• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.347-373
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• 2023

As the significance of ESG (Environmental, Social, and Governance) management amplifies in driving sustainable growth, this study delves into and compares ESG trends and interrelationships from both corporate and societal viewpoints. Employing a combination of Latent Dirichlet Allocation Topic Modeling (LDA) and Semantic Network Analysis, we analyzed sustainability reports alongside corresponding social media datasets. Additionally, an in-depth examination of social media content was conducted using Joint Sentiment Topic Modeling (JST), further enriched by Semantic Network Analysis (SNA). Complementing text mining analysis with the assistance of ChatGPT, this study identified 25 different ESG topics. It highlighted differences between companies aiming to avoid risks and build trust, and the general public's diverse concerns like investment options and working conditions. Key terms like 'greenwashing,' 'serious accidents,' and 'boycotts' show that many people doubt how companies handle ESG issues. The findings from this study set the foundation for a plan that serves key ESG groups, including businesses, government agencies, customers, and investors. This study also provide to guide the creation of more trustworthy and effective ESG strategies, helping to direct the discussion on ESG effectiveness.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.11-21
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• 2022

All-solid-state batteries are one of the promising candidates for next-generation batteries and are drawing attention as a key component that will lead the future electric vehicle industry. This study analyzes 10,280 comments on Reddit, which is a global social media, in order to identify policy issues and public interest related to all-solid-state batteries from 2016 to 2021. Text mining such as frequency analysis, association rule analysis, and topic modeling, and sentiment analysis are applied to the collected global data to grasp global trends, compare them with the South Korean government's all-solid-state battery development strategy, and suggest policy directions for its national research and development. As a result, the overall sentiment toward all-solid-state battery issues was positive with 50.5% positive and 39.5% negative comments. In addition, as a result of analyzing detailed emotions, it was found that the public had trust and expectation for all-solid-state batteries. However, feelings of concern about unresolved problems coexisted. This study has an academic and practical contribution in that it presented a text mining analysis method for deriving key issues related to all-solid-state batteries, and a more comprehensive trend analysis by employing both a top-down approach based on government policy analysis and a bottom-up approach that analyzes public perception.

• Journal of Cadastre & Land InformatiX
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• v.46 no.1
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• pp.75-86
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• 2016

This study examines the social issues between the central and local governments related with the underground space after happenings of sinkholes in Jamsil area in July, 2014. In this study, we consider the keyword network of the social network analysis as a research methodology. The social issues regarding the underground space have been dealt with through the analysis of the centrality and group density to know the attributes of the network. The results show that the government has been steadily helpful to the local governments for establishing the socialized law for the underground space. This research suggests that the laws and technologies as to the underground space issues cooperate each other in the future. It also shows that the government should enact the policies and the national plans for the development of the underground.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.51-58
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• 2018

As the amount of users and data of NS explosively increased, research based on SNS Big data became active. In social mining, Latent Dirichlet Allocation(LDA), which is a typical topic model technique, is used to identify the similarity of each text from non-classified large-volume SNS text big data and to extract trends therefrom. However, LDA has the limitation that it is difficult to deduce a high-level topic due to the semantic sparsity of non-frequent word occurrence in the short sentence data. The BTM study improved the limitations of this LDA through a combination of two words. However, BTM also has a limitation that it is impossible to calculate the weight considering the relation with each subject because it is influenced more by the high frequency word among the combined words. In this paper, we propose a technique to improve the accuracy of existing BTM by reflecting semantic relation between words.

• The Journal of Bigdata
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• v.5 no.1
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• pp.55-63
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• 2020

This study proposed an approach of clustering collective users' activities of location-based social networks using check-in data of Foursquare users in Seoul. In order to cluster the collective activities, we generated sequential rules of the activities using sequential rule mining, and then constructed activity networks based on the rules. We analyzed the activity networks to identify network structure and hub activities, and clustered the activities within the networks. Unlike previous studies that analyzed activity transition patterns of location-based social network users, this study focused on analyzing the structure and clusters of successive activities. Hubs and clusters of activities with the approach proposed in this study can be used for location-based services and marketing. They could also be used in the public sector, such as infection prevention and urban policies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1199-1205
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• 2021

In the era of big data, not only structured data well organized in databases, but also the Internet, social network services, it is very important to effectively analyze unstructured big data such as web documents, e-mails, and social data generated in real time in mobile environment. Big data analysis is the process of creating new value by discovering meaningful new correlations, patterns, and trends in big data stored in data storage. We intend to summarize and visualize the analysis results through frequency analysis of unstructured article data using R language, a big data analysis tool. The data used in this study was analyzed for total 104 papers in the Mon-May 2021 among the journals of the Korea Institute of Information and Communication Engineering. In the final analysis results, the most frequently mentioned keyword was "Data", which ranked first 1,538 times. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.

