• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.41-46
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• 2022

The purpose of this study is to confirm and analyze the impact on consumers through big data keyword analysis on weak food. For data collection, web documents, blogs, news, cafes, intellectuals, academic information, and Google Web, news, and Facebook provided by Naver and Daum were used as analysis targets. The data analysis period was set from January 2018 to December 2021. For data collection and analysis, the frequency and matrix of keywords were extracted through Textom, a social matrix site, and the relationship and connection centrality between keywords were analyzed and visualized using the Netdraw function among UCINET6 programs. In addition, CONCOR analysis was conducted to derive clusters for similar keywords. As a result of analyzing yakseon food with keywords, a total of 35,985 cases of collected data were derived. Through this, it was confirmed that medicinal food affects consumers. Furthermore, if a business model is created and developed through yakseon food, it will be possible to lead the popularization of yakseon food.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.7-12
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• 2022

The modern stage of society's development is characterized by the rapid penetration of information technologies into all spheres of life. Their use contributes to improving the quality of tourism services, as well as the competitiveness of tourism industry enterprises. The role of information technology in tourism is growing more and more every year, which determines the relevance of the study of modern trends in the use of information technology in the tourism sector. The purpose of the study is to determine the possibilities of using information technologies to improve the quality of services provided to the tourism industry under the COVID-19 restrictions. The article systematizes the main approaches to the "cluster" category and provides an original definition of the "regional tourist cluster" concept. Based on an expert survey, the main trends in the introduction of information technologies in the tourism industry under the COVID-19 restrictions have been identified, which include virtual reality and augmented reality, speech recognition technologies, photo, video, audio (contactless control technologies), mobile IT applications and Big Data technologies. It has been concluded that the vast majority of improvements in the organization of tourism services under restrictions will be based on the organization of virtual solutions and online activities. The types of tourism services will also change, and information technology will help their development and dissemination.

• Journal of the Korean Society for information Management
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• v.35 no.1
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• pp.13-32
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• 2018

The purpose of this study is to analyze the association among the subject areas of big data research papers. The subject group of the units of analysis was extracted by applying co-citation networks, and the rules of association were analyzed using Apriori algorithm of R program, and visualized using the arulesViz package of R program. As a result of the study, 22 subject areas were extracted and these subjects were divided into three clusters. As a result of analyzing the association type of the subject, it was classified into 'professional type', 'general type', 'expanded type' depending on the complexity of association. The professional type included library and information science and journalism. The general type included politics & diplomacy, trade, and tourism. The expanded types included other humanities, general social sciences, and general tourism. This association networks show a tendency to cite other subject areas that are relevant when citing a subject field, and the library should consider services that use the association for academic information services.

• Journal of Internet of Things and Convergence
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• v.2 no.2
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• pp.21-26
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• 2016

In this paper, it is possible to update the tourist information in real time, on/off-line tour proposes a solution(BBTS) based on a bluetooth beacon can provide tourist information without the need for wireless data network. BBTS consists of a bluetooth based data of the low-power supply system and the beacons and interoperable smart applications. Data supply system consists of the BLE & Beacon Pairing-based / non-pairing data transmission module with integral hardware. Smart application modules that provide indoor location of users information, internal server module and tourist information collection and information guide around comprised of applications. The proposed BBTS is possible that indoor service tourism tourist demand due to utilizing the beacon technology. Outdoor tourist information is designed to be downloaded to the smartphone receives the information received from the beacon APK file to provide services. BBTS system is expected to make a big impact on the smart tourism services industry.

• International Journal of Advanced Culture Technology
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• v.12 no.1
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• pp.158-168
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• 2024

With Wenhai big data SaaS cloud platform.2.0, this study analyzed data of 135 news reports relating to Chinese city Chongqing from Yonhap News Agency and ten South Korean mainstream newspapers from May 1^st, 2018 to September 30^th, 2022. Under the framework of Frame Theory, this research conducted data mining and analysis on how Korean mainstream media shaped city image of Chongqing, what kind of city images were shaped from dimensions of politics, economy, society, culture & sports as well as tourism and whether they are consistent with those in Chinese media. At the last part, discussions and suggestions was made.

