• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.219-240
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• 2023

Conversational AI which allows users to interact with satisfaction is a long-standing research topic. To develop conversational AI, it is necessary to build training data that reflects real conversations between people, but current Korean datasets are not in question-answer format or use honorifics, making it difficult for users to feel closeness. In this paper, we propose a conversation dataset (KOMUChat) consisting of 30,767 question-answer sentence pairs collected from online communities. The question-answer pairs were collected from post titles and first comments of love and relationship counsel boards used by men and women. In addition, we removed abuse records through automatic and manual cleansing to build high quality dataset. To verify the validity of KOMUChat, we compared and analyzed the result of generative language model learning KOMUChat and benchmark dataset. The results showed that our dataset outperformed the benchmark dataset in terms of answer appropriateness, user satisfaction, and fulfillment of conversational AI goals. The dataset is the largest open-source single turn text data presented so far and it has the significance of building a more friendly Korean dataset by reflecting the text styles of the online community.

• Journal of Software Assessment and Valuation
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• v.17 no.1
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• pp.67-73
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• 2021

In this paper, a system configuration method that can practice Linux-based programming language online is presented. The proposed system utilizes the web-server function, which is the biggest feature of the Linux operating system, and simulates the telnet and FTP functions without firewalls or other security restrictions, so that it is possible to practice similar to the actual Linux console. To do this, we analyzed the functional elements that a programming tool should have on the web and established an algorithm to implement it. In particular, a method was implemented in which an error message caused by a user's mistake can appear in the same form as the actual telnet screen. As a result of using the implemented learning system in the class for students, it is possible to practice the Linux programming language online, as well as the instructor can directly check and guide all the learners, so the learner's satisfaction is similar to that of the offline class was confirmed.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.26 no.4
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• pp.263-282
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• 2015

Online education in the field of LIS has continued to spread out in university curricula or with collaborative online programs through consortia among universities. Unlike the traditional online education, however, MOOCs (Massive Open Online Courses) with the recent advent and advances have risen as a new paradigm in education of the future in that these are massive online learner-centered courses, free and open to any person with no limit on enrollment. With no exception to this phenomenon, the LIS field centered by overseas iSchool universities has been offering MOOCs for core LIS courses. This research conducted a case study of utilizing a part of overseas LIS MOOCs in a core LIS course at domestic University-A, in order to explore the potential for utilizing overseas MOOCs in LIS education. The results of conducting a survey and a focus group interview to students discovered that MOOCs content was interesting and useful and many of them were willing to take other MOOCs in the future, despite some language barriers. Based on these findings, this study suggested the need for establishing educational value, administering methods, ways to motivate students, and designing MOOCs by incorporating the characteristics of the LIS field, as ways to utilize MOOCs in LIS education.

• Journal of Distribution Science
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• v.22 no.4
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• pp.11-22
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• 2024

Purpose: This research delves into the various factors that influence the performance of restaurant businesses on social commerce platforms in Bangkok, Thailand. The study considers both internal and external factors, including but not limited to business characteristics and location. Moreover, this research also analyzes the effects of employing multiple social commerce platforms on business efficiency and explores the underlying reasons for such effects. Research design, data, and methodology: Restaurants can be classified into different price ranges: low, medium, and high. To further investigate, we employed natural language processing AI to analyze online reviews and evaluate algorithm performance using machine learning techniques. We aimed to develop a model to gauge customer satisfaction with restaurants across different price categories effectively. Results: According to the research findings, several factors significantly impact restaurant groups in the low and mid-price ranges. Among these factors are population density and the number of seats at the restaurant. On the other hand, in the mid-and high-price ranges, the price levels of the food and drinks offered by the restaurant play a crucial role in determining customer satisfaction. Furthermore, the correlation between different social commerce platforms can significantly affect the business performance of high-price range restaurant groups. Finally, the level of online review sentiment has been found to influence customer decision-making across all restaurant types significantly. Conclusions: The study emphasizes that restaurants' characteristics based on their price level differ significantly, and social commerce platforms have the potential to affect one another. It is worth noting that the sentiment expressed in online reviews has a more significant impact on customer decision-making than any other factor, regardless of the type of restaurant in question.