• The Research Journal of the Costume Culture
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• v.32 no.2
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• pp.215-231
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• 2024

The changing media landscape has diversified how and what is discussed about fashion. This study aims to examine expert discussions about fashion shows on social media from the perspective of fashion criticism. To achieve this goal objectively, a text mining program, Leximancer, was used. In total, 58 videos were collected from the panel discussion section of Showstudio from S/S 21 to S/S 24, and the results of text mining on 24,080 collected texts after refinement are detailed here. First, the researchers examined the frequency of keywords by season. This revealed that in 2021-2022, digital transformation, diversity, and fashion films are now commonly used to promote fashion collections, often replacing traditional catwalk shows. From 2023, sustainability and virtuality appeared more frequently, and fashion brands focused on storytelling to communicate seasonal concepts. In S/S 2024, the rise of luxury brand keywords and an increased focus on consumption has been evident. This suggests that it is influenced by social and cultural phenomena. Second, the overall keywords were analyzed and categorized into five concepts: formal descriptions and explanations of the collection's outfits, sociocultural evaluations of fashion shows and designers, assessments of the commerciality and sustainability of the current fashion industry, interpretations of fashion presentations, and discussions of the role of fashion shows in the future. The significance of this study lies in its identification of the specificity of contemporary fashion criticism and its objective approach to critical research.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.311-321
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• 2024

This study comprehensively analyzed the perceptions of Chinese consumers who have and have not purchased Samsung smartphones, based on data from the social media platform Weibo. Various big data analysis techniques were used, including text mining, frequency analysis, centrality analysis, semantic network analysis, and CONCOR analysis. The results indicate that positive perceptions of Samsung smartphones include aspects such as design aesthetics, camera functionality, AI features, screen quality, specifications, and performance, and their status as a premium brand. On the other hand, negative perceptions include issues with pricing, a yellow tint in photos, slow charging speeds, and safety concerns. These findings will provide a crucial basis for making significant improvements in Samsung's market strategy in China.

• Journal of the Korean Society for information Management
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• v.36 no.4
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• pp.207-226
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• 2019

Depression is a serious psychological disease that is expected to afflict an increasing number of people. And studies on depression have been conducted in the context of social media because social media is a platform through which users often frankly express their emotions and often reveal their mental states. In this study, large amounts of Korean text were collected and analyzed to determine whether such data could be used to detect depression in users. This study analyzed data collected from Twitter users who had and did not have depressive tendencies between January 2016 and February 2019. The data for each user was separately analyzed before and after the appearance of depressive tendencies to see how their expression changed. In this study the data were analyzed through co-occurrence word analysis, topic modeling, and sentiment analysis. This study's automated data collection method enabled analyses of data collected over a relatively long period of time. Also it compared the textual characteristics of users with depressive tendencies to those without depressive tendencies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.129-130
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• 2018

Big data analysis is a technique for effectively analyzing unstructured data such as the Internet, social network services, web documents generated in the mobile environment, e-mail, and social data, as well as well formed structured data in a database. The most big data analysis techniques are data mining, machine learning, natural language processing, and pattern recognition, which were used in existing statistics and computer science. Global research institutes have identified analysis of big data as the most noteworthy new technology since 2011. Therefore, companies in most industries are making efforts to create new value through the application of big data. In this study, we analyzed using the Social Matrics which a big data analysis tool of Daum communications. We analyzed public perceptions of "Artificial Intelligence" keyword, one month as of May 19, 2018. The results of the big data analysis are as follows. First, the 1st related search keyword of the keyword of the "Artificial Intelligence" has been found to be technology (4,122). This study suggests theoretical implications based on the results.