• KIPS Transactions on Software and Data Engineering
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• v.7 no.6
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• pp.229-238
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• 2018

The rapid response of disaster accidents can be archived through the organical involvement of various disaster and safety control agencies. To define the terminology of disaster safety is essential for communication between disaster safety agencies and well as announcement for the public. Also, to efficiently construct a word dictionary of disaster safety terminology, it's necessary to define the priority of the terms. In order to establish direction of word dictionary construction, this paper compares the usage of disaster safety terminology by media: word dictionary, new media, and social media, respectively. Based on the terminology resources collected from each media, we visualized the distribution of terminology according to frequency weights and analyzed co-occurrence patterns. We also classified the types of terminology into four categories and proposed the priority in the construction of disaster safety word dictionary.

• Knowledge Management Research
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• v.25 no.1
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• pp.173-192
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• 2024

This study aims to understand the changes in the beauty industry due to the pandemic from the consumer's perspective based on consumers' opinions about their skin online before and after the pandemic. Furthermore, this study tries to derive strategies for companies and governments to support sustainable growth and innovation in the beauty industry. To this end, posts on social media from 2017 to 2022 that contained the keyword 'skin concerns' are collected, and after data preprocessing, 96,908 posts are used for the structural topic model. To examine whether consumers' interest areas related to skin change according to the pandemic situation, the analysis period is divided into 7 periods, and the variables that distinguish each stage are used as meta-variables for the structural topic model. As a result, it is found that consumers' interests can be divided into 22 topics, which can be categorized into four main categories: beauty manufacturing, beauty services, skin concerns, and other. The results of this study are expected to be utilized in construction of product development and marketing strategies of related companies and the establishment of economic support policies by the government in response to changes in demand in the beauty industry due to the pandemic.

• Knowledge Management Research
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• v.23 no.4
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• pp.69-89
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• 2022

As we enter a super-aged society, senior citizens' health issues are affecting a variety of fields, including medicine, economics, society, and culture. In this study, we intend to draw implications from unstructured data analysis such as text mining and social network analysis in order to apply digital health care service design for improving the cognitive ability of senior citizens. The research procedure of this study improved the service design methodology into a process suited to the analysis of unstructured data, and six steps were applied. Related keywords that exist on social media, focusing on cognitive improvement and healthcare for senior citizens, were collected and analyzed, and based on these results, the direction of healthcare service design for improving on the cognitive abilities of senior citizens was derived. The results of this study are expected to have academic and practical implications for expanding the scope of the use of big data analysis methods and improving existing healthcare service development methodologies.

• Information Systems Review
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• v.25 no.4
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• pp.233-248
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• 2023

The increasing integration of intelligent information technologies within organizational systems has amplified the risk to personal information security. This escalation, in turn, has fueled growing apprehension about an organization's capabilities in safeguarding user data. While Internet users adopt a multifaceted approach in assessing a company's information security, existing research on the multiple dimensions of information security is decidedly sparse. Moreover, there is a conspicuous gap in investigations exploring whether users' evaluations of organizational information security differ across industry types. With an aim to bridge these gaps, our study strives to identify which information security attributes users perceive as most critical and to delve deeper into potential variations in these attributes across different industry sectors. To this end, we conducted a structured survey involving 498 users and utilized the analytic hierarchy process (AHP) to determine the relative significance of various information security attributes. Our results indicate that users place the greatest importance on the technological dimension of information security, followed closely by transparency. In the technological arena, banks and domestic portal providers earned high ratings, while for transparency, banks and governmental agencies stood out. Contrarily, social media providers received the lowest evaluations in both domains. By introducing a multidimensional model of information security attributes and highlighting the relative importance of each in the realm of information security research, this study provides a significant theoretical contribution. Moreover, the practical implications are noteworthy: our findings serve as a foundational resource for Internet service companies to discern the security attributes that demand their attention, thereby facilitating an enhancement of their information security measures.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.30-37
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• 2018

The social media data and the broadcasting data related to onion as well as agri-food consumer panel data were collected and investigated if the amount of money spent to purchase onion in year 2014 when onion price plunged latest were correlated with the frequencies of onion-related keywords in the social media data and the broadcasting programs because onion price in year 2018 is expected to plunge due to overproduction and there has been needs to analyze impacts of social media and broadcasting program on onion purchase in the previous similar events, and identify potential factors that can promote onion consumption in advance. What we identified from our study include a) broadcasting news programs mentioning words "onion," were correlated with onion purchase with 3 - 6 weeks in advance; b) broadcasting entertainment programs mentioning words "onion and health," were correlated with onion purchase with 11 weeks in advance; c) blog mentioning words "onion and efficacy," were correlated with onion purchase with 5 weeks in advance. Our study provided a case on how social media and broadcasting programs could be analyzed for their effects on consumer purchase behavior using big data collection and analysis in the field of agriculture. We propose to use the findings from the study may be applied to promote onion consumption.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.240-243
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• 2021

