• Economic and Environmental Geology
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• v.54 no.3
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• pp.353-364
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• 2021

Since the mid-twentieth century, geology has gradually evolved as an interdisciplinary context in South Korea. The journal of Economic and Environmental Geology (EEG) has a long history of over 52 years and published interdisciplinary articles based on geology. In this study, we performed a literature review using topic modeling based on Latent Dirichlet Allocation (LDA), an unsupervised machine learning model, to identify geological topics, historical trends (classic topics and emerging topics), and association by analyzing titles, keywords, and abstracts of 2,571 publications in EEG during 1968-2020. The results showed that 8 topics ('petrology and geochemistry', 'hydrology and hydrogeology', 'economic geology', 'volcanology', 'soil contaminant and remediation', 'general and structural geology', 'geophysics and geophysical exploration', and 'clay mineral') were identified in the EEG. Before 1994, classic topics ('economic geology', 'volcanology', and 'general and structure geology') were dominant research trends. After 1994, emerging topics ('hydrology and hydrogeology', 'soil contaminant and remediation', 'clay mineral') have arisen, and its portion has gradually increased. The result of association analysis showed that EEG tends to be more comprehensive based on 'economic geology'. Our results provide understanding of how geological research topics branch out and merge with other fields using a useful literature review tool for geological research in South Korea.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.109-119
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• 2024

This study aims to contribute to the initial research on on-device AI in an environment where generative AI-based services on mobile and other on-device platforms are increasing. To derive success strategies for generative AI-based chatbot services in a mobile environment, over 200,000 actual user experience review data collected from the Google Play Store were analyzed using the LDA topic modeling technique. Interpreting the derived topics based on the Information System Success Model (ISSM), the topics such as tutoring, limitation of response, and hallucination and outdated informaiton were linked to information quality; multimodal service, quality of response, and issues of device interoperability were linked to system quality; inter-device compatibility, utility of the service, quality of premium services, and challenges in account were linked to service quality; and finally, creative collaboration was linked to net benefits. Humanization of generative AI emerged as a new experience factor not explained by the existing model. By explaining specific positive and negative experience dimensions from the user's perspective based on theory, this study suggests directions for future related research and provides strategic insights for companies to improve and supplement their services for successful business operations.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.223-232
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• 2020

This study aims to examine the trend of research articles for the age of youth at 20s during 10 years through topic modeling and keyword network analysis. In results, 'incomes', 'program', 'business start-up', and 'culture' were selected as main keywords, and the research articles were classified into six topics, which were employment support services, values, unstable life, government support policies, religious views, and business start-up support services. Additionally, we found the youth at 20s had higher rate of efficacy for digital technology, pursued efficient consumption of digital information, showed meaningful and athetical patterns of consumption, tried to search for their identity, and showed realistic action in daily. Finally, we raised some questions for value gap among aging groups, inbalance of regional development, and income inequality and suggested long-term youth policies to solve fundamental problems of youth at 20s.

• Journal of Digital Convergence
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• v.15 no.9
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• pp.231-239
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• 2017

Solar energy is attracting attention as an alternative to fossil fuels. However, there was a lack of discussion on the overall research direction and future direction of research in technology development. In order to develop more effective technology, we analyzed and discussed the technology trend of solar energy using patent data and thesis data. As an analysis method, topics were selected by using topic modeling and text mining, the increase of included keywords was analyzed, and the direction of development of solar technology was analyzed. Research on solar power generation technology is expected to proceed steadily, and it is analyzed that intensive research will be done especially on high efficiency and high performance technology. Future studies could be conducted by adding overseas patent data and various paper data.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.430-440
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• 2021

We analyzed research trends of 288 Korean academic dissertations and articles regarding platform work, using topic modeling and keyword network analysis method. Research disciplines of many studies were laws, business administration, and economics fields. Thigh frequent themes were platform labor protection measures and direct or indirect effects of the sharing economy. The main keywords were digital, value, industry, and labor in terms of infrastructure and structural change. Besides, the main topics were the protection of platform workers, the values of sharing services, digital paradigm, and platform regulations. Based on the results of the analysis, we derived four implications and suggestions such as researching structural frames in macroscopic contexts, generalizing case analysis, and technology supplementation by applying average and quantitative analysis methods, researching individual competency development to realize the essential symbiotic value of sustainability, and developing customized vocational education and training programs.

