• Journal of the East Asian Society of Dietary Life
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• v.19 no.3
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• pp.311-315
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• 2009

The purpose of this research was to discern how consumers experience the images of sensibility vocabularies for atmosphere of family restaurants and to suggest the applications for restaurant managers and marketers by presenting words that appeal to consumers' needs and emotions. More reliable results were obtained by researching the sensibility vocabularies using free association and stimulus methods. Extracting 8 sensibility words among 28 vocabularies, we constructed the relation of evaluation concepts by using a structural equation model. Overall, the structural equation model, which is a method to select reliable sensibility vocabularies can increase the sensitivity of the model.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.576-581
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• 2022

감정 분석은 긍부정의 극성을 판단하는 감성 분석과 달리 텍스트로부터 구체적인 감정 유형을 분류해내는 과제이다. 본 논문에서는 소설 텍스트에 감정 분석을 수행하는 것을 새로운 과제로 설정하고, 이에 활용할 수 있는 감정 용언 사전을 소개한다. 이 사전에는 맥락과 상관없이 동일한 감정을 전달하는 직접 감정 표현과 맥락에 따라 다른 감정으로 해석될 수 있는 간접 감정 표현이 구분되어 있다. 우리는 이로써 한국어 자연어처리 연구자들이 소설의 풍부한 감정 표현 텍스트로부터 정확한 감정을 분류해낼 수 있도록 그 단초를 마련한다.

• Science of Emotion and Sensibility
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• v.15 no.4
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• pp.503-516
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• 2012

According to the researches of robot and software agent until now, computational emotion model is dependent on system, so it is hard task that emotion models is separated from existing systems and then recycled into new systems. Therefore, I introduce the Engine of computational Emotion model (shall hereafter appear as EE) to integrate with any robots or agents. This is the engine, ie a software for independent form from inputs and outputs, so the EE is Emotion Generation to control only generation and processing of emotions without both phases of Inputs(Perception) and Outputs(Expression). The EE can be interfaced with any inputs and outputs, and produce emotions from not only emotion itself but also personality and emotions of person. In addition, the EE can be existed in any robot or agent by a kind of software library, or be used as a separate system to communicate. In EE, emotions is the Primary Emotions, ie Joy, Surprise, Disgust, Fear, Sadness, and Anger. It is vector that consist of string and coefficient about emotion, and EE receives this vectors from input interface and then sends its to output interface. In EE, each emotions are connected to lists of emotional experiences, and the lists consisted of string and coefficient of each emotional experiences are used to generate and process emotional states. The emotional experiences are consisted of emotion vocabulary understanding various emotional experiences of human. This study EE is available to use to make interaction products to response the appropriate reaction of human emotions. The significance of the study is on development of a system to induce that person feel that product has your sympathy. Therefore, the EE can help give an efficient service of emotional sympathy to products of HRI, HCI area.

• Journal of the Korea Fashion and Costume Design Association
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• v.17 no.3
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• pp.85-98
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• 2015

Modern fashion shows the trend of various styles and the period focusing on only product functions is changed to the period focusing on consumer's sensibility. Consumers show different sensitivities and preference by individual at the stage cognizing and recognizing the stimulation of given image and the method of objective measurement based on the fashion sensible vocabularies is necessary to measure fashion sensibility. Therefore, this research is significant to examine differences of preference to classic fashion by generation and awareness for sensible vocabularies and suggest methodology of design sensible evaluation research through the quantitative evaluation objectifying subjective sensibility. For the method of research, precedent theses related to classic, concept and characteristics of classic in books and definition and characteristics by generation were examined, the best 3 domestic portal sites were selected and adjective vocabularies and images related to classic were collected from 2010 to 2014. Among the 206 adjectives collected, vocabularies whose average is more than 3.5 were drawn by 5-point Likert scale for fashion expert group. And, among the total 306 images collected, 21 representative images were selected by preliminary investigation of fashion expert group. For the classic images and vocabularies selected, frequency analysis, factor analysis and variance analysis were conducted by SPSS 19.0. The results of analysis are as follows. Preference to classic fashion image by generation was analyzed. As a result, both of two generations selected classic fashion as the most classic one. The images of the next orders were analyzed. As a result, Y generation selected basic classic fashion image which is casual with high activity as a classic one. Baby boom generation selected ancient classic fashion image, so there were differences in preference for classic by generation. As a factor analysis on classic adjective vocabularies, they could be divided into 5 factors such as basic form, attractive form, traditional form, vintage form and active form and they verified that credibility of all measuring variables for classic sensible vocabularies was achieved. Differences of classic sensible vocabularies by classic fashion image and generation were examined. As a result, generation and classic fashion image made a significant effect on five factors. Therefore, there were differences of the awareness on classic fashion images and sensible vocabularies among the generations and this thesis can be a fundamental material which objectifies subjective sensibility and suggests the methodology of new research.

