• Science of Emotion and Sensibility
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• v.2 no.1
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• pp.77-94
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• 1999

직물 및 직물 관련 제품에서 주로 사용되는 감성 어휘들의 관계 및 구조를 분석하기 위해 잡지 및 설문 조사 과정을 통해 어휘를 수집하였다. 수집된 어휘의 빈도를 조사하고, 어휘 적절성을 평가하여 감성어라고 생각될 수 있는 어휘들만을 정리하였다. 여기서 추출된 372개의 어휘는 직물 및 직물 관련 상황에서의 감성어로 사용될 수 있을 분만 아니라 유사 분야의 감성어 모형에 대한 기본 자료로 활용될 수 있을 것이다. 어휘들 간 관계구조에 대한 분석은 몇 가지 면에서 이뤄졌다. 자유연상 과제를 실시하여 어휘들 간 관계의 연결망(network)을 확인할 수 있었다. 어휘들이 내포하고 있는 의미의 여러 측면에서 어휘들 간 관계를 파악할 수 있도록 어휘들에 대해 다차원 분석을 실시한 결과 어휘 간 관계는 3차원이면 충분히 설명될 수 있는 것으로 나타났다. 두 개의 주차원을 중심으로 어휘들의 공간 분포를 그리고 이들 어휘를 범주로 분류한 결과 대략 11개의 범주로 어휘들을 나눠볼 수 있었다.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1998.04a
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• pp.230-236
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• 1998

본 연구에서는 형용사로 감성 반응을 평가하는 일반적인 감성공학 연구에서 감성공학 연구에서 감성 공간을 보다 쉽게 도식화하고 평가 방법을 단순화시키는 전략을 제시하고자 한다. 기존의 감성공학연구에서는 수집된 감성 어휘를 순차적으로 정리해 줄이는 방법을 취한 다음 최종 어휘 목록을 이용하여 평가 대상이 다소 복잡하여 표본의 수가 많은 경우 어휘 목록을 인위적으로 줄이지 않으면 신뢰로운 반응을 기대하기 어렵다. 본 연구에서는 보다 단순한 방법으로 감성 반응을 포괄적으로 얻을 수 있도록 하기 위하여 감성 형용사를 설명하는데 필요한 최소 차원을 설정하고 차원 평정치에 따라 필요한 어휘군의 목록만 사용하여 어휘를 평가할 수 있는 방법을 제안하고자 한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.139-146
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• 2014

This paper proposes an efficient method for automatically distinguishing various facial expressions. To recognize the emotions from facial expressions, the facial images are obtained by digital cameras, and a number of feature points were extracted. The extracted feature points are then transformed to 49-dimensional feature vectors which are robust to scale and translational variations, and the facial emotions are recognized by statistical pattern classifiers such Naive Bayes, MLP (multi-layer perceptron), and SVM (support vector machine). Based on the experimental results with 5-fold cross validation, SVM was the best among the classifiers, whose performance was obtained by 50.8% for 6 emotion classification, and 78.0% for 3 emotions.

• Journal of Korean Society of Transportation
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• v.32 no.3
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• pp.207-217
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• 2014

Many a number of policies have been tried to reduce auto accidents so far, but it is obvious that further studies are still needed to find a more fundamental and multi-dimensional preventive measure with effect. The National Mental Health Statistics shows that the most profound forms of negative emotions, that is, depression and anxiety, have been increasing, but studies on such a topic are scarce to find. Therefore, we conducted a structural analysis between the negative emotions, including depression and anxiety, of drivers and their driving behaviors using a Structural Equation Modeling(SEM) technique. The review of past literature and studies indicated that not all of human emotions manifest themselves as the ultimate behaviors because they go through emotion regulation Strategies. For this reason, the purpose of this study was set to analyze the structural model developed in this study reflecting the emotion regulation strategies. The result of our analysis showed that the driver's negative emotion had a more significant influence on dangerous driving behaviors than safe ones, and especially, the expressive suppression strategy was found to be the highest factor. Also, the total effect analysis with the negative emotional factors showed that expressive suppression had more significant influence compared to that of cognitive reappraisal. The implication of this study might provide a better understanding on driving behaviors of the drivers and could be used as a fundamental study for future policy development to reduce traffic accidents.

