• Information Systems Review
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• v.26 no.2
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• pp.205-221
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• 2024

In June 2023, the term "spatial computing" began gaining recognition among the public with Apple's Vision Pro announcement, and interest surged exponentially after its official release in February 2024. With the market opening up, there's a need to analyze public perception for sustainable growth of Spatial Computing and provide evidence-based strategies for industry and government response. This study explores domestic public perception of Spatial Computing using various text mining techniques and seeks strategic directions for successful market penetration based on the analysis. Significantly, the study contributes by leading research on Spatial Computing, proposing new research methodologies, and offering strategic and policy directions for stakeholders.

• Journal of Fashion Business
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• v.26 no.3
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• pp.138-154
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• 2022

The purpose of this study is to identify the characteristics of domestic American casual fashion trends using big data text mining analysis. 108,524 posts and 2,038,999 extracted keywords from Naver and Daum related to American casual fashion in the past 5 years were collected and refined by the Textom program, and frequency analysis, word cloud, N-gram, centrality analysis, and CONCOR analysis were performed. The frequency analysis, 'vintage', 'style', 'daily look', 'coordination', 'workwear', 'men's wear' appeared as the main keywords. The main nationality of the representative brands was Japanese, followed by American, Korean, and others. As a result of the CONCOR analysis, four clusters were derived: "general American casual trend", "vintage taste", "direct sales mania", and "American styling". This study results showed that Japanese American casual clothes are influenced by American casual clothes, and American casual fashion in Korea, which has been reinterpreted, is completed with various coordination and creative styles such as workwear, street, military, classic, etc., focusing on items and brands. Looks were worn and shared on social networks, and the existence of an active consumer group and market potential to obtain genuine products, ranging from second-hand transactions for limited edition vintages to individual transactions were also confirmed. The significance of this study is that it presented the characteristics of American casual fashion trends academically based on online text data that the public actually uses because it has been spread by the public.

• Science of Emotion and Sensibility
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• v.26 no.1
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• pp.101-114
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• 2023

This research aimed to recognize the determinant characteristics affecting consumers' clothing fit evaluation by employing text mining and social network analysis. For this aim, we first extracted text data linked to clothing fit from 2,000 consumer reviews collected from social network services and conducted semantic network examination and CONCOR analysis. As a result, we reported that "pants" and "skirts" were the most commonly associated clothing items with consumers' clothing fit evaluation. And the length of clothing was most commonly investigated. Then, the "waist" and "hip" were the most critical body parts affecting consumers' perception of clothing fit. Further, the four keywords including "wide," "large," "short," and "long" were the most employed ones in consumer reviews when evaluating clothing fit. This study is meaningful in that it specifically recognized the structural relationship and semantic meanings of keywords relevant to consumers' evaluation of clothing fit, which could bring empirical reference information for advanced clothing fit.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.1-10
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• 2021

This paper aimed to look at the awareness of foreign workers in social platforms by using text mining, one of the big data techniques and draw suggestions for foreign workers. To achieve this purpose, data collection was conducted with search keyword 'Foreign Worker' from Jan. 1, to Dec. 31, 2020, and frequency analysis, TF-IDF analysis, and degree centrality analysis and 100 parent keywords were drawn for comparison. Furthermore, Ucinet6.0 and Netdraw were used to analyze semantic networks, and through CONCOR analysis, data were clustered into the following eight groups: foreigner policy issue, regional community issue, business owner's perspective issue, employment issue, working environment issue, legal issue, immigration issue, and human rights issue. Based on such analyzed results, it identified national awareness of foreign workers and main issues and provided the basic data on policy proposals for foreign workers and related researches.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.29 no.2
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• pp.85-108
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• 2018

This study aimed to analyze media reports of the Comprehensive Library Advancement Plan using big data analysis in order to determine trends and implications by period. To do so, related data from 2009 to 2017 were collected from major domestic web portal sites. Words in the collected data were refined through the text mining process and frequency, centrality, and structural equivalence analyses were performed. Results confirmed that, during the implementation of the first and the second phases of the Comprehensive Library Advancement Plan, the focus of the library policy changed from external growth to strengthening internal stability and advancement of library operation, and the media coverage were limited to specific policies such as expansion of library facilities. Findings from this study will serve as useful material for ascertaining the approach to perceive and understand the national library policy represented by the Comprehensive Library Advancement Plan.

• The Journal of Society for e-Business Studies
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• v.26 no.4
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• pp.95-118
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• 2021

As the AI speaker business has risen significantly in recent years, the potential for numerous uses of AI speakers has gotten a lot of attention. Consumers have created an environment in which they can express and share their experiences with products through various channels, resulting in a large number of reviews that leave consumers with a variety of candid opinions about their experiences, which can be said to be very useful in analyzing consumers' thoughts. Using this review data, this study aimed to examine the factors driving the continued use of AI speakers. Above all, it was determined whether the seven characteristics associated with the intention to adopt AI identified in prior studies appear in consumer reviews. Based on customer review data on Amazon.com, text mining and social network analysis were utilized to examine Amazon eco-products. CONCOR analysis was used to classify words with similar connectivity locations, and Connection centrality analysis was used to classify the factors influencing the continuous use of AI speakers, focusing on the connectivity between words derived by classifying review data into positive and negative reviews. Consumers regarded personality and closeness as the most essential characteristics impacting the continued usage of AI speakers as a result of the favorable review survey. These two parameters had a strong correlation with other variables, and connectedness, in addition to the components established from prior studies, was a significant factor. Furthermore, additional negative review research revealed that recognition failures and compatibility are important problems that deter consumers from utilizing AI speakers. This study will give specific solutions for consumers to continue to utilize Amazon eco products based on the findings of the research.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.97-109
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• 2024