• The KIPS Transactions:PartB
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• v.17B no.1
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• pp.93-98
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• 2010

In this paper, we introduce a system that summarizes product evaluation through linguistic analysis to effectively utilize explosively increasing product reviews. Our system analyzes polarities of product reviews by product features, based on which customers evaluate each product like 'design' and 'material' for a skirt product category. The system shows to customers a graph as a review summary that represents percentages of positive and negative reviews. We build an opinion word dictionary for each product feature through context based automatic expansion with small seed words, and judge polarity of reviews by product features with the extracted dictionary. In experiment using product reviews from online shopping malls, our system shows average accuracy of 69.8% in extracting judgemental word dictionary and 81.8% in polarity resolution for each sentence.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.113-125
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• 2013

Every company wants to know customer's requirement and makes an effort to meet them. Cause that, communication between customer and company became core competition of business and that important is increasing continuously. There are several strategies to find customer's needs, but VOC (Voice of customer) is one of most powerful communication tools and VOC gathering by several channels as telephone, post, e-mail, website and so on is so meaningful. So, almost company is gathering VOC and operating VOC system. VOC is important not only to business organization but also public organization such as government, education institute, and medical center that should drive up public service quality and customer satisfaction. Accordingly, they make a VOC gathering and analyzing System and then use for making a new product and service, and upgrade. In recent years, innovations in internet and ICT have made diverse channels such as SNS, mobile, website and call-center to collect VOC data. Although a lot of VOC data is collected through diverse channel, the proper utilization is still difficult. It is because the VOC data is made of very emotional contents by voice or text of informal style and the volume of the VOC data are so big. These unstructured big data make a difficult to store and analyze for use by human. So that, the organization need to automatic collecting, storing, classifying and analyzing system for unstructured big VOC data. This study propose an intelligent VOC analyzing system based on opinion mining to classify the unstructured VOC data automatically and determine the polarity as well as the type of VOC. And then, the basis of the VOC opinion analyzing system, called domain-oriented sentiment dictionary is created and corresponding stages are presented in detail. The experiment is conducted with 4,300 VOC data collected from a medical website to measure the effectiveness of the proposed system and utilized them to develop the sensitive data dictionary by determining the special sentiment vocabulary and their polarity value in a medical domain. Through the experiment, it comes out that positive terms such as "칭찬, 친절함, 감사, 무사히, 잘해, 감동, 미소" have high positive opinion value, and negative terms such as "퉁명, 뭡니까, 말하더군요, 무시하는" have strong negative opinion. These terms are in general use and the experiment result seems to be a high probability of opinion polarity. Furthermore, the accuracy of proposed VOC classification model has been compared and the highest classification accuracy of 77.8% is conformed at threshold with -0.50 of opinion classification of VOC. Through the proposed intelligent VOC analyzing system, the real time opinion classification and response priority of VOC can be predicted. Ultimately the positive effectiveness is expected to catch the customer complains at early stage and deal with it quickly with the lower number of staff to operate the VOC system. It can be made available human resource and time of customer service part. Above all, this study is new try to automatic analyzing the unstructured VOC data using opinion mining, and shows that the system could be used as variable to classify the positive or negative polarity of VOC opinion. It is expected to suggest practical framework of the VOC analysis to diverse use and the model can be used as real VOC analyzing system if it is implemented as system. Despite experiment results and expectation, this study has several limits. First of all, the sample data is only collected from a hospital web-site. It means that the sentimental dictionary made by sample data can be lean too much towards on that hospital and web-site. Therefore, next research has to take several channels such as call-center and SNS, and other domain like government, financial company, and education institute.

• Science of Emotion and Sensibility
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• v.11 no.2
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• pp.257-270
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• 2008

Three experiments were carried out in order to determine whether users have longer using time, better recall of product information, and flow in an aesthetically appealing product (media player) in products offering good usability. For the experiment, fourteen emotional words were employed which were made up of 8 aesthetic and 6 usability words. In a preliminary experiment, the subjects freely used three media players and selected emotional words by a 7-point likert scale to distinguish a group of similar usability value and another group contrary to the other in aesthetic and usability value. (N=18) In the main experiment, it was hypothesized that users use more and have more flow and recalled information in the case of the aesthetically appealing product. Therefore, in the main experiment, we measured how much time subjects spent using the product and asked them to make an assumption regarding the time spent by the group that has the same usability value. We then examined the time they spent and the gap between the actual and estimated time. We also calculated the amount of menu information recalled via a questionnaire. In the last experiment, we selected the group of products contrary to each other in aesthetic and usability value and assessed the differences in using time, recall of product information, and flow. (N=18) The empirical results provide evidence that aesthetically appealing products are associated with greater flow and recall of product information than other products, thus supporting the hypothesis. In addition, it was found that there is a positive correlation between the aesthetically appealing product and flow index as well as with recalled information.

