• The Journal of the Korea Contents Association
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• v.14 no.1
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• pp.30-39
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• 2014

The purpose of this study is to suggest the feasible strategies to vitalize e-learning through investigating the characteristics of e-learning and the evaluations of distance learners on online courses in the domestic universities in Korea. First, in order to accomplish this, 10 Universities and 17 Cyber Universities were selected to explore their characteristics and main projects of e-learning for the administration level investigation. Secondly, content analysis of the bulletin board systems(BBS) and in-depth interviews on distance learners in Cyber Universities were conducted for the user level investigation. The results revealed that Universities in Korea were focused on establishing mobile or smart campuses, diversifying online educational contents, enhancing online interactive systems, and educating e-learning system and smart device utilization. However, distance learners reported that mobile e-learning lacked stability when taking online courses despite its convenience for purpose of academic administration. In addition, distance learners requested the social application workshops to improve on their learning experience as well as the interactions among peers. Therefore, it is important to focus more on how to establish the education-oriented e-learning environment rather than how to implement the administrative projects to animate e-learning in the domestic universities in Korea.

• Journal of the Korean Society for information Management
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• v.39 no.2
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• pp.111-129
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• 2022

Insomnia is a chronic disease in modern society, with the number of new patients increasing by more than 20% in the last 5 years. Insomnia is a serious disease that requires diagnosis and treatment because the individual and social problems that occur when there is a lack of sleep are serious and the triggers of insomnia are complex. This study collected 5,699 data from 'insomnia', a community on 'Reddit', a social media that freely expresses opinions. Based on the International Classification of Sleep Disorders ICSD-3 standard and the guidelines with the help of experts, the insomnia corpus was constructed by tagging them as insomnia tendency documents and non-insomnia tendency documents. Five deep learning language models (BERT, RoBERTa, ALBERT, ELECTRA, XLNet) were trained using the constructed insomnia corpus as training data. As a result of performance evaluation, RoBERTa showed the highest performance with an accuracy of 81.33%. In order to in-depth analysis of insomnia social data, topic modeling was performed using the newly emerged BERTopic method by supplementing the weaknesses of LDA, which is widely used in the past. As a result of the analysis, 8 subject groups ('Negative emotions', 'Advice and help and gratitude', 'Insomnia-related diseases', 'Sleeping pills', 'Exercise and eating habits', 'Physical characteristics', 'Activity characteristics', 'Environmental characteristics') could be confirmed. Users expressed negative emotions and sought help and advice from the Reddit insomnia community. In addition, they mentioned diseases related to insomnia, shared discourse on the use of sleeping pills, and expressed interest in exercise and eating habits. As insomnia-related characteristics, we found physical characteristics such as breathing, pregnancy, and heart, active characteristics such as zombies, hypnic jerk, and groggy, and environmental characteristics such as sunlight, blankets, temperature, and naps.

• Journal of Korea Game Society
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• v.21 no.6
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• pp.75-86
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• 2021

Brand image refers to how customers, stakeholders and the market see and recognize the brand. A positive brand image leads to continuous purchases, but a negative brand image is directly linked to consumers' buying behavior, such as stopping purchases, so from the corporate perspective, it needs to be quickly and accurately identified. Currently, methods of investigating brand images include surveys and SNS surveys, which have limited number of samples and are time-consuming and costly. Therefore, in this study, we are going to conduct an emotional analysis of text data on social media by utilizing the machine learning based KoBERT model, and then suggest how to use it for game corporate brand image analysis and verify its performance. The result has proved some degree of usability showing the same ranking within five brands when compared with the BRI Korea's brand reputation ranking.

• Journal of Digital Convergence
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• v.18 no.11
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• pp.19-29
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• 2020

The study aims to examine the effectiveness of flipped learning teaching methods by using learning analytics to enable effective programming learning for non-major students. After designing a flipped learning programming class model applied with the ADDIE model, learning-related data of the lecture support system operated by the school was processed with crawling. By providing data processed with crawling through a dashboard so that the instructor can understand it easily, the instructor can design classes more efficiently and provide individually tailored learning based on this. As a result of analysis based on the learning-related data collected through one semester class, it was found that the department, academic year, attendance, assignment submission, and preliminary/review attendance had an effect on academic achievement. As a result of survey analysis, they responded that the individualized feedback of instructors through learning analysis was very helpful in self-directed learning. It is expected that it will serve as an opportunity for instructors to provide a foundation for enhancing teaching activities. In the future, the contents of social network services related to learners' learning will be processed with crawling to analyze learners' learning situations.

• Journal of KIISE
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• v.43 no.1
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• pp.47-53
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• 2016

While the Internet develops rapidly, a huge amount of image data collected from smart phones, digital cameras and black boxes are being shared through social media sites. Generally, social images are handled by tagging them with information. Due to the ease of sharing multimedia and the explosive increase in the amount of tag information, it may be considered too much hassle by some users to put the tags on images. Image retrieval is likely to be less accurate when tags are absent or mislabeled. In this paper, we suggest a method of extracting tags from social images by using image content. In this method, CNN(Convolutional Neural Network) is trained using ImageNet images with labels in the training set, and it extracts labels from instagram images. We use the extracted labels for automatic image tagging. The experimental results show that the accuracy is higher than that of instagram retrievals.

