• Journal of Information Management
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• v.35 no.4
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• pp.1-16
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• 2004

In this paper, we outline the user interface problems that the text messaging users face to provide empirical basis for developing better improved mobile text messaging system. Our initial hypothesis was that the majority of the problems that the text messaging users face, namely, 1) difficulty in correctly understanding the intent of the incoming messages and 2) problem with frequently mis-addressing the recipient of the outgoing messages, can be accounted for by the poor usability of the text messaging user interface. Our analysis is based on the text message-based communication diaries, which were recorded for one week by each and every one of 75 college students, and survey taken from the same subjects. The data was collected in 2004. The students listed various difficulties including the limited message length, obscure input method, lack of mean to express emotional content, lack of receipt confirmation, lack of auto save feature when preparing messages to send, and lack of means to permanently save messages. Some of these problems were also identified in the previous studies. However, we were able to gather additional problems that the users face and also elicit potential solutions to remedy the problems. From these findings and analysis, we attempted to provide ways to improve the text messaging user interface.

• Information Systems Review
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• v.26 no.1
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• pp.269-288
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• 2024

The main objective of this study is to empirically explore how the organizational culture influences financial performance of companies. To achieve this, 58 companies included in the KOSPI 200 were selected from an online job platform in South Korea, JobPlanet. In order to understand the organizational culture of these companies, data was collected and analyzed from 81,067 reviews written by current and former members of these companies on JobPlanet over a period of 9 years from 2014 to 2022. To define the organizational culture of each company based on the review data, this study utilized well-known text analysis techniques, namely Word2Vec and FastText analysis methods. By modifying, supplementing, and extending the keywords associated with the five organizational culture values (Innovation, Integrity, Quality, Respect, and Teamwork) defined by Guiso et al. (2015), this study created a new Culture Dictionary. By using this dictionary, this study explored which cultural values-related keywords appear most often in the review data of each company, revealing the relative strength of specific cultural values within companies. Going a step further, the study also investigated which cultural values statistically impact financial performance. The results indicated that the organizational culture focusing on innovation and creativity (Innovation) and on customers and the market (Quality) positively influenced Tobin's Q, an indicator of a company's future value and growth. For the indicator of profitability, ROA, only the organizational culture emphasizing customers and the market (Quality) showed statistically significant impact. This study distinguishes itself from traditional surveys and case analysis-based research on organizational culture by analyzing large-scale text data to explore organizational culture.

• The Research Journal of the Costume Culture
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• v.8 no.3
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• pp.493-511
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• 2000

The objective of this study was to trace and to examine costume terminologies recorded in Pak Tong Sa Eun Hae. Names of costumes and costume related terms were collected from P마 Tong Sa Eun Hae. Books and various references from China and Korea were used for this study. Costume terms were examined from the Chinese and Korean. Classifications of costume terminologies from the data were made for the analysis : man and woman's costume, accessories, names of fabrics, colors, and decorative motifs used, professional costume, special occasion dresses and so on. Conclusions and summary of results and findings can be summerized as follows : It revealed that manes of man's costume and other costume related words were a large in number compared with those of woman. Only one name of woman's costume appeared in the text : It was kind of long vests. However, names of accessories such as a hat, a hat decorated with jewels and phoenix design, a hair pin, earings, bracelets, finger rings, a soft belt were shown in the text. While many costume names of man included in the text were of garments such as a kind of long vest, a short vest, an outer robe, a kind of long coat with pleated skirt, leg coverings, outer jacket and so on. Also names of undergarments such as an under skirt, a belly covering, and drawers were found in the text. Fabric names were mostly silks such as brocade, twill, sarcenet, damasks and plain silks. Blue was the most widely appeared fabric color in the text and red was the second. Design motifs of fabric design were of dragon, flowers, eight precious things, clouds which were characteristic design motifs of the Chinese. It was found that some of the Chinese costume terminologies were translated into the Korean although many Chinese costume terms were used as the original Chinese.

• Journal of the Korean Society for information Management
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• v.33 no.3
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• pp.155-176
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• 2016

This study aims to develope a scholarly metadata information system based on conceptual elements of text structure of Korean studies research articles and to identify the applicability of text structure based metadata as compared with the existing similar system. For the study, we constructed a database(Korean Studies Metadata Database, KMD) with text structure based on metadata of Korean Studies journal articles selected from the Korea Citation Index(KCI). Then we verified differences between KCI system and KMD system through search results using same keywords. As a result, KMD system shows the search results which meet the users' intention of searching more efficiently in comparison with the KCI system. In other words, even if keyword combinations and conditional expressions of searching execution are same, KMD system can directly present the content of research purposes, research data, and spatial-temporal contexts of research et cetera as search results through the search procedure.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.25-31
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• 2020

This study is to study a method of applying weights for quality characteristics in software test evaluation. The weight application method analyzes the text of the test report and uses the ratio according to the frequency of the text as a weight for the quality characteristics of the software test score. The feasibility review of the results of this study was conducted by comparing the results of the questionnaire survey, which made the developers and users to evaluate the importance of software, and the results of the frequency analysis of text analysis. When measuring quality based on the eight quality characteristics presented in ISO/IEC 25023, the result of this study is the software quality measurement result considering software characteristics, whereas the result of this study is the software quality measurement result by applying the same weight when measuring quality.

