• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.314-319
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• 2002

To inspect how the different sensitivities in BAS (or BIS) moderate on the HRV pattern stimulated by positive or negative affective sound, we measured the electrocardiogram (ECG) of 25 students, consisted of 4 groups depending on the BAS (or BIS) sensitivity, while listening music or being exposed to noise. The power spectral density of HRV was derived from the ECG, and the power of HRV was calculated for 3 major frequency ranges (low frequency [LF], medium frequency [MF], and high frequency [HF]). We found that the index MF/(LF+HF), while listening music, was 20 % higher in the individuals with a low BIS but high BAS than in the individuals with a low sensitivity In both BIS and BAS. Moreover, in the former group this index was 30 % higher while listening music than while being exposed to noise. From these results we suggest that individuals with a low BIS but high BAS be more sensitive to positive affective stimuli than other groups, and the index MF/(LF+HF) be applicable to evaluate positive affects.

• Science of Emotion and Sensibility
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• v.20 no.2
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• pp.171-178
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• 2017

'Smart Sensibility Mat (SSM)' was developed and manufactured for positive sensibility of newborn with fiber, digital, and sensibility technology to reflect features and advantages of kangaroo care. For tactile stimuli, the tube of the silicon material to provide a constant temperature of $32^{\circ}C$ was inserted into the mat and connected to the water-thermostat. To provide a uniform temperature throughout the mat, the fabric by the inserting conductive yarn was attached to the mat surface. After wrapping the mat with cotton pad, the polyurethane foam used as medicine in order to similar to the human skin was bonded to the surface of the mat. To provide the auditory stimuli of a level of 30dB with mother's heartbeat sounds and voice recorded in advance, the Bluetooth speaker was inserted into the mat. To investigate the effects of SSM, 10 newborns who born within two weeks were involved in this experiment. While the baby was lying on each of the general mat (GM) and SSM, the baby's physiological signals-heart rate, breathing rate, temperature- were measured and then, those were conducted t-test to examine the difference between the signals of SSM and GM. The results were as follows: heart rate (t=8.131, p<.001) and respiratory rate (t=7.227, p<.001) among the physiological signals of SSM decreased significantly than GM within the normal range. Temperature (t=1.062, p=0.292) at SSM showed a tendency to decrease than GM within the normal range. This means the tactile stimuli and the auditory stimuli providing from SSM give stable physiological responses. Thus, SSM leads to have psychological comfort and stability of newborns.

• Archives of design research
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• v.20 no.3 s.71
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• pp.49-60
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• 2007

As the world is in the age of globalization and information, we observe diverse changes in the market environment. Since wide-spread internet services and global networks made ubiquitous learning and business possible, equalizing consumers' ideology and preference, new trend and life style could be introduced easily. This study stipulates on the theoretical concept of the wellbeing consumer and LOHAS consumer. Consumers of LOHAS could be sampled out through pre-questionnaire targeting at selected food, clothing, and shelter based on companies of both wellbeing and general brands. Through this it is attempted to measure wellbeing emotion, recognition quotient of emotion and reason, affirmation and negation, mental emotion quotient, and preference in order to find out their value and to ultimately come up with what web design should be aiming at. Conclusions are as follows: Firstly, consumers easily recognize emotional identification from the web pages of wellbeing brand, rather than that of general brands. Secondly, what web pages of wellbeing brand recognize is reason, not emotion. Thirdly, the design of wellbeing brands scored higher than those of general brands in terms of positive aspects such as hospitality and familiarity, and high mental emotion quotient could not affect the consumers' preference toward web design. Fourthly, wellbeing brands win more preference than general brands do, and preference becomes higher after customers' visit to web pages basically. Lastly, sampled emotional adjectives toward the web designs of wellbeing brands marked a aesthetic graph figure, without leaning toward an active or stable one. It is expected that this study can serve as a groundwork to create proper strategies to actively involve consumers in industrial sphere.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.264-266
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• 2010

