• The Journal of the Korea Contents Association
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• v.15 no.3
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• pp.21-31
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• 2015

This thesis proposes the emotions acquired after listening to the music as an emotion model composed of 8 types of emotions, based on the emotion model studied previously. The 5 musical factors selected, that affect the emotion, are tempo, dynamics, amplitude change, brightness, and noise. According to the emotion model composed of 8 types of emotions, 160 songs categorized into the 8 types of emotions were selected, and the actual data was extracted and analyzed. Through the analysis of actual data, an emotion equation made of weighted value of 5 factors was derived, and an algorithm that can predict the emotion by mapping on the 2-dimensional emotion coordinate system through the emotion equation was designed. Also, a way of controlling emotion by moving the coordinates on the 2-dimensional emotion coordinate system was suggested.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2009.05a
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• pp.55-58
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• 2009

본 연구는 주관적인 사용자의 감성을 객관적으로 정의하여 2차원 감성 모델에 의한 터치폰의 GUI 디자인 요소에 대한 디자인 가이드라인을 제시하고자 한다. 본 연구는 다음과 같은 단계로 연구를 진행하였다. 첫 번째 단계로 그루밍(Grooming) 사용자들의 라이프 스타일을 조사하여 Norman(2002)에 의거한 감각적, 행태적, 그리고 심볼적 세 가지 레벨의 감성요소를 추출하였다. 두 번째 단계로 Russell(1980)의 28개 감성 어휘와 세 단계 감성과의 관계성을 설문하여 감성모델을 구현하였다. 마지막으로 요인분석을 이용하여 대표 감성 어휘를 도출한 후 감성적 터치폰의 GUI(Graphic User Interaction) 디자인 요소를 제시함으로써 사용자의 감성이 반영된 인간 중심적인 제품 디자인을 위한 가이드라인을 제안한다.

• Science of Emotion and Sensibility
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• v.17 no.3
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• pp.57-64
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• 2014

In this paper, we verify the relation between elements (active and inactive) of Russell's emotional dimension ("A Circumplex Model") to propose a new representing method. Russell's emotional dimension expresses emotional words (happy, joy, sad, nervous, etc.) as a point on the two dimensions (Arousal and Valence). It is most commonly used in many filed such as Science of Emotion & Sensibility, Human-Computer Interaction (HCI), and Psychology etc. But other researchers have insisted that Russell's emotional dimension have to be modified because of its inherent problems. Such problems included the possibility of mixed feelings, the difference of emotion and sensibility, and the difference of Arousal axis and Valence axis. Therefore, we verify relationship of A Circumplex Model's elements (active and inactive) and find how to people express their Arousal feelings using survey. We finally propose new method to express emotion in Russell's emotional dimension. Using this method, we can solve Russell's problems and compensate other researches.

• Science of Emotion and Sensibility
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• v.20 no.1
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• pp.75-82
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• 2017

We propose a novel method for modeling emotional dimensions using expansion of Russell's (1980) emotional dimensions (Circumplex Model). The Circumplex Model represents emotional words in two axes (Arousal, Valence). However, other researchers have insisted that location of word in Russell's model which is expressed by single point could not represent exact position. Consequently, it is difficult to apply this model in engineering fields (such as Science of Emotion & Sensibility, Human-Computer-Interaction, Ergonomics, etc.). Therefore, we propose a new modeling method which expresses emotional word not as a single point but as a region. We conducted survey to obtain actual data and derived equations using ellipse formula to represent emotional region. Furthermore, we applied ANEW and IAPS which are commonly used in many studies to our emotional model using pattern recognition algorithm. Using our method, we could solve problems with Russell's model and our model is easily applicable to the field of engineering.

• Science of Emotion and Sensibility
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• v.7 no.3
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• pp.51-56
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• 2004

Arousal level has been well defined by autonomic responses. However, entire emotion including both valence and arousal level is often questioned to be completely described by only autonomic responses. This study is to find the autonomic physiological parameters which were used emotion evaluation, 15 undergraduate students were asked to watch eight video clips from diverse movies and comedy shows for experiencing emotions. The subjectively experienced emotion were grouped by three factors. Two dimensional emotion model having the pleasant-unpleasant and arousal-non arousal factors were mapped with three physiological responses(GSR, PPG, SKT). The results may suggest that PPG and GSR may be used as arousal index while SKT may pleasant index. And the complex relation of physiological responses to emotional experiences are discussed.

