Kim, Myung-Kyu;Kim, Jung-Ho;Cha, Myung-Hoon;Chae, Soo-Hoan
Science of Emotion and Sensibility
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v.12
no.4
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pp.433-442
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2009
People are tending to buy products through the Internet rather than purchasing them from the store. Some of the consumers give their feedback on line such as reviews, replies, comments, and blogs after they purchased the products. People are also likely to get some information through the Internet. Therefore, companies and public institutes have been facing this situation where they need to collect and analyze reviews or public opinions for them because many consumers are interested in other's opinions when they are about to make a purchase. However, most of the people's reviews on web site are too numerous, short and redundant. Under these circumstances, the emotion scanning system of text documents on the web is rising to the surface. Extracting writer's opinions or subjective ideas from text exists labeled words like GI(General Inquirer) and LKB(Lexical Knowledge base of near synonym difference) in English, however Korean language is not provided yet. In this paper, we labeled positive, negative, and neutral attribute at 4 POS(part of speech) which are noun, adjective, verb, and adverb in Korean dictionary. We extract construction patterns of emotional words and relationships among words in sentences from a large training set, and learned them. Based on this knowledge, comments and reviews regarding products are classified into two classes polarities with positive and negative using SO-PMI, which found the optimal condition from a combination of 4 POS. Lastly, in the design of the system, a flexible user interface is designed to add or edit the emotional words, the construction patterns related to emotions, and relationships among the words.
The article reports findings on: (1) development of emotion assessment scale in evaluating the Television(TV) picture quality; and (2) how psychological and physical factors relate to TV picture quality. A total of 152 adjectives that specifically describe emotional reactions were first selected from a Korean dictionary of adjectives, followed by ratings on their suitability for the evaluation of TV picture quality. The final selection of 19 adjective, based on the reported rating scores greater than 4.1, were used on 126 college students who were asked to perform similarity ratings on the adjectives. Based on factor analyses (i.e., principal component analysis with oblique rotation) on the similarity of scores, the following adjectives were selectively chosen for the development of the new emotion assessment scale: 'neat-messy', 'refreshing-gloomy', 'clean-dirty', 'comfortable-tense', 'smooth-rough', 'bright-dark', 'gorgeous-plain', 'diverse-monotonous', 'satisfying', 'natural', and 'sensuous'. These adjectives composed into two distinct constructs, 'cleanness or smart' factor and 'gorgeousness' factor, which demonstrated sensitivity to changes in brightness, contrast, color, and tint in the TV picture quality, except for changes in sharpness.
Journal of the Korean BIBLIA Society for library and Information Science
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v.25
no.3
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pp.101-118
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2014
Given the context of rapid developing ubiquitous computing environment, it is imperative for users to search and use images based on affective meanings. However, it has been difficult to index affective meanings of image since emotions of image are substantially subjective and highly abstract. In addition, utilizing low level features of image for indexing affective meanings of image has been limited for high level concepts of image. To facilitate the access points of affective meanings of image, this study aims to utilize user-provided responses of images. For a data set, emotional words are collected and cleaned from twenty participants with a set of fifteen images, three images for each of basic emotions, love, sad, fear, anger, and happy. A total of 399 unique emotion words are revealed and 1,093 times appeared in this data set. Through co-word analysis and network analysis of emotional words from users' responses, this study demonstrates expanded word sets for five basic emotions. The expanded word sets are characterized with adjective expression and action/behavior expression.
Generally human sensibility is expressed in a certain language. To discover the sensibility of visitors in relation to the forest environment, it is first necessary to determine their exact meanings. Furthermore, it is necessary to sort these terms according to their meanings based on an appropriate classification system. This study attempted to develop a classification system for forest sensibility vocabulary by extracting Korean words used by forest visitors to express their sensibilities in relation to the forest environment, and established the structure of the system to classify the accumulated vocabulary. For this purpose, we extracted forest sensibility words based on literature review of experiences reported in the past as well as interviews of forest visitors, and categorized the words by meanings using the Standard Korean Language Dictionary maintained by the National Institute of the Korean Language. Next, the classification system for these words was established with reference to the classification system for vocabulary in the Korean language examined in previous studies of Korean language and literature. As a result, 137 forest sensibility words were collected using a documentary survey, and we categorized these words into four types: emotion, sense, evaluation, and existence. Categorizing the collected forest sensibility words based on this Korean language classification system resulted in the extraction of 40 representative sensibility words. This experiment enabled us to determine from where our sensibilities that find expressions in the forest are derived, that is, from sight, hearing, smell, taste, or touch, along with various other aspects of how our human sensibilities are expressed such as whether the subject of a word is person-centered or object-centered. We believe that the results of this study can serve as foundational data about forest sensibility.
