plan to extend rural tourism that develop valuable contents and connect the content clusters properly.

• Journal of Digital Contents Society
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• v.17 no.4
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• pp.295-305
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• 2016

The purpose of this study is to analyze the internal reputation factors that affect the job satisfaction by big data analysis in the social media for corporate reputation and verify the difference between large corporations and small-medium corporations for each factor of internal reputation. The result showed 'Salaries and Benefits' is a major factor that affects the job satisfaction for all research corporations, 'Senior Management' is a major factor for large corporations, and 'Salaries and Benefits' is a major factor for small-medium corporations. As for the difference factors of large corporations and small-medium corporations are 'Job Satisfaction', 'Salaries and Benefits', and 'Work-life Balance'. Unstructured data analysis shows some interesting features to be studied further.