• Journal of the Korea Convergence Society
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• v.10 no.5
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• pp.119-124
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• 2019

The purpose of this study is to introduce the NHK"NEWS WEB EASY"online site to intermediate level learners of Japanese language, and to teach effective methods of Blended Learning through the lesson planning method using "NEWS WEB EASY" Suggesting. First, this paper helped students cultivate various vocabulary learning ability through Blended Learning using "NEWS WEB EAS Y", Second, helped them learn about Japanese culture and Japan through various articles, Third, helped them naturally perform listening training through listening files, Fourth, helped them practice reading Kanji and improve vocabulary skills by distributing the files without Furigana to them for search, and Fifth, showed them how to improve speaking ability by reading practice through learning using "NEWS WEB EASY". We could learn the fact that the study helped students a lot understand Japan and improve their Japanese ability by learning news articles that they could not come across due to prejudice through learning using "NEWS WEB EASY".

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.57-65
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• 2019

In the case of Domeggook B2B online shopping malls, it has a market share of over 70% with more than 2 million members and 800,000 items are sold per one day. However, since the same or similar items are stored and registered in different catalog entries, it is difficult for the buyer to search for items, and problems are also encountered in managing B2B large shopping malls. Therefore, in this study, we developed a catalog entry auto classification and recommendation system for products by using semi-supervised machine learning method based on previous huge shopping mall purchase information. Specifically, when the seller enters the item registration information in the form of natural language, KoNLPy morphological analysis process is performed, and the Naïve Bayes classification method is applied to implement a system that automatically recommends the most suitable catalog information for the article. As a result, it was possible to improve both the search speed and total sales of shopping mall by building accuracy in catalog entry efficiently.

• Journal of Practical Engineering Education
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• v.14 no.3
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• pp.555-563
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• 2022

This study explored the educational effects of e-PBL by using metaverse tools to strengthen PBL interactions among foreign learners. The university's 3-hour, 15-week PBL subject was systematically reorganized to satisfy the needs of online groups of students. Metaverse technology was also used as a tool for interaction in the process of solving practical problems closely related to our social issues through e-PBL. e-PBL lectures are composed of foreign learners from various countries. More than half of the 43 participating students are from 11 different nations. Learners in an e-PBL class are able to partake in task-based learning activities through the use of the metaverse. This qualitative study identified the metaverse is an effective communication tool which transcends language and nationality. It is also a unique space where both verbal and non-verbal communication between team members are possible online. This study can demonstrate the positive effects of e-PBL teaching methods. By using the metaverse's various tools of interaction to improve communication among foreign learners whose Korean levels are not perfect, we can create a digital space which more closely resembles an offline, interpersonal learning experience.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.685-695
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• 2023

This study aimed to investigate the current utilization and implications of digital learning support media in the field of French studies, and to explore future research directions. To achieve this, we conducted a comprehensive review of the use of digital media in various learning processes within French studies. Additionally, we examined the direct application of ChatGPT, an emerging technology, to learning by extending its use to foreign language and education fields. Our findings indicate that the application of digital learning support media in French studies is somewhat limited, with selective use in processes such as online class support media, pre-class learning, efficient learning and interaction, and self-directed learning. In the case of ChatGPT, our research found that no studies have been conducted within French studies, and very few studies have been conducted on its practical application in other educational fields. While ChatGPT has a wide range of applications and has shown positive effects on learners, ethical concerns have been raised regarding the quality, source, and reliability of information. Therefore, future research in French studies should focus on educational application and effectiveness verification in university teaching and learning situations, as well as interdisciplinary convergence with digital learning support media.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.19-33
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• 2022

With the explosive growth of social media, its abundant text-based data generated by web users has become an important source for data analysis. For example, we often witness online movie reviews from the 'Naver Movie' affecting the general public to decide whether they should watch the movie or not. This study has conducted analysis on the Naver Movie's text-based review data to predict the actual ratings. After examining the distribution of movie ratings, we performed semantics analysis using Korean Natural Language Processing. This research sought to find the best review rating prediction model by comparing machine learning and deep learning models. We also compared various regression and classification models in 2-class and multi-class cases. Lastly we explained the causes of review misclassification related to movie review data characteristics.

• Journal of Information Science Theory and Practice
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• v.2 no.3
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• pp.29-39
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• 2014

In recent years, several studies have proposed making use of the Twitter micro-blogging service to track various trends in online media and discussion. In this study, we specifically examine the use of Twitter to track discussions of food safety in the Korean language. Given the irregularity of keyword use in most tweets, we focus on optimistic machine-learning and feature set selection to classify collected tweets. We build the classifier model using Naive Bayes & Naive Bayes Multinomial, Support Vector Machine, and Decision Tree Algorithms, all of which show good performance. To select an optimum feature set, we construct a basic feature set as a standard for performance comparison, so that further test feature sets can be evaluated. Experiments show that precision and F-measure performance are best when using a Naive Bayes Multinomial classifier model with a test feature set defined by extracting Substantive, Predicate, Modifier, and Interjection parts of speech.