집단에 의해 자유롭게 생성되는 데이터 중 댓글을 활용하여 수익을 내려는 포털사이트들이 증가하고 있는 추세이며, 물건이나 콘텐츠에 대한 사용 후기를 바탕으로 평가하는데 있어서 품질이 높을 때와 낮을 때, 사용 후기의 유효성과 신뢰성 판단이 달라진다. 그만큼 댓글은 중요하게 다뤄지고 있다. 본 연구에서는 동영상콘텐츠로부터 추출된 댓글을 활용하여 긍정적인 감성어휘와 부정적인 감성어휘 그리고 강조 감성어휘를 도출하여 평가가 높은 동영상 콘텐츠를 추천하는 모델을 설계하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.1030-1032
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• 2019

형태소 분석을 통하여 한국어 문장을 형태소 단위의 임베딩 및 학습 관련 연구가 되었으나 최근 비정형적인 텍스트 데이터의 증가에 따라 음소 단위의 임베딩을 통한 신경망 학습에 대한 요구가 높아지고 있다. 본 논문은 비정형적인 텍스트 감성 분석 성능 향상을 위해 음소 단위의 토큰을 생성하고 이를 CNN 모형을 기반으로 다차원 임베딩을 수행하고 감성분석을 위하여 양방향 순환신경망 모델을 사용하여 유튜브의 비정형 텍스트를 학습시켰다. 그 결과 텍스트의 긍정 부정 판별에 있어 90%의 정확도를 보였다.

• Asia Marketing Journal
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• v.8 no.2
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• pp.93-99
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• 2006

최근 국내외로 각광을 많이 받은 마케팅 개념 중 하나가 바로 체험마케팅이다. 특히 업계에서는 체험마케팅에 대한 많은 관심이 있었기에 체험마케팅의 여러 개념과 적용사례가 부각되고 있다. 본 글에서는 체험마케팅에 관련된 주요 오해가 무엇이고 그 실상이 또한 무엇인지 설명하고자 한다. 체험마케팅의 첫째 오해는 체험마케팅이 획기적인 개념이라는 것인데 사실 체험마케팅의 근원이 되는 소비자 반응의 계층, 감성 마케팅, 그리고 통합마케팅 등은 다른 선행 이론에서 잘 정립되었다. 하지만 체험마케팅의 공헌은 이처럼 분할된 여러 개념을 한 틀 속에 응용할 수 있게끔 체계화시켰다는 점이다. 둘째 오해는 체험마케팅의 뜻이 감각마케팅과 통용된다는 것이다. 체험마케팅의 유수 사례 중에는 오감마케팅만을 강조하는 경우가 많은데 성공적인 체험마케팅을 꾀하는 기업은 소비자의 전체적인 체험의 향상을 지향해야 한다. 셋째 오해는 체험마케팅이 감성마케팅만을 강조하고 이성적 마케팅을 간과한다는 생각이다. 체험마케팅은 실제로 소비자의 상황분석에 근거하여 이성과 감성적 마케팅을 둘 다 포괄하는 접근방식이다. 넷째 오해는 체험마케팅의 개념이 너무 추상적이어서 실무에는 큰 도움이 되지 않는다는 주장이다. 하지만 체험마케팅의 실무적인 프로세스를 충실히 따르면 여러 가지의 마케팅 및 조직상의 긍정효과가 나타난다.

• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.80-88
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• 2019

With more emphasis on emotional communication with users in product design field, designers' clear and prompt grasp of user's emotion has become the core activity in product design research. To increase the flexibility applying emotion measurement in the process of product design, this study has used Facial Action Coding System (FACS) of behavioral emotion measurement method in product design evaluation. To select specimens, it has flexibly used the emotional product Image Map. Then this study has selected six product irritants inducing positive, negative and neutral emotions, and conducted FACS experiment with ordinary product users of 20 generations as the experimental subject, and analyzed users' emotional state in response to the irritants through their facial expressions. It also analyzes the advantages and disadvantages of FACS in the process of product design, such as "recording users' unconscious facial expressions" and puts forward some applicable schemes, such as "choosing a product stimulus with high user response". It is expected that this paper can be helpful to the flexibility of FACS as a method to predict user's emotion in advance at the trial stage of product design before launching them to the market.

