• Science of Emotion and Sensibility
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• v.8 no.2
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• pp.161-167
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• 2005

The study is to examine how facial EMG responses change when children experience a positive emotion(happiness) and a negative emotion(fear). It is to prove that the positive emotion(happiness) could be distinguishable from the negative emotion(fear) by the EMG responses. Audiovisual film clips were used for evoking the positive emotion(happiness) and the negative emotion(fear). 47 children (11-13 years old, 23 boys and 24 girls) participated in the study Facial EMG (right corrugator and orbicularis oris) was measured while children were experiencing the positive or negative emotion. Emotional assessment scale was used for measuring children's psychological responses. It showed more than $85\%$ appropriateness and 3.15, 4.04 effectiveness (5 scale) for happiness and fear, respectively. Facial EMG responses were significantly different between a resting state and a emotional state both in happiness and in fear (p<001). Result suggests that each emotion was distinguishable by corrugator and orbicularis oris responses. Specifically, corrugator was more activated in the positive emotion(happiness) than in the negative emotion(fear), whereas orbicularis oris was more activated in the negative emotion(fear) than in the positive emotion(fear).

• Journal of KIISE:Software and Applications
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• v.37 no.6
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• pp.470-479
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• 2010

In relation to the extraction of opinion words, it may be difficult to directly apply most of the methods suggested in existing English studies to the Korean language. Additionally, the manual method suggested by studies in Korea poses a problem with the extraction of opinion words in that it takes a long time. In addition, English thesaurus-based extraction of Korean opinion words leaves a challenge to reconsider the deterioration of precision attributed to the one to one mismatching between Korean and English words. Studies based on Korean phrase analyzers may potentially fail due to the fact that they select opinion words with a low level of frequency. Therefore, this study will suggest the k-Structure (k=5 or 8) method, which may possibly improve the precision while mutually complementing existing studies in Korea, in automatically extracting opinion words from a simple sentence in a given Korean product review. A simple sentence is defined to be composed of at least 3 words, i.e., a sentence including an opinion word in ${\pm}2$ distance from the attribute name (e.g., the 'battery' of a camera) of a evaluated product (e.g., a 'camera'). In the performance experiment, the precision of those opinion words for 8 previously given attribute names were automatically extracted and estimated for 1,868 product reviews collected from major domestic shopping malls, by using k-Structure. The results showed that k=5 led to a recall of 79.0% and a precision of 87.0%; while k=8 led to a recall of 92.35% and a precision of 89.3%. Also, a test was conducted using PMI-IR (Pointwise Mutual Information - Information Retrieval) out of those methods suggested in English studies, which resulted in a recall of 55% and a precision of 57%.

• The Journal of the Korea Contents Association
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• v.20 no.8
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• pp.350-358
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• 2020

The objective of this empirical study is to examine the mediating and moderating effects of perceived risks on the relationship between knowledge, feelings and acceptance intention towards AI. Subjects in their teens to forties were surveyed and the final sample comprised 1,969 subjects. Data were analyzed using Mediation using Multiple Regression and Moderated Multiple Regression. Results showed that people's knowledge and feelings towards AI affected their acceptance intention of AI. Results also showed that the perceived risks of AI partially mediated and moderated the relationship between feelings and acceptance intention towards AI and moderated but not mediated the relationship between knowledge and acceptance intention towards AI. Overall, these results suggest that people's perceived risks of AI are associated more strongly with their feelings towards AI than their knowledge towards AI. Implications and directions for future research were discussed in relation to increasing general population's acceptance intention towards AI.

• The Journal of the Korea Contents Association
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• v.13 no.6
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• pp.95-107
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• 2013

A linguistic element found in a movie, dialogue, plays a critical role in building up narrative structure. Still, analyses conducted on movies mostly focus on images due to the nature of a movie that conveys a story through its visual images while dialogue has either been underestimated or received less spotlight despite their importance. This study highlights the significance of lines in a movie. This study calls attention to dialogue, which has stayed out of the main focus and been on the periphery thus far when analyzing movies, so as to see how they contribute to constructing a narrative. It then spotlights the significance of dialogue in the movie. To this end, the study sorts out emotional expressions articulated by actors through their dialogues then to make polarity classification into affirmation and negation, followed by a quantitative analysis of how the polarity proportion of emotional expressions changes depending on the narrative structure. The study also suggests a narrative's relevance with emotions by pointing to dynamic emotional changes that shift between affirmation and negation depending on incidents, conflicts and resolution thereof throughout a movie.

