• International journal of advanced smart convergence
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• v.13 no.1
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• pp.108-113
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• 2024

The purpose of this study is to analyze user perceptions of tourism platforms through big data. Data were collected from Naver, Daum, and Google as big data analysis channels. Using semantic network analysis with the keyword 'tourism platform,' a total of 29,265 words were collected. The collection period was set for two years, from August 31, 2021, to August 31, 2023. Keywords were analyzed for connected networks using TexTom and Ucinet programs for social network analysis. Keywords perceived by tourism platform users include 'travel,' 'diverse,' 'online,' 'service,' 'tourists,' 'reservation,' 'provision,' and 'region.' CONCOR analysis revealed four groups: 'platform information,' 'tourism information and products,' 'activation strategies for tourism platforms,' and 'tourism destination market.' This study aims to expand and activate services that meet the needs and preferences of users in the tourism field, as well as platforms tailored to the changing market, based on user perception, current status, and trend data on tourism platforms.

• International Journal of Advanced Culture Technology
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• v.12 no.1
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• pp.254-261
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• 2024

Among eco-friendly energy, solar energy is one of the renewable energy sources that is developing in the spotlight in many countries. In line with this, the Korean government and local governments are carrying out projects to provide subsidies for the distribution of household solar power, raising the spread of household solar power and awareness. However, due to the lack of research on consumer perception of household solar power, this study investigated the perception of household solar power from 2015 to 2022 by setting the central word as solar power. As a result, 2016 had the highest number of collections, and when the top 50 words for each year were analyzed, it was confirmed that words related to the installation and maintenance of household solar power dominated. And through CONCOR analysis, a total of four were derived: solar energy recognition, renewable and eco-friendly energy recognition, solar government policies, solar companies, and perceptions of households. Through emotional analysis, it was confirmed that 2021 had the most positive data. As a result, consumer perception of household solar power is positive based on what was mentioned above, but research on how to use negative opinions on waste control and installation and maintenance should be conducted.

• International Journal of Advanced Culture Technology
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• v.12 no.1
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• pp.270-275
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• 2024

Introduction. In this study, purpose is to analize the types of golf tourism, inbound or outbound, by using big data and see how movement of industry is being changed and what changes have been made during and after Covid-19 in golf industry. Method Using Textom, a big data analysis tool, "golf tourism" and "Covid-19" were selected as keywords, and search frequency information of Naver and Daum was collected for a year from 1 st January, 2023 to 31st December, 2023, and data preprocessing was conducted based on this. For the suitability of the study and more accurate data, data not related to "golf tourism" was removed through the refining process, and similar keywords were grouped into the same keyword to perform analysis. As a result of the word refining process, top 36 keywords with the highest relevance and search frequency were selected and applied to this study. The top 36 keywords derived through word purification were subjected to TF-IDF analysis, visualization analysis using Ucinet6 and NetDraw programs, network analysis between keywords, and cluster analysis between each keyword through Concor analysis. Results By using big data analysis, it was found out option of oversea golf tourism is affecting on inbound golf travel. "Golf", "Tourism", "Vietnam", "Thailand" showed high frequencies, which proves that oversea golf tour is now the re-coming trends.

• Journal of the Korea Fashion and Costume Design Association
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• v.22 no.3
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• pp.49-61
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• 2020

This study analyzed the social media accounts and performed a Big Data analysis of Z-generation fashion using Textom Text Mining Techniques program and Ucinet Big Data analysis program. The research results are as follows: First, as a result of keyword analysis on 67.646 Z-generation fashion social media posts over the last 5 years, 220,211 keywords were extracted. Among them, 67 major keywords were selected based on the frequency of co-occurrence being greater than more than 250 times. As the top keywords appearing over 1000 times, were the most influential as the number of nodes connected to 'Z generation' (29595 times) are overwhelmingly, and was followed by 'millennials'(18536 times), 'fashion'(17836 times), and 'generation'(13055 times), 'brand'(8325 times) and 'trend'(7310 times) Second, as a result of the analysis of Network Degree Centrality between the key keywords for the Z-generation, the number of nodes connected to the "Z-generation" (29595 times) is overwhelmingly large. Next, many 'millennial'(18536 times), 'fashion'(17836 times), 'generation'(13055 times), 'brand'(8325 times), 'trend'(7310 times), etc. appear. These texts are considered to be important factors in exploring the reaction of social media to the Z-generation. Third, through the analysis of CONCOR, text with the structural equivalence between major keywords for Gen Z fashion was rearranged and clustered. In addition, four clusters were derived by grouping through network semantic network visualization. Group 1 is 54 texts, 'Diverse Characteristics of Z-Generation Fashion Consumers', Group 2 is 7 Texts, 'Z-Generation's teenagers Fashion Powers', Group 3 is 8 Texts, 'Z-Generation's Celebrity Fashions' Interest and Fashion', Group 4 named 'Gucci', the most popular luxury fashion of the Z-generation as one text.