In the field of domestic tourism, tourism trend analysis of tourism consumers, both international tourists and domestic tourists, is essential not only for the Korean tourism market but also for local and governmental tourism policy makers. e will explore the keywords and sentiment analysis on social media to establish a marketing strategy plan and revitalize the domestic tourism industry through communication and information from tourism consumers. This study utilized TEXTOM 6.0 to analyze recent trends in Korean tourism. Data was collected from September 31, 2022, to August 31, 2023, using 'Korean tourism' and 'domestic tourism' as keywords, targeting blogs, cafes, and news provided by Naver, Daum, and Google. Through text mining, 100 key words and TF-IDF were extracted in order of frequency, and then CONCOR analysis and sentiment analysis were conducted. For Korean tourism keywords, words related to tourist destinations, travel companions and behaviors, tourism motivations and experiences, accommodation types, tourist information, and emotional connections ranked high. The results of the CONCOR analysis were categorized into five clusters related to tourist destinations, tourist information, tourist activities/experiences, tourism motivation/content, and inbound related. Finally, the sentiment analysis showed a high level of positive documents and vocabulary. This study analyzes the rapidly changing trends of Korean tourism through text mining on Korean tourism and is expected to provide meaningful data to promote domestic tourism not only for Koreans but also for foreigners visiting Korea.

• Journal of the Korea Convergence Society
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• v.11 no.6
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• pp.145-151
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• 2020

'Hansik', or Korean cuisine is one of Korea national brands. To understand the domestic consumer awareness of Korean cuisine, data was gathered under the keyword search, 'Hansik.' Textom 3.5 was used to gather data from blogs, news media found on Naver from November 1, 2018, to October 31, 2019. The results from frequency and TF-IDF analysis indicate that the 'buffet' had the largest proportion in terms of consumer awareness to Hansik. Also, broadcasting contents starring star chefs had a great influence. The Hansik awareness did not remain in the domains of its traditionality, but also branched into extents into areas such as fusional and gourmet cuisine. UCINET6 and NetDraw were used to conduct CONCOR analysis. Four cluster formations have been found; various food cultural cluster, high-end restaurant cluster referring to aired restaurants on media, Hansik brand cluster, and Hansik buffet cluster. This study proposes presenting a various menu of Hansik which use a multiple number of ingredients. Also, a promotion that introduces fine Hansik and a development of marketing views and media contents about the convenient HMRs make the associated imagery of Hansik to be strengthen.

• The Journal of the Korea Contents Association
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• v.22 no.6
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• pp.319-328
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• 2022

The purpose of this study was to investigate the relationship between depression due to the COVID-19 pandemic and park use behaviors using on line posts. During the period of the pandemic prevention activities, text data containing both 'park' and 'depression' were collected from blogs and cafes in the search engine of Naver and Daum, then analyzed using Text Mining and Social Network techniques. As a result, the main usage behaviors of park users who mentioned depression were 'look', 'stroll(walk)' and 'eat'. Other types of behaviors were connected centering around 'look', one of the communication behaviors. Also, from CONCOR analysis, as the cluster referred from communication behavior and dynamic behavior was formed as a single behavior type, it was considered park users with depression perceived the park as the space for communication and physical activities. As the spread of COVID-19 caused the restriction of communication activities, the users might consider parks as one of the solutions. In addition, it was considered that passive usage behaviors have prevailed rather than active ones due to the depression. Resulting outcomes would be useful to plan helpful urban park for citizens. It is necessary to further analyze the park use behavior of users in relation to the period of before/after the COVID-19 pandemic and the existence/nonexistence of depression.

• Journal of The Korean Association of Information Education
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• v.26 no.6
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• pp.553-566
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• 2022

This study collects social data and academic research data from portal sites and RISS, and analyzes TF-IDF, N-Gram, semantic network analysis, and CONCOR analysis to analyze the social awareness and current aspects of 'AI Literacy' and 'AI Convergence Education'. Through this, we tried to understand the social awareness aspect and the current situation, and to suggest implications and directions. In the social data, the collection of 'AI Convergence Education' was more than twice that of 'AI Literacy', indicating that awareness of 'AI Literacy' was relatively low. In 'AI Literacy', the keyword 'human' in social data showed no cluster to which it belonged, indicating a lack of philosophical interest in and awareness of humanities and AI. In addition, the keyword 'Ministry of Education' showed high frequency, importance, and centrality of connection only in the social data of 'AI convergence education', confirming that 'AI convergence education' is closely related to government policy.