• Science of Emotion and Sensibility
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• v.1 no.1
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• pp.105-111
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• 1998

중추신경계돠 자율신경계에 나타난 후각에 의한 감성변화를 측정하기 위해 후각자극은 0.6% 오렌지향과 2.5% valeric acid를 수증기로 포화시키는 후각 자극기를 통해 일정 flow와 농도로 시행하였다. 대조자극은 방안 공기로 향자극 전후에 시행하였으며 뇌파자극은 international 10-20 systim에서 4채널을 사용하였다. 이외에 ECG, EOG, heart rate, skin conductance와 respiration를 기록하였고 뇌파분석은 fast Fourier tuansform analysis의 power spectra로 하였다. 그 frequency banes는 delta(0-4.5Hz), theta(4.5-7Hz)은 쾌하고 친숙하게 valeric acid는 불쾌하고 성가시게 평가되었다. 뇌파분석에서 쾌와 불쾌 자극간의 차이는 PG2-A2 channel 에서 alphal의 자극전후의 차이를 나타내었으며 불유쾌한 자극에서는 모든 channel alphal, alpha2와 beta파 증가를 보였다. 또한 heart rate, galvaric skin resistance는 쾌자극에서 감소양상을 나타내었으며 불쾌자극에서는 반대경향을 보였다. 호흡에서는 쾌자극에서 호흡수 감소경향과 input/output amplitude dutation와 duration의 증가경향을 보였으며 불쾌자극에서는 반대양상을 보였다. 결론적으로 쾌와 불쾌 후각자극으로 감성변화를 뇌파와 자율신경계에서 볼 수 있었다.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.74-85
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• 2022

Along with the change in the values of marriage and the prevalence of non-marriage in Korean society, a new form of family composition called unmarried birth or non-maritalism childbirth has appeared, and social discussion in taking place in connection with the problem of a decrease in the birthrate. Using sentiment analysis and social network analysis, this research explored how the people's sentiment and perception has changed toward 'nonmarital birth.' The data used is comments on news articles from the period of November 2020 to August 2021. As a result of the study, there were a lot of positive comments during the social issue period by marriage, whereas there were many negative comments from the policy agenda to the policy making period. As a result of co-occurrence network analysis, the topic of family norm, policy, and personal aspect appeared. This study is significant in that it revealed that negative perceptions prevailed during the policy-making process after the issue of unmarried births after the issue of unmarried births, and it became a cornerstone of social discussion on unmarried births

• Science of Emotion and Sensibility
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• v.16 no.2
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• pp.157-166
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• 2013

There are few that evaluate tactile emotion as it pertains to car interior parts, while studies on visual evaluations of car interiors as well as usability tests in a visual sense are numerous. The purpose of this study is to determine typical in-vehicle tactile emotions so that they can be used to evaluate tactile impressions of car interior parts. 52 words related to tactile impressions of car interiors were gathered from a survey in conjunction with an in-vehicle test, interviews with the car salespersons, and an analysis of car reviews. After a factor analysis with 52 words, 10 categories of major tactile emotions were clustered. These were roughness, toughness, friction, comfortability, stiffness, softness, temperature, sleekness, familiarity, and flexibility. These representative tactile emotions regarding a car interior can be used to evaluate tactile impressions of surfaces such as leather, plastic, metal and wood when used as parts in car interiors.

• Journal of the Korea Society of Computer and Information
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• v.25 no.11
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• pp.41-50
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• 2020

Sentimental analysis begins with the search for words that determine the sentimentality inherent in data. Managers can understand market sentimentality by analyzing a number of relevant sentiment words which consumers usually tend to use. In this study, we propose exploring performance of feature selection methods embedded with Particle Swarm Optimization Multi Objectives Evolutionary Algorithms. The performance of the feature selection methods was benchmarked with machine learning classifiers such as Decision Tree, Naive Bayesian Network, Support Vector Machine, Random Forest, Bagging, Random Subspace, and Rotation Forest. Our empirical results of opinion mining revealed that the number of features was significantly reduced and the performance was not hurt. In specific, the Support Vector Machine showed the highest accuracy. Random subspace produced the best AUC results.

• Journal of Digital Convergence
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• v.12 no.10
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• pp.243-252
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• 2014

Many researches about the effect on politics, economics and Sociocultural phenomenon using the social media are in progress. Authors utilized NAVER Trend most famous web browsing service in korea, NAVER Blog social media, NAVER Cafe service and Open Data(API) and also used temperature, humidity index data of Korea Meteorological Administration. This study analyzed a change of the public's emotion in korea using Cluster analysis of vocabulary of taste among its of feelings and senses. K-means clustering was followed by decision of the number of groups which was used Chi-square goodness of fit test and ward analysis. Eight groups was made and it represented sensitive vocabulary. By Discriminant analysis, eight groups decided by Cluster analysis has 98.9% accuracy. The change of the public's emotion has capability to predict people's activity so they can share sensibility and a bond of sympathy developed between them.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.780-786
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• 2015

Social media, such as Social Network Service include a lot of spontaneous opinions from customers, so recent companies collect and analyze information about customer feedback by using the system that analyzes Big Data on social media in order to efficiently operate businesses. However, it is difficult to analyze data collected from online sites accurately with existing morpheme analyzer because those data have spacing errors and spelling errors. In addition, many online sentences are short and do not include enough meanings which will be selected, so established meaning selection methods, such as mutual information, chi-square statistic are not able to practice Emotional Classification. In order to solve such problems, this paper suggests a module that can revise the meanings by using initial consonants/vowels and phase pattern dictionary and meaning selection method that uses priority of word class in a sentence. On the basis of word class extracted by morpheme analyzer, these new mechanisms would separate and analyze predicate and substantive, establish properties Database which is subordinate to relevant word class, and extract positive/negative emotions by using accumulated properties Database.