• Journal of Digital Contents Society
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• v.18 no.7
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• pp.1293-1301
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• 2017

Recent changes in social structure, such as nuclear family and personalization, are leading to personal and social problems, which may cause various problems due to negative emotional amplification. The absence of a family member who gives a sense of psychological stability in the past can be considered as a representative cause of the emotional difficulties of modern people. This personal and social problem is solved through the empathic interaction of the companion robot communication with users in daily life. In this study, we developed sophisticated empathic interaction design through prototyping of emotional robots. As a result, it was confirmed that the face interaction greatly affects the emotional interaction of the emotional robot and the interaction of the robot improves the emotional sense of the robot. This study has the theoretical and practical significance in that the emotional robot is made more sophisticated interaction and the guideline of the sympathetic interaction design is presented based on the experimental results.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1999.03a
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• pp.201-201
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• 1999

감성(sensitibility)은 반사적이며 직관적으로 발생하고, 인위적인 조정이 불가능하며 명확한 표현이 어렵고 모호하다고 한다. 또 감정(emotion)이 주어진 대상에 따라 동일한 반응을 보이는 공통성을 갖지만, 감성은 동일한 대상에도 개인에 따라 다양한 반응을 보이며, 시간과 환경에 따라 변한다고 본다. 이는 반응 형성의 두 가지 요소인 외부의 자극이나 대상과 반응하는 주체의 양자에서, 감정이 감성에 비해 외부의 자극이나 대상의 영향을 더 받고, 감성은 상대적으로 외부 상황보다는 반응 주체의 다양한 내부 상황에 따라 결정된다는 것을 의미할 수 있다. 이러한 감성의 특징들은 Freud가 말한 무의식의 특징과 비슷한 점이 많다. 따라서 무의식의 작용양상을 살펴보는 것이 감성의 연구에 도움이 될 수 있다고 생각한다. 무의식이란 우리의 마음에 항상 작용하고 있지만, 일상적인 상태에서는 분명하게 알아 볼수가 없고 확실하게 드러나지도 않는 어떤 힘을 말한다. 이는 개인의 다양한 과거 경험이 포함되어 있어서 사람에 따라 각기 다르게 나타나게 된다. 우리가 항상 경험하고 있는 의식은 확실하게 서로 구분되는 대상과 확인율(the principle of identity), 구분논리(bivalent logic), 모순율(the principle of formal contradiction), 상반율(the principle of incompatibility), 가감율(the operation of substraction)을 수용하여 작용한다. 무의식은 의식활동의 이러한 명료함과 정연함을 벗어나 활동한다. 대상간의 구분이 모호해지고 정연한 논리가 흐트러진다. 일상에서는 꿈의 내용과 어린이의 생각, 감정에 치우칠 때 무의식의 특징이 나타난다. Freud는 꿈을 관찰하여 무의식의 작용양상을 다음과 같이 설명하였다. 서로 상반되는 것들이 다음과 같이 설명하였다. 서로 상반되는 것들이 부딛힘이 없이 공존하고 일상의 논리가 무시된다. 부정, 의심이 없고 확실한 것이 없다. 한 대상에 가졌던 생각이 다른 대상에 옮겨간다(displacement). 한 대상이 여러 대상이 갖고 있는 의미를 함축하고 있다(condensation). 시각적인 순서가 무시된다. 마음속의 생각과 외부의 실제적인 일을 구분하지 못한다. 시간 상의 순서가 있다가 없다가 한다. 차례로 일어나야 할 일이 동시에 한꺼번에 일어난다. 대상들이 서로 비슷해지고 동시에 있을 수 없는 대상들이 함께 나타난다. 사고의 정상적인 구조가 와해된다. Matte-Blance는 무의식에서는 여러 독립된 대상들간의 구분을 없애며, 주체와 객체를 하나로 보려는 대칭화(symmetrization)의 경향이 있기 때문에 이런 변화가 생긴다고 하였다. 또 대칭화가 진행되면 무한대의 느낌을 갖게 되어, 전지(moniscience), 전능(omnipotence), 무력감(impotence), 이상화(idealization)가 나타난다. 그러나 무의식에 대칭화만 있는 것은 아니며, 의식의 사고양식인 비대칭도 어느 정도 나타나며, 대칭화의 정도에 따라, 대상들이 잘 구분되어 있는 단계, 의식수준의 감정단계, 집단 내에서의 대칭화 단계, 집단간에서의 대칭화 단계, 구분이 없어지는 단계로 구분하였다.