• Science of Emotion and Sensibility
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• v.5 no.1
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• pp.25-32
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• 2002

This paper aimed to identify the sensation and the sensibility of transformed colors from the rustling sound of silk fabrics and to visualize the relationship between sensibilities and fabrics by two-dimensional model. The rustling sounds of 7 silk fabrics were recorded and then the recorded sounds were transformed into colors by the program of sound to color transformation. The sensation and the sensibility of transformed colors were evaluated by 30 participants with Likert scale and the physical properties of each specimen were obtained with red portion (RP), green portion (GP), blue portion (BP), and sum of color count (CC) by means of new equation. The adjectives of sensibility were grouped into three groups: Elegant, Active, and Tough. Elegant was related with RP positively and CC negatively. On the other hand, Active was related with GP and CC positively. Also Tough was highly related with RP. Furthermore, the fabrics that were estimated the high purchase preference showed high CC, RP and GP. Also two dimensional model of relation of the sensation and the sensibility could help to understand those relation.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.309-316
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• 2018

Objectives: Both the valence and arousal components of affect are important considerations when managing mental healthcare because they are associated with affective and physiological responses. Research on arousal and valence analysis, which uses images, texts, and physiological signals that employ deep learning, is actively underway; research investigating how to improve the recognition rate is needed. The goal of this research was to design a deep learning framework and model to classify arousal and valence, indicating positive and negative degrees of emotion as high or low. Methods: The proposed arousal and valence classification model to analyze the affective state was tested using data from 40 channels provided by a dataset for emotion analysis using electrocardiography (EEG), physiological, and video signals (the DEAP dataset). Experiments were based on 10 selected featured central and peripheral nervous system data points, using long short-term memory (LSTM) as a deep learning method. Results: The arousal and valence were classified and visualized on a two-dimensional coordinate plane. Profiles were designed depending on the number of hidden layers, nodes, and hyperparameters according to the error rate. The experimental results show an arousal and valence classification model accuracy of 74.65 and 78%, respectively. The proposed model performed better than previous other models. Conclusions: The proposed model appears to be effective in analyzing arousal and valence; specifically, it is expected that affective analysis using physiological signals based on LSTM will be possible without manual feature extraction. In a future study, the classification model will be adopted in mental healthcare management systems.

• Science of Emotion and Sensibility
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• v.13 no.4
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• pp.789-796
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• 2010

The purpose of this study was to test a large screen and rear-projected virtual reality (VR) interface in color choice for environmental design. The study piloted a single three-dimensional model of a bedroom including furniture in different color combinations. Using a mouse with an $8'{\times}6'$ rear-projector screen, participants could move 360 degree motion in each room. The study used 34 college students who viewed and interacted with virtual rooms projected on a large screen, then filled out a survey. This study aimed to understand the interaction between the users and the VR interface through measurable dimensions of the interaction: interest and user perceptions of presence and emotion. Specifically, the study focused on spatial presence, topic involvement, and enjoyment. Findings should inform design researchers how empirical evidence involving environmental effects can be obtained using a VR interface and how users experience the interaction with the interface.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• Journal of the Korea Society of Computer and Information
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• v.16 no.5
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• pp.33-39
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• 2011

In modern society, in business decisions of our customers are continually increasing in importance, and owing to the development of information and communication technology effectively on a computer to measure the preferences of key customer techniques are being studied. However, this preference reflects significantly on personal ideas, and therefore, it is difficult to commercialize a measure calculated according to the ambiguous results. In this paper, by using biometric information that has been measure; we have configured the multi-sensitivity models based on customer preferences to evaluate the proposed system. This system consists of multiple biometric information of multi-dimensional vector model for learning through the use of structured emotional to apply the same criteria to evaluate customer preferences. In addition, by studying the specific subject-specific emotion model, it is shown to improve accuracy with further experiments.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.163-172
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• 2002

One of the characteristics the agent-based negotiation model has is that we have to input the related parameters together. Considering such properties as vagueness, incompleteness and dynamism B2B domain inherently has this may be an unreasonable request. In this paper we suggested a user interface model for B2B negotiation which mainly focussed on this problem. The suggested model supports such functions as a two dimensional negotiation space, diverse negotiation modes and emotion-based control mechanism. In this paper, we tried to show how these three functions can be used for improving the usefulness of the existing agent-based negotiation model.