• Cartoon and Animation Studies
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• s.33
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• pp.403-423
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• 2013

Video images of emotional happiness or unhappiness, stress or emotional division of tranquility in the form of a tree is evaluated by weighting. Representative evaluation of the video image brightness contrast sensitivity ratings 1 car happy, unhappy or nervous, calm and refined with two car dependency, sensitivity to visual images are separated. Emotion Recognition of four compared to the numerical data is measured by brightness. OpenCV implementation through evaluation graph the stress intensity contrast, tranquility, happiness, unhappiness with changes in the value of four, separated by sensitivity to computing. Contrast sensitivity of computing the brightness according to the input value 'unhappy' to 'happy' or 'stress' to 'calm' the emotional changes are implemented. Emotion computing the regularity of the image to calculate the sensitivity localized computing system can be controlled according to the emotion of the contrast value of the brightness changes are implemented. The future direction of industry on the application of emotion recognition will play a positive role.

• Science of Emotion and Sensibility
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• v.16 no.2
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• pp.221-234
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• 2013

Positive affect has beneficial consequences in various life domains including social relational aspect. This study proposes that 'joy,' a highly arousing positive social emotion, promotes emotionally supportive reactions during a social interaction. In Study 1, after controlling for confounding personality characteristics (e.g., extraversion, agreeableness, adult attachment) and cognitive variables (e.g., empathy, self-esteem), positive affect predicted the amount of emotional support and enthusiasm one showed to a person who was describing a positive experience. The arousal dimension of positive emotion appeared to be the key in creating this outcome. Study 2 found that people were most likely to react in supportive ways (e.g., more laughing, emotion sharing, and active-constructive responses) to other's conversation after viewing a joy-inducing (rather than pride, awe, calm) film clip. These results partly explain why happy people are socially popular and suggest that one of the central functions of "joy" is to promote social bonding with others.

• Science of Emotion and Sensibility
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• v.7 no.1
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• pp.41-49
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• 2004

The affective words on photographic images were analyzed and a model was structured. Based on this model, the effects of color on the affections were studied. In study 1, the photographic images with various materials and techniques were presented and the affective responses are collected. The factor analysis using principal axing method showed that the variance of the affective words could be explained about 42％ by the three factors. These are named positive-negative, dynamic-static, light-heavy, respectively. In study 2, the effects of color on the affections were evaluated on three basic dimensions. Ninety representative color images were converged black-and-white images, and each of 180 images was rated on the three affective scales. The t-test showed that the effects of color are statistically significant on the three affective scales, respectively. The achromatic images were felt more negative, more static, and heavier than chromatic images.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.562-570
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• 2013

Image of emotional pleasure or displeasure, tension or emotional division of tranquility in the form of a tree is evaluated by weighting. Image representative evaluation of the sensitivity of the brightness contrast ratings 1 car pleasure, displeasure or stress or emotional tranquility and two cars are separated by image segmentation. Emotion Recognition of four compared to the numerical data is measured by brightness. OpenCV implementation through evaluation graph the stress intensity contrast, tranquility, pleasure, displeasure, depending on changes in the value of the computing is divided into four emotional. Contrast sensitivity of computing the brightness depending on the value entered 'nuisance' to 'excellent' or 'stress' to 'calm' the emotional changes can give. Calculate the sensitivity of the image regularity of localized computing system can control the future direction of industry on the application of emotion recognition will play a positive role.

• 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'

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.7
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• pp.1557-1564
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• 2010

In this paper, we propose a maximum entropy-based emotion recognition model using the individual average difference of emotional signal, because an emotional signal pattern depends on each individual. In order to accurately recognize a user's emotion, the proposed model utilizes the difference between the average of the input emotional signals and the average of each emotional state's signals(such as positive emotional signals and negative emotional signals), rather than only the given input signal. With the aim of easily constructing the emotion recognition model without the professional knowledge of the emotion recognition, it utilizes a maximum entropy model, one of the best-performed and well-known machine learning techniques. Considering that it is difficult to obtain enough training data based on the numerical value of emotional signal for machine learning, the proposed model substitutes two simple symbols such as +(positive number)/-(negative number) for every average difference value, and calculates the average of emotional signals per second rather than the total emotion response time(10 seconds).

• Information Systems Review
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• v.17 no.1
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• pp.49-64
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• 2015

Customer reviews which include subjective opinions for the product or service in online store have been generated rapidly and their influence on customers has become immense due to the widespread usage of SNS. In addition, a number of studies have focused on opinion mining to analyze the positive and negative opinions and get a better solution for customer support and sales. It is very important to select the key terms which reflected the customers' sentiment on the reviews for opinion mining. We proposed a document-level terms-based sentiment classification model by select in the optimal terms with part of speech tag. SVMs (Support vector machines) are utilized to build a predictor for opinion mining and we used the combination of POS tag and four terms extraction methods for the feature selection of SVM. To validate the proposed opinion mining model, we applied it to the customer reviews on Amazon. We eliminated the unmeaning terms known as the stopwords and extracted the useful terms by using part of speech tagging approach after crawling 80,000 reviews. The extracted terms gained from document frequency, TF-IDF, information gain, chi-squared statistic were ranked and 20 ranked terms were used to the feature of SVM model. Our experimental results show that the performance of SVM model with four POS tags is superior to the benchmarked model, which are built by extracting only adjective terms. In addition, the SVM model based on Chi-squared statistic for opinion mining shows the most superior performance among SVM models with 4 different kinds of terms extraction method. Our proposed opinion mining model is expected to improve customer service and gain competitive advantage in online store.

