• Asia Marketing Journal
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• v.18 no.1
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• pp.37-53
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• 2016

The purpose of this study is to show that the effectiveness of utilitarian versus hedonic attributefocused advertisement on product evaluation depends on the arousal level of positive emotion, and to explore mediation effect of cognitive response to utilitarian attribute-focused advertisement and affective response to hedonic attribute-focused advertisement on the effectiveness of utilitarian versus hedonic attribute-focused advertisement. This research employs a 2 (arousal level: mild vs. elevated) × 2 (advertisement type: utilitarian vs. hedonic) between-subjects design, and 200 undergraduate students participate in the experiment, in which there are 50 students at each experimental group. The results of ANCOVA with positive emotion level as a covariate on advertised product evaluation show significant interaction effect of arousal level and advertisement type, and no effect of positive emotional level. Both of the mediation effects of the cognitive response and those of the affective response are significant. Participants under mild (elevated) arousal of positive emotion more positively evaluate the product in utilitarian (hedonic) attribute-focused advertisement. The positive effect of utilitarian (hedonic) attributefocused advertisement on product evaluation is partially mediated by cognitive (affective) response to the advertisement when consumers are under the mild (elevated) arousal of positive emotion. The results of this study show that advertisers should use utilitarian (hedonic) attribute-focused advertisement to consumers under the mild (elevated) arousal of ambient positive emotion, which should be searched by exploring what kind of event they have experienced.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.191-196
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• 2005

Although the technology for emotion recognition is important one which was demanded in various fields, it still remains as the unsolved problems. Especially, there is growing demand for emotion recognition technology based on racial image. The facial image based emotion recognition system is complex system comprised of various technologies. Therefore, various techniques such that facial image analysis, feature vector extraction, pattern recognition technique, and etc, are needed in order to develop this system. In this paper, we propose new emotion recognition system based un previously studied facial image analysis technique. The proposed system recognizes the emotion by using the fuzzy classifier. The facial image database is built up and the performance of the proposed system is verified by using built database.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2003.05a
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• pp.101-106
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• 2003

This paper introduces the basic concept of computational modeling of perception processes for multimedia data. Such processes are modeled as hierarchical inter-and relationships amongst information in physical, physiological, psychological and cognitive layers in perception. Based on our framework, this paper gives the , algorithms for content-based retrieval for multimedia database systems.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.193-196
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• 2007

The purpose of this paper is to find boundary tone's characteristics in Korean emotion speeches. I mainly focus on investigating patterns and f0 values of boundary tones and f0 values in utterance final phrases.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2004.11a
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• pp.46-46
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• 2004

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2004.11a
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• pp.48-48
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• 2004

• The Journal of Korean Institute for Practical Engineering Education
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• v.4 no.2
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• pp.171-175
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• 2012

Recently, the research on ICT (Information and Communication Technology), which cognizes and communicates human's emotion through information technology, is increasing. For instance, there are researches on phones and services that perceive users' emotions through detecting people's voices, facial emotions, and biometric data. In short, emotions which were used to be predicted only by humans are, now, predicted by digital equipment instead. Among many ICT researches, research on emotion recognition from face is fully expected as the most effective and natural human interface. This paper studies about sensitivity ICT and examines mechanism of facial expression recognition system as an example of sensitivity ICT.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.1
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• pp.57-62
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• 2010

Recently in the mobile market, the communication technology which bases on the sense of sight, sound, and touch has been developed. However, human beings uses all five - vision, auditory, palatory, olfactory, and tactile - senses to communicate. Therefore, the current paper presents a technology which enables individuals to be aware of other people's emotions through a machinery device. This is achieved by the machine perceiving the tone of the voice, body temperature, pulse, and other biometric signals to recognize the emotion the dispatching individual is experiencing. Once the emotion is recognized, a scent is emitted to the receiving individual. A system which coordinates the emission of scent according to emotional changes is proposed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.11
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• pp.1496-1505
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• 2018

The technology of emotion recognition is necessary for human computer interaction communication. There are many cases where one cannot communicate without considering one's emotion. As such, emotional recognition technology is an essential element in the field of communication. n this regard, it is highly utilized in various fields. Various bio-sensor sensors are used for human emotional recognition and can be used to measure emotions. This paper proposes a system for recognizing human emotions using two physiological sensors. For emotional classification, two-dimensional Russell's emotional model was used, and a method of classification based on personality was proposed by extracting sensor-specific characteristics. In addition, the emotional model was divided into four emotions using the Support Vector Machine classification algorithm. Finally, the proposed emotional recognition system was evaluated through a practical experiment.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2004.11a
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• pp.13-13
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• 2004