• Journal of the Korean Society of Clothing and Textiles
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• v.40 no.6
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• pp.1034-1044
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• 2016

This study seeks a method to objectively evaluate sensibility based on Big Data in the field of design. In order to do so, this study examined the sensibility responses on design factors for the public through a network analysis of texts displayed in social media. 'Hanbok', a formal clothing that represents Korea, was selected as the subject for the research methodology. We then collected 47,677 keywords related to Hanbok from 12,000 posts on Naver blogs from January $1^{st}$ to December $31^{st}$ 2015 and that analyzed using social matrix (a Big Data analysis software) rather than using previous survey methods. We also derived 56 key-words related to design elements and sensibility responses of Hanbok. Centrality analysis and CONCOR analysis were conducted using Ucinet6. The visualization of the network text analysis allowed the categorization of the main design factors of Hanbok with evaluation terms that mean positive, negative, and neutral sensibility responses. We also derived key evaluation factors for Hanbok as fitting, rationality, trend, and uniqueness. The evaluation terms extracted based on natural language processing technologies of atypical data have validity as a scale for evaluation and are expected to be suitable for utilization in an index for sensibility evaluation that supplements the limits of previous surveys and statistical analysis methods. The network text analysis method used in this study provides new guidelines for the use of Big Data involving sensibility evaluation methods in the field of design.

• Journal of Fashion Business
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• v.23 no.5
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• pp.81-95
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• 2019

The purpose of this study is to research consumers' perception of the HiSeoul fashion show, which is being used by new designers as a means of promotion, and to propose a strategy for revitalizing new designer brands. This was done in order to secure basic data from fashion consumers, to help guide marketing strategies and promote rising designers. In this research, the consumers' perception of HiSeoul fashion show was verified using text-mining, data refinement and word clouding that was undertaken by TEXTOM3.0. Also, semantic network analysis, CONCOR analysis and visualization of the analysis results were performed using Ucinet 6.0 and NetDraw. "HiSeoul fashion show" was used as the keyword for text-mining and data was collected from March 1, 2018 to April 30, 2019. Using frequency analysis, TF-IDF, and N-gram, it was also shown that consumers are aware of places where shows are held, such as DDP and Igansumun. It was also revealed that consumers recognize rising designer brands, designer's names, the names of guests attending the show and the photo times. This study is meaningful in that it not only confirmed consumers' interest in new designer brands participating in the HiSeoul Fashion Show through big data but also confirmed that it is available as a marketing strategy to boost brand sales. This study suggests using HiSeoul show room to induce consumer sales, or inviting guests that match the brand image to promote them on SNS on the day the show is held for a marketing strategy.

• Journal of Fashion Business
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• v.25 no.3
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• pp.90-107
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• 2021

This study concludes that communication based on interaction between broadcasting hosts and consumers is differently characterized by fashion live commerce types. Subcategories of the types of fashion live commerce were created and used in the analyses of domestic consumer awareness. Three subcategories were created: The department store type, Designer brand type, and Influencer host type. Comments representing consumers' awareness that appear immediately during real-time broadcasting were collected and used for the analyses. The frequency and TF-IDF-based top keywords were selected to analyze the semantic network and CONCOR, and the top keywords were analyzed by deriving the values of degree of centrality. The analysis identified that a group of product attributes and a group of live commerce offered value were common between the three types. As for the group characteristics classified by type, for the department store types, brand attributes, benefits, and values from pursuing the products were identified. For designer brand types, a group of viewers' responses and inquiries were identified. It is interpreted that the satisfaction value gained from hosts with product expertise has been clustered. Influencer host types have affirmed a group of external product values. A close relationship is formed and it is thought to have led a group of values to trust the external image of the product. This study carries significance in analyzing real-time comment data from consumers using fashion live commerce to empirically reveal the characteristics of each type.