• Communications of Mathematical Education
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• v.35 no.4
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• pp.359-376
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• 2021

In this study, in accordance with the research trend that the learner's emotions expressed positively or negatively in mathematics learning or the learner's beliefs and attitudes toward mathematics learning affect the results of mathematics learning, the learner's emotions and affective factors are analyzed in the learner's own learning. A power that can be adjusted according to a goal or purpose is needed, and I tried to explain this power through meta-affect. To this end, the meaning of the definitional and conceptual factors of meta-affect was explored based on prior studies. Affective factors of meta-affect were viewed as emotions, attitudes, and beliefs, and conceptual factors of meta-affect were viewed as awareness, evaluating, controlling, utilization, and monitoring, and the meaning of each conceptual factor was also defined. In this study, the conceptual factors and meanings of meta-affect in terms of using them to help in learning mathematics by controlling them, beyond the identification or examination of the characteristics of the affective factors, which are meaningfully dealt with in the field of mathematics education.

• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.507-511
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• 2020

오픈 도메인 대화에서 텍스트에 나타난 태도나 성향과 같은 화자의 주관적인 감정정보를 분석하는 것은 사용자들에게서 풍부한 응답을 이끌어 내고 동시에 제공하는 목적으로 사용될 수 있다. 하지만 한국어 감성분석에서 기존의 대부분의 연구들은 긍정과 부정 두개의 클래스 분류만을 다루고 있고 이는 현실 화자의 감정 정보를 정확하게 분석하기에는 어려움이 있다. 또한 최근에 오픈한 다중클래스로된 한국어 대화 감성분석 데이터셋은 중립 클래스가 전체 데이터셋의 절반을 차지하고 일부 클래스는 사용하기에 매우 적은, 다시 말해 클래스 간의 데이터 불균형 문제가 있어 다루기 굉장히 까다롭다. 이 논문에서 우리는 일곱개의 클래스가 존재하는 한국어 대화에서 세션들을 효율적으로 분류하는 기법들에 대해 논의한다. 우리는 극심한 클래스 불균형에도 불구하고 76.56 micro F1을 기록하였다.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.53-60
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• 2022

YouTube realistically shows fake news and biased content based on facts that have not been verified due to low entry barriers and ambiguity in video regulation standards. Therefore, this study aims to analyze the influence of the media and YouTube on individual behavior and their relationship. Data from YouTube and Twitter are randomly imported with selenium, beautiful soup, and Twitter APIs to classify the 31 most frequently mentioned keywords. Based on 31 keywords classified, data were collected from YouTube, Twitter, and Naver News, and positive, negative, and neutral emotions were classified and quantified with NLTK's Natural Language Toolkit (NLTK) Vader model and used as analysis data. As a result of analyzing the correlation of data, it was confirmed that the higher the negative value of news, the more positive content on YouTube, and the positive index of YouTube content is proportional to the positive and negative values on Twitter. As a result of this study, YouTube is not consistent with the emotion index shown in the news due to its secondary processing and affected characteristics. In other words, processed YouTube content intuitively affects Twitter's positive and negative figures, which are channels of communication. The results of this study analyzed that YouTube plays a role in assisting individual discrimination in the current situation where accurate judgment of information has become difficult due to the emergence of yellow media that stimulates people's interests and instincts.

• Journal of Practical Agriculture & Fisheries Research
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• v.21 no.2
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• pp.103-114
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• 2019

In this study we examined the preferences of college life factors for students at Korea National College of Agriculture and Fisheries(KNCAF). Analytical techniques of unstructured data used opinion mining and text mining techniques, and the results of text mining were visualized as word cloud. And those results were used for statistical analysis of the students' willingness to farm after graduation. The items of the favorable survey consisted of 10 items in 5 areas including university image, self-capacity, dormitory, education system, and future vision. After classifying the emotions of positive and negative in the collected questionnaire, a dictionary of positive and negative was created to evaluate the preference. The items of 'college image' at the time of university support, 'self after 10 years' after graduation, 'self-capacity' and 'present KNCAF' showed high positive emotion. On the other hand, positive emotion was low in the items of 'college dormitory', 'educational course', 'long-term field practice' and 'future of Korean agriculture'. In the cross-analysis of the difference in the will to farming according to gender, farming base, and entrance motivation, the will to farm according to gender and entrance motivation showed statistically significant results, but it was not significant in farming base. Also in binary logistic regression analysis on the will to farming, the statistically significant variable was found to be 'motivation for admission'