• Journal of Korean Institute of Industrial Engineers
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• v.23 no.3
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• pp.609-619
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• 1997

The concept of human emotion is recently demanded to be imbedded in industrial product and environment for enhancing quality of life. Human emotion is attempted to be qualified and quantified by physiological measurements. EEG variation, one of the physiological measurement, is observed to characterize psychological response in this study. This study is to find function and process of brain according to emotion. Twenty university students participated in this study and experienced positive and negative emotion by auditory stimulus. Delta, theta, alpha and beta waves showed characteristic variation in normalized sense according to positive and negative emotion. Local area showing significant difference between positive and negative emotion decreases with stimulus duration. Delta, theta and beta waves increase with negative emotion while alpha wave does with positive emotion.

• Journal of Chest Surgery
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• v.29 no.1
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• pp.52-58
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• 1996

Sixty six patients who were operated as lung cancer during the period from Mar. 1991 to Sep. 1993 at the department of Thoracic and cardiovascular surgery, were reviewed retrospectively and the accuracy of regional lymph node in preoperative CT were compared with histopathologlc report obtained from operation. The age ranged from 30 to 72 years old (mean age : 56.5), and 51 patients were male and 15 patients were female. The author analysed the true positive, true negative, false positive and false negative and sensitivity, specificity, positive predictive index, negative predictive index and accuracy of each nodes. The result is that there were differences between seven nodal groups in specificity, sensitivity, positive predictive Index, negative predictive index and accuracy. The range of each nodal group is from 81.7 to 98.3% The nodes of the most poor accuracy are aortopulmonary area and hilar area.

• The Journal of Information Systems
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• v.28 no.4
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• pp.271-291
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• 2019

Purpose The purpose of this study is to propose a methodology to derive positive keywords and negative keywords through deep learning to classify reviews into positive reviews and negative ones, and then refine the results of topic modeling using these keywords. Design/methodology/approach In this study, we extracted topic keywords by performing LDA-based topic modeling. At the same time, we performed attention-based deep learning to identify positive and negative keywords. Finally, we refined the topic keywords using these keywords as filters. Findings We collected and analyzed about 6,000 English reviews of Gyeongbokgung, a representative tourist attraction in Korea, from Tripadvisor, a representative travel site. Experimental results show that the proposed methodology properly identifies positive and negative keywords describing major topics.

• Journal of the Korean Home Economics Association
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• v.41 no.11
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• pp.189-200
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• 2003

The purpose of this study was to investigate that positive and negative word-of-mouth informations getting hairshop customers have influence on visiting intention of potential consumers. Data were collected from 354 university or college women. The results showed as follows; First, positive word-of-mouth informations that consumers have experienced in using hairshop were employee altitude/technique, consideration in customer's situation, kindness, saving of time/additional service, facilities, rational price, gift service/benefit in conditions of location. Second, negative word-of-mouth informations that consumers have experienced in using hairshop were inconsistent service, service focus on non-customers, irrational price/technique insufficiency/ inadequate compensational system, irrelevance of face-to-face management. Third, in getting positive word-of-mouth informations, consideration in customer's situation, rational price and gift service/benefit in conditions of location, consumers had visiting intention, and in getting negative informations, irrational price/technique insufficiency/inadequate compensational system, consumers had no visiting intention.

• Bulletin of the Korean Chemical Society
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• v.34 no.11
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• pp.3261-3264
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• 2013

Carbon nanocomposites composed of carbon nanostructures and metal nanoparticles have become one of useful materials for various applications. Here we present the preparation and antibacterial activity of CNT-Ag and GO-Ag nanocomposites. Their physical properties were characterized by TEM, XPS, and Raman measurements, revealing that size-similar and quasi-spherical Ag nanoparticles were anchored to the surface of the CNT and GO. The antibacterial activities of CNT-Ag and GO-Ag were investigated using the growth curve method and minimal inhibitory concentrations against Gram-negative and Gram-positive bacteria. The antibacterial activities of the carbon nanocomposites were slightly different against Gram-positive and Gram-negative bacteria. The proposed mechanism was discussed.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.1
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• pp.87-97
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• 2019