This essay researches which affective responses can be made from musical elements of Korean traditional music through adjective description about mood responses. It results that traditional mode which are classified into Pyoungjo and Gyemounjo hardly have any the affective responses, while major and minor mode, tempo, tone color, vertical and horizontal texture have some effects on particular affects. Meanwhile, newly composed Korean traditional music have more effects for as inducing affective responses rather than works as old Korean traditional music. As well as Western music, Korean traditional music induces different affective responses to each musical element. As such, this aspects can be adopted to different realms like background music, commercial music, music therapy and so on.
Journal of The Korean Association For Science Education
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v.29
no.7
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pp.783-791
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2009
This study was conducted to examine my hypothesis that how teacher's teaching style influences emotional and physiological states of students in the secondary school science classroom. Sixty healthy secondary school students were participated in this study and divided into two groups: manipulation and non-manipulation. Each group underwent different styles of teaching on the scientific hypothesis-generating of com starch experiment. Before and after the class, the strength of emotion was measured using adjective emoticon check lists and they extracted their saliva sample for salivary hormone analysis. Here are the results of this study. First, the intensity of positive emotions in the manipulation group was significantly stronger than the one in the non-manipulation group, whereas the intensity of negative emotions in the non-manipulation group was significantly stronger than the one in the manipulation group. Second, the cortisol level, an indicator of stress, was decreased in the manipulation group whereas it was increased in non-manipulation group. Third, the quality of scientific hypotheses which is generated by students during the class had no connection with types of instructions. Fourth, this study found significantly negative correlation between students' emotional intensity of interest and concentration changes of salivary cortisol. Therefore, the different teaching styles have influence upon students' attitude and interest in science.
The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.
Emotional interest in the 1970s, Japan started from the technical and engineering beyond the scope, period late structuralist entering the world has been the subject of interest, as well as in academic research is becoming the main theory. In addition, communication between various disciplines such as humanities through the study of consilience and fusion, the human life to continue as a subject, its importance has risen. So this study are to design for the study of emotion through the human heart in space and how the expression of emotions and can be validated in a study. GSD to evaluate the action (verb) and emotional words (adjective) related to two variables to measure the degree of correlation coefficient was an experiment to find out. Picasso painting, it is 'difficult to understand', 'special', 'interesting', 'not interested', 'confused', 'fun', 'anxious', 'dark', 'cool', 'hard' to have relevance, such as the distribution of emotional words, and as a result of the move was a lot of work. This result can be obtained through the arcane resistance of the cubist paintings that make a lot of body movements. In Renoir painting 'stable', 'warm', 'soft', 'easy to understand', 'bright', 'boring', 'curious', such as emotional words ranged to have a relationship with this behavior is less motion in space. This result can be obtained through the understanding of the Impressionist paintings that are less body movements. As a result, space design, emotional design in the evaluation (GSD) for the empirical analysis that evaluated the feasibility and future of the emotional space of the design could be based in the area is considered.
Journal of the Korean Institute of Intelligent Systems
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v.9
no.5
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pp.526-537
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1999
In the paper. we propose an evaluation model based the adaptive fuzzy systems, which can transform the physical features of a color pattern to the emotional features. The model is motivated by the Soen's psychological experiments, in which he found the physical features such as average hue, saturation, intensity and the dynamic components of the color patterns affects to the emotional features represented by a pair of adjective words having the opposite meanings. Our proposed model consists of two adaptive fuzzy rule-bases and the y-model, a l i r ~ r ys et operator, to fuze the evaluation values produced by them. The model shows con~parablep erformances to the neural network for the approximation of the nonlinear transforms, and it has the advantage to obtain the linbwistic interpretation from the trained results. We believe the evaluated results of a color pattern can be used to the emotion-based color image retrievals.
In a fiercely competitive market, brands have become an important foundation for people to choose their products, and brands are also symbols of people's status and strength. Therefore, the importance of brand image design is growing. And color is an expression of emotion and can improve the communication and marketing environment of brand image as an important element of brand image. In this study, Interbrand selected Korea-China TOP 50 brand as a survey target in 2019 and downloaded brand CI from each brand's homepage to data images through Adobe Photoshop program and HSB system, and analyzed the color of the brand. There is no big difference in analyzing the color characteristics of Korean and Chinese brands, and the I.R.I color image scale analysis shows that the overall design of Korean brands is vibrant, elegant and warm to consumers. On the other hand, the overall design of the Chinese brand offers consumers a solemn, modern and sophisticated emotional adjective. Based on the results, this study can provide practical implications for companies to select colors and forms when launching their own brands and developing products.
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