• Journal of The Korean Society of Integrative Medicine
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• v.2 no.2
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• pp.79-88
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• 2014

Purpose: This study investigated the relationship between smartphone addiction propensities and compare muscle activity of the forearms and brain wave depend on the type of smartphone when inputting text messages. Method: We used an EMG to measure the change in muscle activity by attaching pads to the four muscles in both forearms of all 16 participants. We simultaneously conducted EEG measurements by observing the changes in alpha and beta waves recorded from electrode attached to both ears and the forehead of the participants. The participants had to input a given text using three different types of smartphones for ten minutes each. Result: The comparison of the EMG when inputting text involved a one way analysis of variance and the results showed that the iPad3 was highest for muscle activity followed by GALAXY Note2 and iPhone4. For EEG measurement, a one way analysis of variance was also used and the results showed iPhone4 was higest followed by GALAXY Note2 and finally iPad3 for EEG stress score. Conclusion: The results are thought to be used as reference data for smart phone users.

• The Journal of Society for e-Business Studies
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• v.14 no.4
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• pp.59-73
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• 2009

Keyword extraction is an important and essential technique for text mining applications such as information retrieval, text categorization, summarization and topic detection. A set of keywords extracted from a large-scale electronic document data are used for significant features for text mining algorithms and they contribute to improve the performance of document browsing, topic detection, and automated text classification. This paper presents a keyword extraction technique that can be used to detect topics for each news domain from a large document collection of internet news portal sites. Basically, we have used six variants of traditional TF-IDF weighting model. On top of the TF-IDF model, we propose a word filtering technique called 'cross-domain comparison filtering'. To prove effectiveness of our method, we have analyzed usefulness of keywords extracted from Korean news articles and have presented changes of the keywords over time of each news domain.

• Journal of Information Technology Applications and Management
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• v.25 no.1
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• pp.19-32
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• 2018

Fake news is defined as the news articles that are intentionally and verifiably false, and could mislead readers. Spread of fake news may provoke anxiety, chaos, fear, or irrational decisions of the public. Thus, detecting fake news and preventing its spread has become very important issue in our society. However, due to the huge amount of fake news produced every day, it is almost impossible to identify it by a human. Under this context, researchers have tried to develop automated fake news detection method using Artificial Intelligence techniques over the past years. But, unfortunately, there have been no prior studies proposed an automated fake news detection method for Korean news. In this study, we aim to detect Korean fake news using text mining and machine learning techniques. Our proposed method consists of two steps. In the first step, the news contents to be analyzed is convert to quantified values using various text mining techniques (Topic Modeling, TF-IDF, and so on). After that, in step 2, classifiers are trained using the values produced in step 1. As the classifiers, machine learning techniques such as multiple discriminant analysis, case based reasoning, artificial neural networks, and support vector machine can be applied. To validate the effectiveness of the proposed method, we collected 200 Korean news from Seoul National University's FactCheck (http://factcheck.snu.ac.kr). which provides with detailed analysis reports from about 20 media outlets and links to source documents for each case. Using this dataset, we will identify which text features are important as well as which classifiers are effective in detecting Korean fake news.

• Korean Medical Education Review
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• v.19 no.3
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• pp.158-168
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• 2017

There is a more urgent call for educational methods of machine learning in medical education, and therefore, new approaches of teaching and researching machine learning in medicine are needed. This paper presents a case using machine learning through text analysis. Topic modeling of news articles with the keyword 'asbestos' were examined. Two hypotheses were tested using this method, and the process of machine learning of texts is illustrated through this example. Using an automated text analysis method, all the news articles published from January 1, 1990 to November 15, 2016 in South Korea which included 'asbestos' in the title and the body were collected by web scraping. Differences in topics were analyzed by structured topic modelling (STM) and compared by press companies and periods. More articles were found in liberal media outlets. Differences were found in the number and types of topics in the articles according to the partisanship and period. STM showed that the conservative press views asbestos as a personal problem, while the progressive press views asbestos as a social problem. A divergence in the perspective for emphasizing the issues of asbestos between the conservative press and progressive press was also found. Social perspective influences the main topics of news stories. Thus, the patients' uneasiness and pain are not presented by both sources of media. In addition, topics differ between news media sources based on partisanship, and therefore cause divergence in readers' framing. The method of text analysis and its strengths and weaknesses are explained, and an application for the teaching and researching of machine learning in medical education using the methodology of text analysis is considered. An educational method of machine learning in medical education is urgent for future generations.

• Journal of Korean Society of Transportation
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• v.27 no.5
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• pp.63-74
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• 2009

One of the effective countermeasures for preventing traffic accidents is to provide traffic safety warning information to drivers. Provision of warning information would lead to safer driving to avoid accident occurrence. This study investigated the effects of in-vehicle warning information on driver's behavior. A variety of warning information contents using text, sound, and pictograms were prepared for the field experiments. Individual vehicle speed and acceleration data, which represent quantitative drivers' behavior in response to in-vehicle warning information, were collected using differential global positioning systems (DGPS). Statistical analyses including ANOVA and Tukey's pairwise comparison were conducted. It is expected that the results could be invaluable for designing more effective warning information.