• The Journal of Pediatrics of Korean Medicine
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• v.36 no.2
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• pp.26-39
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• 2022

Objectives The purpose of this study was to collect reported but scattered data on herbal medicine treatment for depression in adolescents and young adults and to establish future research directions for this topic. Methods Using the scoping review method, 10 Korean and foreign databases were searched for studies published up to March 22, 2022. Studies targeting children and adolescents diagnosed with depression, studies using herbal medicine treatments, and clinical studies were included. Results Twelve randomized clinical trials, two chart reviews, and six case reports were identified. Frequently used Korean medical patterns, treatment methods, herbal medicines, decoctions, treatment periods, and post-treatment evaluation results were analyzed. Differences in the confirmed results from actual clinical settings were reviewed, and the direction of follow-up research on this topic was suggested. Conclusions This study outlined the results of various levels of clinical research on herbal medicine treatment for depression in adolescents and young adults, showed the clinical availability of herbal medicine, and provided a foundation for future research.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.1-17
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• 2015

In this paper, we report what we have observed with regards to a prediction model for the military based on enlisted men's internal(cumulative records) and external data(SNS data). This work is significant in the military's efforts to supervise them. In spite of their effort, many commanders have failed to prevent accidents by their subordinates. One of the important duties of officers' work is to take care of their subordinates in prevention unexpected accidents. However, it is hard to prevent accidents so we must attempt to determine a proper method. Our motivation for presenting this paper is to mate it possible to predict accidents using enlisted men's internal and external data. The biggest issue facing the military is the occurrence of accidents by enlisted men related to maladjustment and the relaxation of military discipline. The core method of preventing accidents by soldiers is to identify problems and manage them quickly. Commanders predict accidents by interviewing their soldiers and observing their surroundings. It requires considerable time and effort and results in a significant difference depending on the capabilities of the commanders. In this paper, we seek to predict accidents with objective data which can easily be obtained. Recently, records of enlisted men as well as SNS communication between commanders and soldiers, make it possible to predict and prevent accidents. This paper concerns the application of data mining to identify their interests, predict accidents and make use of internal and external data (SNS). We propose both a topic analysis and decision tree method. The study is conducted in two steps. First, topic analysis is conducted through the SNS of enlisted men. Second, the decision tree method is used to analyze the internal data with the results of the first analysis. The dependent variable for these analysis is the presence of any accidents. In order to analyze their SNS, we require tools such as text mining and topic analysis. We used SAS Enterprise Miner 12.1, which provides a text miner module. Our approach for finding their interests is composed of three main phases; collecting, topic analysis, and converting topic analysis results into points for using independent variables. In the first phase, we collect enlisted men's SNS data by commender's ID. After gathering unstructured SNS data, the topic analysis phase extracts issues from them. For simplicity, 5 topics(vacation, friends, stress, training, and sports) are extracted from 20,000 articles. In the third phase, using these 5 topics, we quantify them as personal points. After quantifying their topic, we include these results in independent variables which are composed of 15 internal data sets. Then, we make two decision trees. The first tree is composed of their internal data only. The second tree is composed of their external data(SNS) as well as their internal data. After that, we compare the results of misclassification from SAS E-miner. The first model's misclassification is 12.1%. On the other hand, second model's misclassification is 7.8%. This method predicts accidents with an accuracy of approximately 92%. The gap of the two models is 4.3%. Finally, we test if the difference between them is meaningful or not, using the McNemar test. The result of test is considered relevant.(p-value : 0.0003) This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of enlisted men's data. Additionally, various independent variables used in the decision tree model are used as categorical variables instead of continuous variables. So it suffers a loss of information. In spite of extensive efforts to provide prediction models for the military, commanders' predictions are accurate only when they have sufficient data about their subordinates. Our proposed methodology can provide support to decision-making in the military. This study is expected to contribute to the prevention of accidents in the military based on scientific analysis of enlisted men and proper management of them.

• Asia pacific journal of information systems
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• v.15 no.2
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• pp.149-170
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• 2005

This study assessed diversity in MIS research by examining 357 articles published in Journal of MIS Research from 1991 to 2003. The classification system developed by Vessey et al.(2002) was employed to examine research diversity of the articles. The classification system comprises four key characteristics of diversity such as research topic, research method, level of analysis, and reference discipline. The results of our study were also compared with Vessey et al,(2002)'s results to address problems in current MIS research and to suggest some recommendations for the future MIS research in Korea.

• Journal of Research and Publication Ethics
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• v.4 no.1
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• pp.23-29
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• 2023

Purpose: While prior research has identified ethical issues in marketing research, more research needs to identify specific ethical behaviors that marketing researchers should adopt. This research addresses this gap by identifying a few ethical behaviors that marketing researchers should consider. Research design, data and methodology: The present study has a justification to collect adequate textual data in the current literature using screening process based on the marketing ethics topics and themes, and ethical behaviors of marketing researchers. Results: Based on the literature analysis, a marketing ethics model, which included the principles of fairness, honesty, responsibility, and respect for stakeholders. Since the rise of e-commerce and technology, a growing demand for a method to analyze website user behavior has been growing. Researchers in the field of marketing are responding to this demand by creating innovative interactive platforms. Conclusions: The term "ethical practices in market research" refers to a collection of best practices that, when followed, increase the likelihood that the research carries out is ethical, fair, and accurate. Because there needs to be more theoretical work done on the topic, there should be more empirical studies on ethical behaviors in marketing research.