• Smart Media Journal
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• v.13 no.9
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• pp.9-17
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• 2024

This study aims to analyze the current status of smart tourism in various regions. To achieve this, 599 news articles containing the keyword 'smart tourism' were collected from national daily newspapers in the BigKinds database, covering the period from January 2014 to June 2024. The collected data was subject to topic modeling based on location, and network analysis was performed using the keyword frequencies in each topic. The topic modeling results identified six major topics: 'Jeju,' 'Incheon,' 'Daegu_Busan_Ulsan,' 'Gyeongju,' 'Suwon,' and 'Yangyang.' It was found that the development of smart tourism in all these regions is centered around tourism projects led by government and local authorities. The network analysis results revealed that 'platform' and 'content' are key keywords related to smart tourism technology across all topics, indicating that these concepts are interconnected to provide services to individual tourists. The findings of this study are expected to provide valuable information for formulating policies and strategies to promote smart tourism in various regions and contribute to the realization of sustainable smart tourism.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.409-418
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• 2015

With the development of mobile devices and Internet, information exchange has actively been made through SNS and Blogs. Blogs are widely used as a space where people share their experience after their visit to tourist attractions. We propose a method of recommending associated tourist attractions based on tourists' opinions using issue analysis, association analysis, and sentimental analysis for various online reviews including news in order to help to develop tour products and policies. The result shows that north area of Chungbuk province has been selected as issue attractions, and associated attractions/keywards have been identified for given well-known attraction. Positive/negative opinion for review texts has been analyzed and user can grasp the reason for the sentiments. Multidimensional analysis technique has been integrated to derive additional sophisticated insights and various policy proposal for smart tourism.

• Journal of Smart Tourism
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• v.4 no.2
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• pp.5-14
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• 2024

This study explores the transformative potential of machine learning (ML) and ML-driven data analytics in the hospitality industry. It provides a comprehensive overview of this emerging method, from explaining ML's origins to introducing the evolution of ML-driven data analytics in the hospitality industry. The present study emphasizes the shift embodied in ML, moving from explicit programming towards a self-learning, adaptive approach refined over time through big data. Meanwhile, social media analytics has progressed from simplistic metrics deriving nuanced qualitative insights into consumer behavior as an industry-specific example. Additionally, this study explores innovative applications of these innovative technologies in the hospitality sector, whether in demand forecasting, personalized marketing, predictive maintenance, etc. The study also emphasizes the integration of ML and social media analytics, discussing the implications like enhanced customer personalization, real-time decision-making capabilities, optimized marketing campaigns, and improved fraud detection. In conclusion, ML-driven hospitality data analytics have become indispensable in the strategic and operation machinery of contemporary hospitality businesses. It projects these technologies' continued significance in propelling data-centric advancements across the industry.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.206-211
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• 2022

As the level of consumption is improved and cultural life is pursued, the consumer's consciousness structure is rapidly changing, and the demand for product selection level, variety, and quality is becoming more diverse. The restaurant economy is falling due to the prolonged COVID-19, the economic recession, income decline, and changes in population structure and lifestyle, but the Meal- kit market is growing rapidly. This study aims to identify the consumer perception of Meal-kit, which is rapidly growing as an alternative to existing meals in the fields of dining out, food, and distribution due to the development of technology and social environment using big data. As a result of the analysis, the keywords with the highest frequency of appearance were in the order of Meal-kit, Cooking, Product, Launching, and Market and were divided into 8 groups through the CONCOR analysis. We want to identify consumer trends related to the key keywords of Meal-kit, present effective data related to Meal-kit demand for Meal-kit specialized companies, and provide implications for establishing marketing strategies for differentiated competitive advantage.

• Journal of the Korean Dietetic Association
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• v.30 no.4
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• pp.227-239
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• 2024

This study aimed to analyze the factors influencing the adaptation of novice nutrition teachers through big data analysis and to propose strategies for enhancing this process. Data were collected from internet portals using the keywords 'novice nutrition teacher' and 'nutrition teacher' from May 25, 2021, to May 25, 2024. Text mining techniques, including frequency analysis, semantic network analysis, and CONvergence of iterated CORrelations (CONCOR) analysis, were employed. Key terms such as 'teacher', 'nutrition', 'career', 'school', and 'school meals' exhibited high frequency and centrality, indicating the multifaceted roles of novice nutrition teachers and the need for increased support. Excessive workload and stress related to school meal management negatively impacted adaptation, highlighting the need for systematic management and capacity-building training programs. Mentoring and consulting systems played a crucial role in enhancing professional development, leading to better adaptation and higher job satisfaction. Additionally, stress and anxiety during the appointment preparation process were significant factors influencing adaptation, suggesting the need for improvements in the training curriculum at teacher education institutions. These findings provide valuable insights for developing policies to support the adaptation of novice nutrition teachers.