• The Journal of Society for e-Business Studies
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• v.19 no.4
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• pp.1-19
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• 2014

Efforts to identify user's recognition which exists in the big data are being conducted actively. They try to measure scores of people's view about products, movies and social issues by analyzing statements raised on Internet bulletin boards or SNS. So this study deals with the problem of determining how to find the emotional vocabulary and the degree of these values. The survey methods are using the results of previous studies for the basic emotional vocabulary and degree, and inferring from the dictionary's glosses for the extended emotional vocabulary. The results were found to have the 4 emotional words lists (vocabularies) as basic emotional list, extended 1 stratum 1 level list from basic vocabulary's glosses, extended 2 stratum 1 level list from glosses of non-emotional words, and extended 2 stratum 2 level list from glosses' glosses. And we obtained the emotional degrees by applying the weight of the sentences and the emphasis multiplier values on the basis of basic emotional list. Experimental results have been identified as AND and OR sentence having a weight of average degree of included words. And MULTIPLY sentence having 1.2 to 1.5 weight depending on the type of adverb. It is also assumed that NOT sentence having a certain degree by reducing and reversing the original word's emotional degree. It is also considered that emphasis multiplier values have 2 for 1 stratum and 3 for 2 stratum.

• Journal of the Korea Convergence Society
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• v.9 no.11
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• pp.227-235
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• 2018

The aim of this study is to predict the sensitivity of motion sickness (MS) using pattern of cardinal gaze position (CGP) before experiencing the virtual reality (VR) content. Twenty volunteers of both genders (8 females, mean age $28.42{\pm}3.17$) participated in this experiment. They was required to measure the pattern of CGP for 5 minute, and then watched VR content for 15 minute. After watching VR content, subjective experience for MS reported from participants using by 'Simulator Sickness Questionnaire (SSQ)'. Statistical significance between CGP and SSQ score were confirmed using Pearson correlation analysis and independent t-test, and prediction model was extracted from multiple regression model. PCPA & PCPR indicators from CGP revealed significantly difference and strong or moderate positive correlation with SSQ score. Extracted prediction model was tested using correlation coefficient and mean error, SSQ score between subjective rating and prediction model showed strong positive correlation and low difference.

• Smart Media Journal
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• v.9 no.4
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• pp.144-151
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• 2020

In a mobile environment, communication takes place via SMS text messages. Vocabularies used in SMS texts can be expected to use vocabularies of different classes from those used in general Korean literary style sentence. For example, in the case of a typical literary style, the sentence is correctly initiated or terminated and the sentence is well constructed, while SMS text corpus often replaces the component with an omission and a brief representation. To analyze these vocabulary usage characteristics, the existing colloquial style corpus and the literary style corpus are used. The experiment compares and analyzes the vocabulary use characteristics of the colloquial corpus SMS text corpus and the Naver Sentiment Movie Corpus, and the written Korean written corpus. For the comparison and analysis of vocabulary for each corpus, the part of speech tag adjective (VA) was used as a standard, and a distinctive collexeme analysis method was used to measure collostructural strength. As a result, it was confirmed that adjectives related to emotional expression such as'good-','sorry-', and'joy-' were preferred in the SMS text corpus, while adjectives related to evaluation expressions were preferred in the Naver Sentiment Movie Corpus. The word embedding was used to automatically construct a sentiment lexicon based on the extracted adjectives with high collostructural strength, and a total of 343,603 sentiment representations were automatically built.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.1-36
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• 2020

This study is to reveal the acceptance factors of the Market Sentiment Index (MSI) created by reflecting the investor sentiment extracted by processing unstructured big data. The research model was established by exploring exogenous variables based on the rational behavior theory and applying the Technology Acceptance Model (TAM). The acceptance of MSI provided to investors in the stock market was found to be influenced by the exogenous variables presented in this study. The results of causal analysis are as follows. First, self-efficacy, investment opportunities, Innovativeness, and perceived cost significantly affect perceived ease of use. Second, Diversity of services and perceived benefits have a statistically significant impact on perceived usefulness. Third, Perceived ease of use and perceived usefulness have a statistically significant effect on attitude to use. Fourth, Attitude to use statistically significantly influences the intention to use, and the investment opportunities as an independent variable affects the intention to use. Fifth, the intention to use statistically significantly affects the final dependent variable, the intention to use continuously. The mediating effect between the independent and dependent variables of the research model is as follows. First, The indirect effect on the causal route from diversity of services to continuous use intention was 0.1491, which was statistically significant at the significance level of 1%. Second, The indirect effect on the causal route from perceived benefit to continuous use intention was 0.1281, which was statistically significant at the significance level of 1%. The results of the multi-group analysis are as follows. First, for groups with and without stock investment experience, multi-group analysis was not possible because the measurement uniformity between the two groups was not secured. Second, the analysis result of the difference in the effect of independent variables of male and female groups on the intention to use continuously, where measurement uniformity was secured between the two groups, In the causal route from usage attitude to usage intention, women are higher than men. And in the causal route from use intention to continuous use intention, males were very high and showed statistically significant difference at significance level 5%.