• Science of Emotion and Sensibility
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• v.26 no.4
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• pp.115-124
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• 2023

This study explores the intersection of literary studies and artificial intelligence (AI), focusing on the common theme of human emotions to foster complementary advancements in both fields. By adopting a comparative perspective, the paper investigates emotion as a shared focal point, analyzing various emotion-related concepts from both literary and AI perspectives. Despite the scarcity of research on the fusion of AI and literary studies, this study pioneers an interdisciplinary approach within the humanities, anticipating future developments in AI. It proposes that literary sensibility can contribute to AI by formalizing subjective literary emotions, thereby enhancing AI's understanding of complex human emotions. This paper's methodology involves the terminology-centered extraction of emotions, aiming to blend subjective imagination with objective technology. This fusion is expected to not only deepen AI's comprehension of human complexities but also broaden literary research by rapidly analyzing diverse human data. The study emphasizes the need for a collaborative dialogue between literature and engineering, recognizing each field's limitations while pursuing a convergent enhancement that transcends these boundaries.

• Smart Media Journal
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• v.11 no.8
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• pp.47-54
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• 2022

It is necessary to discover Korea's diverse traditional culture and publicize it to the world to continue the Korean Wave and develop it in a more positive direction. The present study proposes methods for publicizing little-known 'traditional Korean ceramic tableware' by focusing on Hansik, which is the most frequently published in the British Oxford Dictionary among Korean traditional cultures and can best represent Korean food. To this end, the present study measured cultural recipients' emotions regarding traditional Korean ceramic tableware to derive the 'representative emotions felt regarding traditional Korean ceramic tableware' as a method to reflect it in the design. First, the Delphi Technique was carried out based on 182 emotional vocabulary items collected from existing studies to create 33 groups of emotional vocabularies with similar concepts. In addition, among the emotional vocabularies included in each of the 33 groups, those of overlapping concepts were regrouped based on the characteristics of traditional Korean ceramic tableware, and the most appropriate emotional vocabularies were extracted and reduced to 75. A survey was carried out with 135 cultural recipients experienced with traditional Korean ceramic tableware to derive 32 representative emotions felt regarding traditional Korean ceramic tableware. Finally, from the results of a factor analysis of 32 representative emotions, this study classified vocabulary into six emotion categories including 'aesthetic, pleasure, freshness, ownership, satisfaction, and comfort'. The six emotion categories and 32 representative emotions derived from this study's results can be utilized to measure emotional levels felt by cultural recipients while using traditional Korean ceramic tableware.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.482-488
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• 2014

This paper aims to analyze user's emotion automatically by analyzing Twitter, a representative social network service (SNS). In order to create sentiment analysis models by using machine learning techniques, sentiment labels that represent positive/negative emotions are required. However it is very expensive to obtain sentiment labels of tweets. So, in this paper, we propose a sentiment analysis model by using self-training technique in order to utilize "data without sentiment labels" as well as "data with sentiment labels". Self-training technique is that labels of "data without sentiment labels" is determined by utilizing "data with sentiment labels", and then updates models using together with "data with sentiment labels" and newly labeled data. This technique improves the sentiment analysis performance gradually. However, it has a problem that misclassifications of unlabeled data in an early stage affect the model updating through the whole learning process because labels of unlabeled data never changes once those are determined. Thus, labels of "data without sentiment labels" needs to be carefully determined. In this paper, in order to get high performance using self-training technique, we propose 3 policies for updating "data with sentiment labels" and conduct a comparative analysis. The first policy is to select data of which confidence is higher than a given threshold among newly labeled data. The second policy is to choose the same number of the positive and negative data in the newly labeled data in order to avoid the imbalanced class learning problem. The third policy is to choose newly labeled data less than a given maximum number in order to avoid the updates of large amount of data at a time for gradual model updates. Experiments are conducted using Stanford data set and the data set is classified into positive and negative. As a result, the learned model has a high performance than the learned models by using "data with sentiment labels" only and the self-training with a regular model update policy.