• Journal of Fashion Business
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• v.23 no.3
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• pp.85-100
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• 2019

This study examines changes in consumer perceptions of fashion shows, which are critical elements in the apparel industry and a means to represent a brand's image and originality. For this purpose, big data in clothing marketing, text mining, semantic network analysis techniques were applied. This study aims to verify the effectiveness and significance of fashion shows in an effort to give directions for their future utilization. The study was conducted in two major stages. First, data collection with the key word, "fashion shows," was conducted across websites, including Naver and Daum between 2015 and 2018. The data collection period was divided into the first- and second-half periods. Next, Textom 3.0 was utilized for data refinement, text mining, and word clouding. The Ucinet 6.0 and NetDraw, were used for semantic network analysis, degree centrality, CONCOR analysis and also visualization. The level of interest in "models" was found to be the highest among the perception factors related to fashion shows in both periods. In the first-half period, the consumer interests focused on detailed visual stimulants such as model and clothing while in the second-half period, perceptions changed as the value of designers and brands were increasingly recognized over time. The findings of this study can be utilized as a tool to evaluate fashion shows, the apparel industry sectors, and the marketing methods. Additionally, it can also be used as a theoretical framework for big data analysis and as a basis of strategies and research in industrial developments.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.2
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• pp.79-88
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• 2022

Social media big data includes a lot of information that can identify not only consumer consumption patterns but also local images. This paper was collected annually data including 'Samcheok' from 2015 to 2019 from Blog and Cafe of Naver and Daum in domestic portal site, and analyzed the regional image change after refining keyword which forms the regional image by performing text mining and network analysis. According to the research results, the regional image of 2015 was expressed with image cognitive elements of the nearby place name or place etc. such as 'Jangho Port', 'Donghae', and 'Beach'. However the regional image both 2016 and 2019 were changed with image cognitive elements of 'SamcheokSolbich' which is a special place within region. Therefore as the keywords related to the local image include 'Jangho Port' and Resort, which are the representative attractions of Samcheok, it can be seen that the infrastructure factor plays a big role in forming the local image. The significance test for the network data used the bootstrap technique, and the p-values in 2015, 2016, and 2019 were 0.0002, 0.0006, and 0.0002, respectively, which were found to be statistically significant at the significance level of 5%.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.193-204
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• 2022

The 2018 Seoul Zero Pay is a policy actively promoted by the government as an economic stimulus package for small business owners and the self-employed who are experiencing economic depression due to COVID-19. However, the controversy over the effectiveness of Zero Pay continues even after two years have passed since the implementation of the policy. Zero Pay is a joint QR code mobile payment service introduced by the government, Seoul city, financial companies, and private simple payment providers to reduce the burden of card merchant fees for small business owners and self-employed people who are experiencing economic difficulties due to the economic downturn., it was attempted in the direction of economic revitalization for the return of alleyways[1]. Therefore, this study intends to draw implications for improvement measures so that the ongoing zero-pay can be further activated and the economy can be settled normally. The analysis results of this study are as follows. First, it shows the effect of increasing the income of small business owners by inducing consumption in alleyways through the economic revitalization policy of Zero Pay. Second, the issuance and distribution of Zero Pay helps to revitalize the local economy and contribute to the establishment of a virtuous cycle system. Third, stable operation is being realized by the introduction of blockchain technology to the Zero Pay platform. In terms of academic significance, the direction of Zero Pay's policies and systems was able to identify changes in the use of Zero Pay through big data analysis. The implementation of the zero-pay policy is in its infancy, and there are limitations in factors for examining the consumer image perception of zero-pay as there are insufficient prior studies. Therefore, continuous follow-up research on Zero Pay should be conducted.