• 한국어정보학회:학술대회논문집
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• 2016.10a
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• pp.195-200
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• 2016

본 연구는 MUSE 감성 코퍼스를 활용하여 문장의 극성과 키워드의 극성이 얼마만큼 일치하고 일치하지 않은지를 분석함으로써 특히 문장의 극성과 키워드의 극성이 불일치하는 유형에 대한 연구의 필요성을 역설하고자 한다. 본 연구를 위하여 DICORA에서 구축한 MUSE 감성주석코퍼스 가운데 IT 리뷰글 도메인으로부터 긍정 1,257문장, 부정 1,935문장을, 맛집 리뷰글 도메인으로부터는 긍정 2,418문장, 부정 432문장을 추출하였다. UNITEX를 이용하여 LGG를 구축한 후 이를 위의 코퍼스에 적용하여 나타난 양상을 살펴본 결과, 긍 부정 문장에서 반대 극성의 키워드가 실현된 경우는 두 도메인에서 약 4~16%의 비율로 나타났으며, 단일 키워드가 아닌 구나 문장 차원으로 극성이 표현된 경우는 두 도메인에서 약 25~40%의 비교적 높은 비율로 나타났음을 확인하였다. 이를 통해 키워드의 극성에 의존하기 보다는 문장과 키워드의 극성이 일치하지 않는 경우들, 가령 문장 전체의 극성을 전환시키는 극성전환장치(PSD)가 실현된 유형이나 문장 내 극성 어휘가 존재하지 않지만 구 또는 문장 차원의 극성이 표현되는 유형들에 대한 유의미한 연구가 수행되어야 비로소 신뢰할만한 오피니언 자동 분류 시스템의 구현이 가능하다는 것을 알 수 있다.

• The Journal of the Korea Contents Association
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• v.15 no.10
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• pp.400-412
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• 2015

This paper investigates the relationships among transformational leadership, organizational trust, and creativity in ICT(information & communication technology) company. Also the study investigates the moderator effect of leader's emotional intelligence and the mediator effect of organizational trust in the relationships between transformational leadership and creativity. The multiple regression analysis was employed to test the hypotheses. Using survey data collected from 241 R&D department and support personnel in ICT company. The main finding of this study was as follows: First, a transformational leadership had a positive effect on employee creativity. Second, organizational trust had a positive impact on employee creativity. Third, transformational leadership had a positive effect on organizational trust. Fourth, organizational trust turned out not to mediate the relationship between transformational leadership and employee creativity. Fifth, the positive relationships between transformational leadership and employee creativity were stronger when leader's emotional intelligence was high rather than low. In particular, this review concludes with implications for future research, limitations of this study, and practical application.

• Annual Conference on Human and Language Technology
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• 2016.10a
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• pp.195-200
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• 2016

본 연구는 MUSE 감성 코퍼스를 활용하여 문장의 극성과 키워드의 극성이 얼마만큼 일치하고 일치하지 않은지를 분석함으로써 특히 문장의 극성과 키워드의 극성이 불일치하는 유형에 대한 연구의 필요성을 역설하고자 한다. 본 연구를 위하여 DICORA에서 구축한 MUSE 감성주석코퍼스 가운데 IT 리뷰글 도메인으로부터 긍정 1,257문장, 부정 1,935문장을, 맛집 리뷰글 도메인으로부터는 긍정 2,418문장, 부정 432문장을 추출하였다. UNITEX를 이용하여 LGG를 구축한 후 이를 위의 코퍼스에 적용하여 나타난 양상을 살펴 본 결과, 긍 부정 문장에서 반대 극성의 키워드가 실현된 경우는 두 도메인에서 약 4~16%의 비율로 나타났으며, 단일 키워드가 아닌 구나 문장 차원으로 극성이 표현된 경우는 두 도메인에서 약 25~40%의 비교적 높은 비율로 나타났음을 확인하였다. 이를 통해 키워드의 극성에 의존하기 보다는 문장과 키워드의 극성이 일치하지 않는 경우들, 가령 문장 전체의 극성을 전환시키는 극성전환장치(PSD)가 실현된 유형이나 문장 내 극성 어휘가 존재하지 않지만 구 또는 문장 차원의 극성이 표현되는 유형들에 대한 유의미한 연구가 수행되어야 비로소 신뢰할만한 오피니언 자동 분류 시스템의 구현이 가능하다는 것을 알 수 있다.