• Journal of Digital Convergence
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• v.14 no.12
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• pp.173-182
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• 2016

AI (Artificial Intelligence) has attracted interest as a key element for technological advancement in various fields. In Korea, internet companies are leading the development of AI business technology. Active government funding plans for AI technology has also drawn interest. But not everyone is optimistic about AI. Both positive and negative opinions coexist about AI. However, attempts on analyzing people's opinions about AI in a quantitative way was scarce. In this study, we used text mining on SNS (Social Networking Service) to collect opinions about AI. And then we performed a comparative analysis about whether people view it as a positive thing or a negative thing and performed a comparative analysis to recognize popular key-words. Based on the results, it was confirmed that the change of key-words and negative posts have increased through time. And through these results, we were able to predict trend about AI.

• The Research Journal of the Costume Culture
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• v.27 no.6
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• pp.645-653
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• 2019

Laundry services are becoming more specialized and diversified. Therefore, this study investigated consumers' perceptions of professional laundry shops by analyzing social media data. For this purpose, text data from blogs, cafés, and Q&A sections ('Ji-Sik-In') on the portal site, naver.com, was collected. Sixty-four keywords were extracted from 2,213 social texts and transformed into a one-mode matrix using KrKwic, a program for the analysis of Korean text. Semantic network analysis was conducted to understand the network structure and the results were visualized using NodeXL. Keywords included fashion items and materials that require specialized professional laundry services, words related to the establishment of laundry shops, and laundry shop brands. Essential keywords of professional laundry shops included 'luxury,' 'footwear,' 'removal,' 'bag,' 'leather,' 'sneakers,' 'padding,' 'premium,' 'dyeing,' and 'franchise.' These results could be used to deduce that consumers perceive a professional laundry shop as a franchise shop offering specialized professional laundry services. A cluster analysis was conducted to identify the types of consumer perceptions of professional laundry shops. The network was divided into three groups: 'specialized professional laundry service,' 'laundry and repair of winter coats and jackets,' and 'the establishment of a professional laundry shop.' According to the results, consumers perceive professional laundry shops as franchises that offer specialized professional laundry services rather than general laundry services. Therefore, professional laundry shops need a strategy to develop special laundry services that differentiate them from other companies and communicate with consumers about these services.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.203-217
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• 2020

The purpose of this study was to perform a convergence study for investigating main features and influencing factors in career development process, throughout the whole periods of education, that might influence their ultimate choice of majors. We collected data of career development process at the elementary, middle, high school, and college levels using career-o-grams, for the college students who majored in English Lang/Lit and Global Commerce, and we applied text mining techniques for qualitative data analysis. Two major factors influencing career goals were parents and teachers. In particular, teachers were most influential in the career decisions at the middle school level. Teachers, family situations, and peers showed a negative impact on career aspiration. The findings would serve as a guide for career consultants and education program developers.

• KIPS Transactions on Computer and Communication Systems
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• v.1 no.1
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• pp.21-28
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• 2012

In this paper, both content analysis and Romey analysis were employed to analyze three 'General Computer' textbooks used in commerce and information high schools. The content analysis was employed to study the organization and contents of the textbooks, whereas the Romey analysis to determine the inquisitive tendency of four sections - Text, Data, Activity and Evaluation. The results from the content analysis showed that textbooks differed in the number of subsections, the number of pages in each section, and the number of concepts introduced in each section. Also, results from the Romey analysis demonstrated that the section Text in all the textbooks was written with a low level of inquisitive tendency, but also that they differed in that two other sections Data and Activity in one textbook exhibited a high level of inquisitive tendency while the other section Evaluation in two textbooks did. Using the aforementioned results, we proposed ways to improve the readability of 'General Computer' textbooks.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.181-188
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• 2021

Research and importance of legal services applied with AI so that it can be easily understood and predictable in difficult legal fields is increasing. In this study, based on the decision of the Tax Tribunal in the field of tax law, a model was built through self-learning through information collection and data processing, and the prediction results were answered to the user's query and the accuracy was verified. The proposed model collects information on tax decisions and extracts useful data through web crawling, and generates word vectors by applying Word2Vec's Fast Text algorithm to the optimized output through NLP. 11,103 cases of information were collected and classified from 2017 to 2019, and verified with 70% accuracy. It can be useful in various legal systems and prior research to be more efficient application.