Reaction of people is importantly considered about specific case as a social network service grows. In the previous research on analysis of social network service, they predicted tendency of interesting topic by giving scores to sentences written by user. Based on previous study we proceeded research of sentiment analysis for social network service's sentences, which predict the result as positive or negative for movie reviews. In this study, we used movie review to get high accuracy. We classify the movie review into positive or negative based on the score for learning. Also, we performed embedding and morpheme analysis on movie review. We could predict learning result as positive or negative with a number 0 and 1 by applying the model based on learning result to social network service. Experimental result show accuracy of about 80% in predicting sentence as positive or negative.

• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.161-169
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• 2021

The two-dimensional confusion matrix used in credit assessment, biostatistics, and many other fields consists of true positive, true negative, false positive, and false negative. Their rates, such as the true positive rate (TPR), true negative rate (TNR), false positive rate, and false negative rate, can be applied to measure its accuracy. In this study, we propose the TPR-TNR plot, a graphical method that can geometrically describe and explain these rates based on the confusion matrix. The proposed TPR-TNR plot consists of two right-angled triangles. We obtain that the TPR and TNR describe the acute angles of right-angled triangles in the plot. These acute angles can be used to determine optimal thresholds corresponding to lots of accuracy measures.

• Asia Marketing Journal
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• v.18 no.2
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• pp.25-44
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• 2016

This research was implemented by using TRAM model. An existing research (Ok 2011) which dealt with technology readiness and social commerce at once had only asked consumers' attitude on 'general technology'. This research, however, has specifically focused on Social Network Service and mobile social commerce. Research hypotheses and research model were developed and tested by using 610 consumer survey data. It was found that individual's positive/negative technology readiness has a direct influence positively/negatively on perceived ease of use and perceived trust respectively. Also their positive and negative technology readiness has an indirect influence positively/negatively on perceived usefulness. Thus someone's positive and negative attitude on SNS has a different direction towards the perception of mobile social commerce. Perception on mobile social commerce depends on their attitude (positive or negative) concerning SNS. Managerial implications and limitations of the study were also discussed.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.101-109
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• 2023

This paper discusses the use of sentiment analysis and text summarization techniques to extract valuable information from the large volume of user-generated content such as reviews, comments, and feedback on online platforms and social media. The paper highlights the effectiveness of sentiment analysis in identifying positive and negative reviews and the importance of summarizing such text to facilitate comprehension and convey essential findings to readers. The proposed work focuses on summarizing all positive and negative reviews to enhance product quality, and the performance of the generated summaries is measured using ROUGE scores. The results show promising outcomes for the developed methods in summarizing user-generated content.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.418-421
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• 2023

본 논문은 Word2Vec를 이용하여 한국어 증상 기반 질병 예측 모델을 제시한다. 아산병원 질환 백과의 크롤링 데이터를 세 가지 형식으로 나누어, 모델에 알맞은 데이터 형식을 찾고 모델에 적용한다. 가장 모델에 맞는 데이터 형식은 증상별 질병과 질병별 증상을 합친 경우이다. 데이터의 양을 늘려 임베딩 스페이스를 넓혔고, 가장 중요한 증상과 질병의 유사도도 정확하게 출력되었다. 이는 유사도가 높은 질병과 증상들이 제대로 학습이 되었다는 것을 알 수 있다. 이렇게 만들어진 예측 모델에 positive 증상을 입력하면 유사도가 향상되고, negative에 입력하면 하락하는 결과를 확인했다. 따라서 환자의 증상을 positive에 넣으면, 그 증상을 가진 질병이 가까워지는 반면, 환자의 증상이 아닌 증상을 negative에 넣으면, 환자에게 맞지 않는 질병이 멀어진다. 그러므로 환자의 상태에 맞는 질병을 유추해, 의사나 환자가 증상에 대한 질병을 알고 싶을 때 또는 검색에 유용하게 사용할 수 있다. 더불어, 질병의 진료과 데이터를 추가하여, 환자에게 맞는 진료과를 찾는 데도 도움을 줄 수 있다.