• Journal of Animal Environmental Science
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• v.11 no.3
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• pp.153-160
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• 2005

This study was conducted to determine the effect of floor type of finishing building and increasing market weight of finishing Pigs on Performance, carcass traits and pork quality. Four hundred and forty-four finishing pigs were confined and administered with different floor type(concrete slat and bedded with sawdust) and increasing market weight(110kg to 130kg) of finishing building. The result obtained from this study were summarized as follows; 1. There was no significant difference between the floor type of finishing building in the body weight gain, feed intake and gain per feed. And also increasing market weight of finishing pigs was not affected the performance of finishing pigs. 2. Increasing market weight of finishing pigs affect the carcass yield. The market weight at 130kg showed more amount of each cut of carcass, especially the belly portion was higher, but backfat thickness was not different. 3. Carcass traits did not show any significant difference due to the difference of market weight of finishing pig and floor type of finishing building. 4. There was no significant difference in the chemical compositions and meat color of pork loin between the floor type of finishing building and increasing market weight of finishing pigs.

• Journal of Soil and Groundwater Environment
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• v.10 no.6
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• pp.20-31
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• 2005

Three dimensional Monte-Carlo simulations of non-ergodic transport of a lion-reactive solute plume by steady-state groundwater flow under a uniform mean velocity in isotropic heterogeneous aquifers were conducted. The log-normally distributed hydraulic conductivity, K(x), is modeled as a random field. Significant efforts are made to reduce tile simulation uncertainties. Ensemble averages of the second spatial moments of the plume and plume centroid variances were simulated with 1600 Monte Carlo runs for three variances of log K, ${\sigma}_Y^2=0.09,\;0.23$, and 0.46, and three dimensionless lengths of line plume sources normal to the mean velocity. The simulated second spatial moment and the plume centroid variance in longitudinal direction fit well to the first order theoretical results while the simulated transverse moments are generally larger than the first order results. The first order theoretical results significantly underestimated the simulated dimensionless transverse moments for the aquifers of large ${\sigma}_Y^2$ and large dimensionless time. The ergodic condition for the second spatial moments is far from reaching in all cases simulated, and transport In transverse directions may reach ergodic condition much slower than that in longitudinal direction. The evolution of the contaminant transported in a heterogeneous aquifer is not affected by the shape of the initial plume but affected mainly by the degree of the heterogeneity and the size of the initial plume.

• Archives of design research
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• v.18 no.1 s.59
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• pp.91-104
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• 2005

In the past, the design education focused on the technique and skill to satisfy the production and consumption activities of a corporation based on beauty and practicality in the industrial age. Now, it's time to switchover to a life-quality enhancing education by harmonizing and meeting the public's physical and mental needs because design education is a character education which enhances the quality of life by uplifting people's aesthetic sense. This paper has emphasized on the importance of public education of design through the theoretical investigation on social environmental changes caused by the emergence of information society,,education problems, public education, and the necessity of design education. The reason why this study should be done has been suggested by investigating the necessity of this research and bringing up the issue. Furthermore, the current status and problems of public education on design have been analyzed. Then, based on the result, the development direction of design education has been suggested. This study can be concluded as follows: First, the design education should change from its vertical structure to a horizontal one. It should be widely spread to the public, getting off from its privatization for a certain group. Second, designers and the public should correct their way of thinking about design. The ordinary people as well as the designers should cultivate their capability to find and take care of design related issues in their everyday lives. Third, all people should be the subject of design education for the public. As a part of cultural education on the public's aesthetic sense, design education should be reborn as a field of study in which a sound public culture can be developed by the integration of human life and culture, exceeding the limit of school curriculum.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.191-206
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• 2022

Recently, it is a de-facto approach to utilize a pre-trained language model(PLM) to achieve the state-of-the-art performance for various natural language tasks(called downstream tasks) such as sentiment analysis and question answering. However, similar to any other machine learning method, PLM tends to depend on the data distribution seen during the training phase and shows worse performance on the unseen (Out-of-Distribution) domain. Due to the aforementioned reason, there have been many efforts to develop domain-specified PLM for various fields such as medical and legal industries. In this paper, we discuss the training of a finance domain-specified PLM for the Korean language and its applications. Our finance domain-specified PLM, KB-BERT, is trained on a carefully curated financial corpus that includes domain-specific documents such as financial reports. We provide extensive performance evaluation results on three natural language tasks, topic classification, sentiment analysis, and question answering. Compared to the state-of-the-art Korean PLM models such as KoELECTRA and KLUE-RoBERTa, KB-BERT shows comparable performance on general datasets based on common corpora like Wikipedia and news articles. Moreover, KB-BERT outperforms compared models on finance domain datasets that require finance-specific knowledge to solve given problems.