• 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.

• The Journal of Society for e-Business Studies
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• v.23 no.4
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• pp.19-39
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• 2018

Recently, fake news has attracted worldwide attentions regardless of the fields. The Hyundai Research Institute estimated that the amount of fake news damage reached about 30.9 trillion won per year. The government is making efforts to develop artificial intelligence source technology to detect fake news such as holding "artificial intelligence R&D challenge" competition on the title of "searching for fake news." Fact checking services are also being provided in various private sector fields. Nevertheless, in academic fields, there are also many attempts have been conducted in detecting the fake news. Typically, there are different attempts in detecting fake news such as expert-based, collective intelligence-based, artificial intelligence-based, and semantic-based. However, the more accurate the fake news manipulation is, the more difficult it is to identify the authenticity of the news by analyzing the news itself. Furthermore, the accuracy of most fake news detection models tends to be overestimated. Therefore, in this study, we first propose a method to secure the fairness of false news detection model accuracy. Secondly, we propose a method to identify the authenticity of the news using the social data broadly generated by the reaction to the news as well as the contents of the news.

• Journal of Industrial Convergence
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• v.17 no.1
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• pp.1-6
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• 2019

Various cryptographic technologies have been proposed from ancient times and are developing in various ways to ensure the confidentiality of information. Due to exponentially increasing computer power, the encryption key is gradually increasing for security. Technology are being developed; however, security is guaranteed only in a short period of time. With the advent of the 4th Industrial Revolution, encryption technology is required in various fields. Recently, encryption technology using homomorphic encryption has attracted attention. Security threats arise due to the exposure of keys and plain texts used in the decryption processing for the operation of encrypted information. The homomorphic encryption can compute the data of the cipher text and secure process the information without exposing the plain text. When using the homomorphic encryption in processing big data like stored personal information in various services, security threats can be avoided because there is no exposure to key usage and decrypted information.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.8
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• pp.301-310
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• 2021

The TATI model is a Traffic Accident Text to RGB Image model, which is a methodology proposed in this paper for predicting the severity of traffic accidents. Traffic fatalities are decreasing every year, but they are among the low in the OECD members. Many studies have been conducted to reduce the death rate of traffic accidents, and among them, studies have been steadily conducted to reduce the incidence and mortality rate by predicting the severity of traffic accidents. In this regard, research has recently been active to predict the severity of traffic accidents by utilizing statistical models and deep learning models. In this paper, traffic accident dataset is converted to color images to predict the severity of traffic accidents, and this is done via CNN models. For performance comparison, we experiment that train the same data and compare the prediction results with the proposed model and other models. Through 10 experiments, we compare the accuracy and error range of four deep learning models. Experimental results show that the accuracy of the proposed model was the highest at 0.85, and the second lowest error range at 0.03 was shown to confirm the superiority of the performance.

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.1-11
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• 2021

Document page segmentation is an important step in building a quality optical character recognition module. The study examined already existing work on the topic of page segmentation and focused on the development of a segmentation model that has greater functional significance for application in an organization, as well as broad capabilities for managing the quality of the model. The main problems of document segmentation were highlighted, which include a complex background of intersecting objects. As classes for detection, not only classic text, table and figure were selected, but also additional types, such as signature, logo and table without borders (or with partially missing borders). This made it possible to pose a non-trivial task of detecting non-standard document elements. The authors compared existing neural network architectures for object detection based on published research data. The most suitable architecture was RetinaNet. To ensure the possibility of quality control of the model, a method based on neural network modeling using the RetinaNet architecture is proposed. During the study, several models were built, the quality of which was assessed on the test sample using the Mean average Precision metric. The best result among the constructed algorithms was shown by a model that includes four neural networks: the focus of the first neural network on detecting tables and tables without borders, the second - seals and signatures, the third - pictures and logos, and the fourth - text. As a result of the analysis, it was revealed that the approach based on four neural networks showed the best results in accordance with the objectives of the study on the test sample in the context of most classes of detection. The method proposed in the article can be used to recognize other objects. A promising direction in which the analysis can be continued is the segmentation of tables; the areas of the table that differ in function will act as classes: heading, cell with a name, cell with data, empty cell.