• Korean Institute of Interior Design Journal
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• v.19 no.6
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• pp.75-82
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• 2010

The purposes of this study are to present the criteria for a sensibility rating scale for measuring the general public's perception of museums' spatial environment, particularly lobby space, through an empirical analysis; and to clarify the characteristics of the presented rating scale in terms of each rating element and factor. For this study, a survey was conducted during September 11-17, 2010, and a total of 370 museum visitors participated in the survey. A sensibility rating scale used for the survey consisted of a total of 32 adjectives selected from a literature review of previous studies. To specify the dimensions of semantic space using the semantic adjectives, words with opposite meanings were analyzed with the semantic differential technique developed by Osgood et al. Using SPSS, a reliability analysis, factor analysis, and cluster analysis were conducted on the data obtained from the survey. The results of this study can be summarized as follows: According to the general public's perception of museum lobbies, five factors were found from the 19 semantic ratings of the Gyeonggido Museum of Modern Art and the 20 semantic ratings of the Seoul Museum of Art, respectively. In the case of Gyeonggido Museum of Modern Art, three additional semantic words of 'orderly', 'open', and 'original', which did not appear in the case of Seoul Museum of Art, were discovered. In the case of Seoul Museum of Art, more detailed semantic words such as 'restrained', 'ordinary', 'concrete', and 'intellectual (rational)' were obtained. Five semantic elements, which describe the two museums, were: Feelings of 'pleasantness', 'value, 'usage', 'aesthetics', and 'materials'. According to a comparative analysis of the two lobby spaces in terms of semantic rating elements, Gyeonggido Museum of Modern Art was perceived to be an orderly, original, open, soft, and female-like space, whereas Seoul Museum of Art was perceived to be aesthetic, restrained, concrete, realistic, intellectual and rational. In the coming years, the results of this study will serve as valuable data for constructing a sensibility rating scale for evaluating spatial environments of museums.

• Korean Journal of Cognitive Science
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• v.17 no.4
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• pp.337-356
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• 2006

This research was conducted to see the impact of brightness, polarity, and hue diference on legibility and emotional effcts of the word. In the experiment 1, stimuli with three levels of brightness difference and two-typed polarity were used. The results showed that legibility, aesthetics, and preference increased with increasing brightness difference. In the experiment 2, the same stimuli if experiment 1 included four hues: red, green, blue, yellow. As a result, the effects of brightness and polarity and the interaction effect of brightness and polarity on legibility were significant. Also, the effects of brightness, polarity, and hue and the interaction effect of brightness and hue on aesthetics and preference were significant. These results showed that legibility, aesthetics, and preference increased with increasing brightness difference of word and background and positive polarity was better than negative. Aesthetics and preference rating increased according to the following order: red, blue, green, yellow. In addition, the interaction effect of brightness and polarity on legibility was because reaction time of negative polarity was longer than positive at the small brightness difference condition. The interaction effect of brightness and hue on aesthetics and preference ws because the aesthetics rating of hue at the large brightness difference condition had significant difference compared with small brightness difference. In the experiment 3, participants rated text designs and simple color stimuli with 18 emotional adjectives to see the similarity of their emotion. The conclusion was that to reflect the subjective feelings of a rotor on the text design, it would be appropriate to use the rotor on background of the text design.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.193-205
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• 2021

The AI speaker market is growing steadily. However, the satisfaction of actual users is only 42%. Therefore, in this paper, we collected reviews on Amazon Echo Dot 3rd and 4th generation models to analyze what hinders the user experience through the topic changes and emotional changes of each generation of AI speakers. By using topic modeling analysis techniques, we found changes in topics and topics that make up reviews for each generation, and examined how user sentiment on topics changed according to generation through deep learning-based sentiment analysis. As a result of topic modeling, five topics were derived for each generation. In the case of the 3rd generation, the topic representing general features of the speaker acted as a positive factor for the product, while user convenience features acted as negative factor. Conversely, in the 4^th generation, general features were negatively, and convenience features were positively derived. This analysis is significant in that it can present analysis results that take into account not only lexical features but also contextual features of the entire sentence in terms of methodology.