• Journal of Korea Game Society
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• v.18 no.5
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• pp.113-122
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• 2018

This paper describes an approach to building and evaluating a sentiment dictionary using a Web corpus in the game domain. To build a sentiment dictionary, we collected vocabulary based on game-related web documents from a domestic portal site, using the Twitter Korean Processor. From the collected vocabulary, we selected the words whose POS are tagged as either verbs or adjectives, and assigned sentiment score for each selected word. To evaluate the constructed sentiment dictionary, we calculated F1 score with precision and recall, using Korean-SWN that is based on English Senti-word Net(SWN). The evaluation results show that average F1 scores are 0.85 for adjectives and 0.77 for verbs, respectively.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.243-246
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• 2011

학습자의 감성 상태가 충분히 반영되는 오프라인 수업과 달리 지금까지 대부분의 e-러닝은 학습자의 감성 정보를 수업에 효과적으로 반영하지 못했다. 이러한 한계점은 e-러닝의 학습 효과성을 저해하는 문제 중 하나로 지적되었다. 이 문제를 해결하기 위해 학습자의 뇌파를 통해 감성을 인식하고 감성 상태에 따라 적절한 학습 콘텐츠 타입을 추천하여 학습 효과를 증대 시킬 수 있는 방법론이 주목을 받고 있다. 본 논문에서는 기 수집된 학습자들의 감성(뇌파) 데이터를 분석하여 콘텐츠 타입 선호도를 파악한 후 프로파일 데이터를 활용하여 상관계수 기반 NN-Recommendation 학습 콘텐츠 타입 추천 시스템을 제안 하고자 한다. 이 시스템은 일반적인 추천시스템에서 발생하는 Cold-start 문제를 해결할 수 있으며 특히 본 연구에서는 보다나은 추천 정확도를 위해 프로파일 각 속성에 자동적으로 가중치를 부여하는 기법을 제시하여 향상된 성능을 보이게 됨을 실험을 통해 확인 하였다.

• Electronics and Telecommunications Trends
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• v.26 no.5
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• pp.83-91
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• 2011

감성적 제품이 소비를 자극하는 시대가 도래함에 따라, 선진기업은 성숙기 ICT 산업 돌파구로 감성전달 기술에 주목하여, 시장 차별화에 주력하고 있으며, 성능 및 가격 위주의 시장전략에서 사용자 편의성과 만족도를 극대화시키는 소비자 감성 지향형 산업으로 재편을 꾀하고 있다. 진보된 ICT 기술과 심미적인 라이프 스타일 디자인을 통해 사용자와 기기가 교감하는 디지털 인터페이스를 제공하는 감성 UX(User eXperience) 기술은 새로운 형태의 의사소통 방식과 감정전달 메커니즘을 제시하여 사회구성원의 현재 정서를 인지하고 감정의 정화/승화/억제를 보조하여, 가족 및 사회 네트워크 자원과의 유대감을 확고히 함으로써 구성원의 삶의 가치를 향상시키고, 유비쿼터스 환경과 문화 속에서 사람과 정보를 보다 밀접하게 이어주는 매개가 될 것으로 전망하고 있다. 본 고에서는 우리나라가 이미 글로벌 경쟁력을 갖고 있는 모바일 기기, 디지털 TV, 디스플레이를 중심으로, 미래 감성 ICT 생태계의 기반이 되는 감성 UX 기술의 현재의 모습을 살펴보고, 향후 발전 전망에 대해 기술한다.

• Science of Emotion and Sensibility
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• v.21 no.1
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• pp.177-186
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• 2018

The mechanism of emotion is complex and influenced by a variety of factors, so that it is crucial to analyze emotion in broad and diversified perspectives. In this study, we classified neutral and negative emotions(sadness, fear, surprise) using arousal evaluation, which is one of the psychological evaluation scales, as well as physiological signals. We have not only revealed the difference between physiological signals coupled to the emotions, but also assessed how accurate these emotions can be classified by our emotional recognizer based on neural network algorithm. A total of 146 participants(mean age $20.1{\pm}4.0$, male 41%) were emotionally stimulated while their physiological signals of the electrocardiogram, blood flow, and dermal activity were recorded. In addition, the participants evaluated their psychological states on the emotional rating scale in response to the emotional stimuli. Heart rate(HR), standard deviation(SDNN), blood flow(BVP), pulse wave transmission time(PTT), skin conduction level(SCL) and skin conduction response(SCR) were calculated before and after the emotional stimulation. As a result, the difference between physiological responses was verified corresponding to the emotions, and the highest emotion classification performance of 86.9% was obtained using the combined analysis of arousal and physiological features. This study suggests that negative emotion can be categorized by psychological and physiological evaluation along with the application of machine learning algorithm, which can contribute to the science and technology of detecting human emotion.