• Science of Emotion and Sensibility
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• v.15 no.2
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• pp.283-296
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• 2012

The goal of this study is to determine the social emotion model as an emotion sharing relationship and information sharing relationship based on the user's relations at social networking services. 26 social emotions were extracted by verification of compliance among 92 different emotions collected from the literature survey. The survey on the 26 emotion words was verified to the similarity of social relation types to the Likert 7-points scale. The principal component analysis of the survey data determined 12 representative social emotions in the emotion sharing relation and 13 representative social emotions in the information sharing relation. Multidimensional scaling developed the two-dimensional social emotion model of emotion sharing relation and of information sharing relation based on online communication environment. Meanwhile, insignificant factors in the suggest social emotion models were removed by the structural equation modeling analysis, statistically. The test result of validity analysis demonstrated the fitness of social emotion models at emotion sharing relationships (CFI: .887, TLI: .885, RMSEA: .094), social emotion model of information sharing relationships (CFI: .917, TLI: .900, RMSEA : 0.050). In conclusion, this study presents two different social emotion models based on two different relation types. The findings of this study will provide not only a reference of evaluating social emotions in designing social networking services but also a direction of improving social emotions.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2009.11a
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• pp.232-235
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• 2009

본 연구에서는 실시간으로 변화하는 사용자의 감성을 평가하여 각성 또는 이완으로 분류된 시선 정보 이미지를 저장하는 시스템을 구현하고자 한다. 사용자의 감성을 분류하기 위한 요소는 Larson과 Diner 가 정의한 2 차원 감성모델에서 각성, 이완 요소를 사용한다. 감성 상태를 분류하기 위하여 자율 신경계 중 착용과 휴대가 간편한 PPG 센서를 사용하며, PPG 를 분석하기 위한 변수로는 진폭의 양과 초당 Peak 의 빈도수를 사용한다. 머리에 고정할 수 있는 캠을 사용하여 사용자가 바라보는 시선 정보를 획득하고, 클라이언트 컴퓨터는 획득된 시선 정보를 UDP 통신을 사용해 서버 컴퓨터로 전송하는 시스템이다. 320(pixel)*240(pixel)*32(bit)인 영상 데이터를 1/30 로 압축하여 전송하며, 각성과 이완으로 분류되는 시점의 영상을 블록화하여 JPEG 이미지로 저장한다. 본 시스템은 실시간으로 변화되는 사용자의 감성 상태를 파악하여 이미지를 전송하고 서버 컴퓨터에 저장함으로써 당시 사용자가 느꼈던 감성들에 대해 피드백을 주고자 하는데 의의가 있다.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2009.11a
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• pp.91-94
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• 2009

인공감정에 관련된 기존의 연구는 대부분 감정의 인식과 물리적 표현에 중점 되어 연구되었다. 하지만 감정은 성격에 따라 달리 표출되고, 시간에 따라 변화 양상을 갖는다. 또한 새로운 감정자극을 받기 이 전의 감정상태에 따라서 표출 될 감정은 달라진다. 본 논문은 감정을 성격, 시간, 감정간의 관계에 따라 관리하여 현재 표출될 감정을 시각화 해주는 인공감정을 제안한다. 감정을 시각화하기 위해서 본 논문의 인공감정은 감정그래프와 감정장을 갖는다. 감정그래프는 특정 감정을 성격과 시간에 따라 표현하는 2차원 형태의 그래프 이다. 감정장은 감정그래프에서 표현된 서로 다른 종류의 감정들을 시간과 감정간의 관계에 따라 시각화 해주는 3차원 형태의 모델이다. 제안된 인공감정을 통해 감정을 시각화해 보기 위해, 감정의 인식과 물리적 표현을 텍스트 기반으로 간소화시킨 시뮬레이터에 적용했다.

• The Journal of the Korea Contents Association
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• v.15 no.10
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• pp.1-9
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• 2015

In this paper, a study on facial movements are analyzed in terms of opposite emotion stimuli by image processing of Kinect facial image. To induce two opposite emotion pairs such as "Sad - Excitement"and "Contentment - Angry" which are oppositely positioned onto Russell's 2D emotion model, both visual and auditory stimuli are given to subjects. Firstly, 31 main points are chosen among 121 facial feature points of active appearance model obtained from Kinect Face Tracking SDK. Then, pixel changes around 31 main points are analyzed. In here, local minimum shift matching method is used in order to solve a problem of non-linear facial movement. At results, right and left side facial movements were occurred in cases of "Sad" and "Excitement" emotions, respectively. Left side facial movement was comparatively more occurred in case of "Contentment" emotion. In contrast, both left and right side movements were occurred in case of "Angry" emotion.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.