• KIISE Transactions on Computing Practices
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• v.22 no.11
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• pp.619-624
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• 2016

Social-aware video displays not only the relationships between characters but also diverse information on topics such as economics, politics and culture as a story unfolds. Particularly, the speaking habits and behavioral patterns of people in different situations are very important for the analysis of social relationships. However, when dealing with this dynamic multi-modal data, it is difficult for a computer to analyze the drama data effectively. To solve this problem, previous studies employed the deep concept hierarchy (DCH) model to automatically construct and analyze social networks in a TV drama. Nevertheless, since location knowledge was not included, they can only analyze the social network as a whole in stories. In this research, we include location knowledge and analyze the social relations in different locations. We adopt data from approximately 4400 minutes of a TV drama Friends as our dataset. We process face recognition on the characters by using a convolutional- recursive neural networks model and utilize a bag of features model to classify scenes. Then, in different scenes, we establish the social network between the characters by using a deep concept hierarchy model and analyze the change in the social network while the stories unfold.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.241-251
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• 2022

Diagnosis and management of customer's skin condition is an important essential function in the cosmetics and beauty industry. As the social media environment spreads and generalizes to all fields of society, the interaction of questions and answers to various and delicate concerns and requirements regarding the diagnosis and management of skin conditions is being actively dealt with in the social media community. However, since social media information is very diverse and atypical big data, an intelligent skin condition diagnosis system that combines appropriate skin condition information analysis and artificial intelligence technology is necessary. In this paper, we developed the skin condition diagnosis system SCDIS to intelligently diagnose and manage the skin condition of customers by processing the text analysis information of social media into learning data. In SCDIS, an artificial neural network model, AnnTFIDF, that automatically diagnoses skin condition types using artificial neural network technology, a deep learning machine learning method, was built up and used. The performance of the artificial neural network model AnnTFIDF was analyzed using test sample data, and the accuracy of the skin condition type diagnosis prediction value showed a high performance of about 95%. Through the experimental and performance analysis results of this paper, SCDIS can be evaluated as an intelligent tool that can be used efficiently in the skin condition analysis and diagnosis management process in the cosmetic and beauty industry. And this study can be used as a basic research to solve the new technology trend, customized cosmetics manufacturing and consumer-oriented beauty industry technology demand.

• Information Systems Review
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• v.21 no.1
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• pp.23-38
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• 2019

The social network service (SNS) is one of the important marketing channels, so many companies actively exploit SNSs by posting SNS messages with appropriate content and style for their customers. In this paper, we focused on the psychological distances embedded in the SNS messages and developed a method to measure the psychological distance in SNS message by mixing a traditional content analysis, natural language processing (NLP), and machine learning. Through a traditional content analysis by human coding, the psychological distance was extracted from the SNS message, and these coding results were used for input data for NLP and machine learning. With NLP, word embedding was executed and Bag of Word was created. The Support Vector Machine, one of machine learning techniques was performed to train and test the psychological distance in SNS message. As a result, sensitivity and precision of SVM prediction were significantly low because of the extreme skewness of dataset. We improved the performance of SVM by balancing the ratio of data by upsampling technique and using data coded with the same value in first content analysis. All performance index was more than 70%, which showed that psychological distance can be measured well.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.105-111
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• 2018

STEAM and S / W education in public education are effective in fostering talented people and the talents of the 4th industrial revolution era. It is necessary to expand the teachers for this purpose, to find out and apply various learning materials, and to improve education environment for fusion talent education. An open learning platform is effective in reducing private education costs and supplementing public education. Especially, it is useful for flip learning combined with classroom (off-line). In this case, teacher's role can be transformed into active teaching activities and research activities, which can speed up normalization of public education and reduce private education.In particular, the core functions of the MOOC platform for elementary education are 'creative instructional design and contents development function', 'digital teaching and learning curation', 'big data based learner customization', 'learning participation' flip learning and social Learning function.Through this study, it is expected that discussion on the introduction of MOOC for career and admission education for adolescents including elementary education will be established and the Korean youth MOOC platform will be developed and developed as a global advanced model of education democratization.

• The Journal of Bigdata
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• v.6 no.2
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• pp.169-179
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• 2021

Consumers' food consumption behavior is likely to be affected not only by structured data such as consumer panel data but also by unstructured data such as mass media and social media. In this study, a deep learning-based consumption prediction model is generated and verified for the fusion data set linking structured data and unstructured data related to food consumption. The results of the study showed that model accuracy was improved when combining structured data and unstructured data. In addition, unstructured data were found to improve model predictability. As a result of using the SHAP technique to identify the importance of variables, it was found that variables related to blog and video data were on the top list and had a positive correlation with the amount of paprika purchased. In addition, according to the experimental results, it was confirmed that the machine learning model showed higher accuracy than the deep learning model and could be an efficient alternative to the existing time series analysis modeling.