Recently, there have been research results of applying Big data and AI technologies to the evaluation and individual learning for education. It is information technology innovations that collect dynamic and complex data, including student personal records, physiological data, learning logs and activities, learning outcomes and outcomes from social media, MOOCs, intelligent tutoring systems, LMSs, sensors, and mobile devices. In addition, e-learning was generated a large amount of learning data in the COVID-19 environment. It is expected that learning analysis and AI technology will be applied to extract meaningful patterns and discover knowledge from this data. On the learner's perspective, it is necessary to identify student learning and emotional behavior patterns and profiles, improve evaluation and evaluation methods, predict individual student learning outcomes or dropout, and research on adaptive systems for personalized support. This study aims to contribute to research in the field of education by researching and classifying machine learning technologies used in anomaly detection and recommendation systems for educational data.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.21-41
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• 2019

Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.27 no.4
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• pp.63-84
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• 2016

This study aimed to investigate the extent that users participated in Facebook of U.S. state archives and the types of user responses to posts on the Facebook. For the purpose, data created between August 1st and September 30th in 2016 were collected from Facebook continuously operated by 27 state archives. The extent of user participation was measured based on the number of user comments, the number of unique commenters, and the average number of comments per post. According to the measures, top 10 Facebook of state archives were selected. Out of these, Facebook of Ohio (1st), Florida (5th) and Arkansas (10th) state archives were chosen to collect 687 user comments and 132 posts. The analysis showed that comments regarding users' emotional opinion and judgement, adding explanations to a post, and sharing personal stories occupied a large portion. Interactions among users or between a user and an archivist were also identified. With regard to posts, those for sharing information/knowledge of records held in archives were identified as a high percentage. The study suggested that archives should collect and present historical information and related records connected to users' lives, examine methods for effective communication with users via social media and facilitate publicity and outreach services of archives based on shaping and maintaining online user community through social media.

• Journal of the Korean Regional Science Association
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• v.39 no.1
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• pp.3-20
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• 2023

This study aims to categorize the 14 major gentrified commercial areas of Seoul and analyze their characteristics based on their sense of place. To achieve this, we conducted hierarchical cluster analysis using text data collected from Naver Blog. We divided the districts into two dimensions: "experience" and "feature" and analyzed their characteristics using LDA (Latent Dirichlet Allocation) of the text data and statistical data collected from Seoul Open Data Square. As a result, we classified the commercial districts of Seoul into 5 categories: 'theater district,' 'traditional cultural district,' 'female-beauty district,' 'exclusive restaurant and medical district,' and 'trend-leading district.' The findings of this study are expected to provide valuable insights for policy-makers to develop more efficient and suitable commercial policies.

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.91-117
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• 2022

The number of depressed patients in Korea and around the world is rapidly increasing every year. However, most of the mentally ill patients are not aware that they are suffering from the disease, so adequate treatment is not being performed. If depressive symptoms are neglected, it can lead to suicide, anxiety, and other psychological problems. Therefore, early detection and treatment of depression are very important in improving mental health. To improve this problem, this study presented a deep learning-based depression tendency model using Korean social media text. After collecting data from Naver KonwledgeiN, Naver Blog, Hidoc, and Twitter, DSM-5 major depressive disorder diagnosis criteria were used to classify and annotate classes according to the number of depressive symptoms. Afterwards, TF-IDF analysis and simultaneous word analysis were performed to examine the characteristics of each class of the corpus constructed. In addition, word embedding, dictionary-based sentiment analysis, and LDA topic modeling were performed to generate a depression tendency classification model using various text features. Through this, the embedded text, sentiment score, and topic number for each document were calculated and used as text features. As a result, it was confirmed that the highest accuracy rate of 83.28% was achieved when the depression tendency was classified based on the KorBERT algorithm by combining both the emotional score and the topic of the document with the embedded text. This study establishes a classification model for Korean depression trends with improved performance using various text features, and detects potential depressive patients early among Korean online community users, enabling rapid treatment and prevention, thereby enabling the mental health of Korean society. It is significant in that it can help in promotion.