• KNOM Review
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• v.24 no.2
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• pp.35-47
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• 2021

Various stress exists in a modern society, which requires constant competition and improvement. A person under stress often shows his pressure in his facial expression and language. Therefore, it is possible to measure the pressure using facial expression and language analysis. The paper proposes a stress measurement system using facial expression and language sensitivity analysis. The method analyzes the person's facial expression and language sensibility to derive the stress index based on the main emotional value and derives the integrated stress index based on the consistency of facial expression and language. The quantification and generalization of stress measurement enables many researchers to evaluate the stress index objectively in general.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.215-220
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• 2022

Emotion recognition is one of the most important and challenging areas of computer vision. Nowadays, many studies on emotion recognition were conducted and the performance of models is also improving. but, more research is needed on emotion recognition and sentiment analysis of video viewers. In this paper, we propose an emotion analysis system the includes a sentiment analysis model and an interest prediction model. We analyzed the emotional patterns of people watching popular and unpopular videos and predicted the level of interest using the emotion analysis system. Experimental results showed that certain emotions were strongly related to the popularity of videos and the interest prediction model had high accuracy in predicting the level of interest.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.153-160
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• 2021

With the advent of the "age of video" due to the simplification of video content production and the convenience of broadcasting channel operation, review videos on various products are drawing attention. We proposes RASIA, an integrated reliability analysis system based on verbal and nonverbal sentiment analysis of review videos. RASIA extracts and quantifies each emotional value obtained through language sentiment analysis and facial analysis of the reviewer in the video. Subsequently, we conduct an integrated reliability analysis of standardized verbal and nonverbal sentimental values. RASIA provide an new objective indicator to evaluate the reliability of the review video.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.142-151
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• 2022

In this paper, a model combined with explanatory artificial intelligence (xAI) models was presented to secure the reliability of machine learning-based sentiment analysis and prediction. The applicability of the proposed model was tested and described using the IMDB dataset. This approach has an advantage in that it can explain how the data affects the prediction results of the model from various perspectives. In various applications of sentiment analysis such as recommendation system, emotion analysis through facial expression recognition, and opinion analysis, it is possible to gain trust from users of the system by presenting more specific and evidence-based analysis results to users.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.9-12
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• 2021

현대 사회에서 극심한 스트레스로 고통을 호소하는 사람들이 많아짐에 따라 효과적인 스트레스 측정 시스템의 필요성이 대두되었다. 본 연구에서는 영상 속 인물의 표정과 음성 감성 분석을 통한 통합 스트레스 분석 시스템을 제안한다. 영상 속 인물의 표정과 음성 감성 분석 후 각 감성값에서 스트레스 지수를 도출하고 정량화한다. 표정과 음성 스트레스 지수로 도출된 통합 스트레스 지수가 높을수록 스트레스 강도가 높음을 증명하였다.

• Journal of dental hygiene science
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• v.7 no.2
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• pp.89-96
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• 2007

The purpose of this study was to examine the relationship between the self-expression level of dental hygiene students related to communicative competence and their stress during clinical practice and what affected their stress. The subjects in this study were 125 dental hygiene students in W college, on whom a survey was conducted from September 18 through 30, 2006. After the collected data were analyzed with SPSS WIN 10.0 program, the following findings were acquired: 1. When a factor analysis was made to evaluate the self-expression of the students, there appeared three different categories of self-expression: voice/content, facial expression/attitude and sentiment. The three made a 58.1% prediction of their self-expression. As for overall reliability, they turned out highly reliable(Cronbach'a = .881). 2. The dental hygiene students got a mean of 3.58 out of possible five points in self-expression, which indicated that they expressed themselves relatively well. Concerning connections between their general characteristics and self-expression level, those who were inactive during clinical practice got a mean of 3.28, whereas the others who were active got a mean of 3.85. It implied that those who took a more active attitude to clinical practice expressed themselves better(p < .01). The person with whom they found it hard to get along made a statistically significant difference to their self-expression(p < .05). The students who didn't fare well with dental hygienists got the best score(3.70). The second best group(3.53) didn't get along with dentists, followed by assistant nurses(3.46) and patients/caregivers(3.31). As for the impact of the field of dream job, the students who hoped to work or study overseas(4.21) excelled in self-expression those who wanted to be hired in a general hospital, to go onto a school of higher grade and to work in a public dental clinic(p < .05). Among the general characteristics, satisfaction level with major, health status and motivation of choosing dental hygiene made no statistically significant differences to their self-expression. 3. Regarding relations between self-expression level and stress about clinical practice, those who didn't express themselves properly in terms of sentiment scored higher in stress level(3.65). Their stress was statistically significantly different according to self-expression level (p < .05). 4. As for the influence of self-expression and general characteristics on stress with clinical practice, sentiment was selected from among the self-expression categories as a decisive factor to affect stress. Their stress varied statistically significantly with that(p < .05). In contrast, their demographic variables made no statistically significant difference to that, which made a 79.2% prediction of it.