• 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.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• Science of Emotion and Sensibility
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• v.19 no.1
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• pp.95-110
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• 2016

Uncanny valley refers to the condition where the affinity of a human-like object decreases dramatically if the object becomes extremely similar to human, and has been hypothesized to derive from the cognitive load of categorical conflict against an uncanny object. According to the hypothesis, the present study ran an oddball task consisting of trials each displaying one among a non-human, human and uncanny face, and measured event-related potentials (ERPs) for each trial condition. In Experiment 1, a non-human face was presented in 80% of the trials (standard) whereas a human face for another 10% trials (target) and an uncanny face for the remaining 10% trials (uncanny). Participants' responses were relatively inaccurate and delayed in both the target and uncanny oddball trials, but neither P3 nor N170 component differed across the three trial conditions. Experiment 2 used 3-D rendered realistic faces to increase the degree of categorical conflict, and found the behavioral results were similar to Experiment 1. However, the peak amplitude of N170 of the target and uncanny trials were higher than the standard trials while P3 mean amplitudes for both the target and uncanny trials were comparable but higher than the amplitude for the standard trials. P3 latencies were delayed in the order of the standard, target, and uncanny trials. The changes in N170 and P3 patterns across the experiments appear to arise from the categorical conflict that the uncanny face must be categorized as a non-target according to the oddball-task requirement despite its perceived category of a human face. The observed increase of cognitive load following the added reality to the uncanny face also indicates that the cognitive load, supposedly responsible for the uncanny experience, would depend on the increase of categorical conflict information subsequent to added stimulus complexity.

• Science of Emotion and Sensibility
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• v.21 no.4
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• pp.63-76
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• 2018

The purpose of this study was to investigate the effect of contact type textile electrode structure on heart activity signal acquisition for smart healthcare. In this study, we devised six contact type textile electrodes whose electrode size and configuration were manipulated for measuring heart activity signals using computerized embroidery. We detected heart activity signals using a modified lead II and by attaching each textile electrode to the chest band in four healthy male subjects in a standing static posture. We measured the signals four times repeatedly for all types of electrodes. The heart activity signals were sampled at 1 kHz using a BIOPAC ECG100, and the detected original signals were filtered through a band-pass filter. To compare the performance of heart activity signal acquisition among the different structures of the textile electrodes, we conducted a qualitative analysis using signal waveform and size as parameters. In addition, we performed a quantitative analysis by calculating signal power ratio (SPR) of the heart activity signals obtained through each electrode. We analyzed differences in the performance of heart activity signal acquisition of the six electrodes by performing difference and post-hoc tests using nonparametric statistic methods on the calculated SPR. The results showed a significant difference both in terms of qualitative and quantitative aspects of heart activity signals among the tested contact type textile electrodes. Regarding the configurations of the contact type textile electrodes, the three-dimensionally inflated electrode (3DIE) was found to obtain better quality signals than the flat electrode. However, regarding the electrode size, no significant difference was found in performance of heart signal acquisition for the three electrode sizes. These results suggest that the configuration method (flat/3DIE), which is one of the two requirements of a contact type textile electrode structure for heart activity signal acquisition, has a critical effect on the performance of heart activity signal acquisition for wearable healthcare. Based on the results of this study, we plan to develop a smart clothing technology that can monitor high-quality heart activity without time and space constraints by implementing a clothing platform integrated with the textile electrode and developing a performance improvement plan.

• Science of Emotion and Sensibility
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• v.25 no.1
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• pp.41-54
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• 2022

The purpose of this study was to propose a "user experience (UX)-centered product development process" so that the product design development process using the service design process can be systematized and used in practice. In a situation in which usability research on office phones is lacking compared to general home phones, this study expands to a product-based service design point of view rather than simple product development, intending to research ways to provide user experience value through office phone design in smart office. This study focused on extracting UX-centered user needs using the service design process and developing product design that realizes user experience value. In particular, the service design process was applied to systematically extract user needs and experience value elements in the product development process and to discover ideas that were converged with product-based services. For this purpose, the "Double Diamond Design Process Model," which is widely used in the service design field, was adopted. In addition, a product design development process was established so that usability improvement plans, user experience value elements, and product-service connected ideas could be extracted through a work-flow in which real users and people from various fields participate. Based on the double diamond design process, in the "Discover" information collection stage, design trends were identified mainly in the office phone markets. In the "Define" analysis and extraction stage, user needs were analyzed through user observation, interview, and usability survey, and design requirements and user experience issues were extracted. Persona was set through user type analysis, and user scenarios were presented. In the "Develop" development stage, ideation workshops and concept renderings were conducted to embody the design, and people from various fields within the company participated to set the design direction reflecting design preference and usability improvement plans. In the "Deliver" improvement/prototype development/evaluation stage, a working mock-up of a design prototype was produced and design and usability evaluation were conducted through consultation with external design experts. It is meaningful that it established a "UX-centered product development process" model that converged with the existing product design development process and service design process. Ultimately, service design-based product design development process was presented so that I Corp.'s products could realize user experience value through service convergence.

• The Journal of the Korea Contents Association
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• v.14 no.1
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• pp.488-504
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• 2014

The purpose of this study was to identify whether environmental evidence and experiences respectively could affect brand attitude in Korean restaurant. Also, this study investigated a moderating effect of brand reputation in the causal relationship between environmental evidence and brand attitude and, between experiences and brand attitude. Questionnaires were given to a sample of customers who visited branches of H or B Korean restaurant brand which ranked within 20th in sales volume among Korean restaurant brands. A total of 353 questionnaires were analyzed with hierarchical regression analysis using SPSS/PC+. There were four major empirical research findings. Firstly, among components of environmental evidence, space, aesthetics, cleanliness and pleasance affected brand attitude. Secondly, all components of experiences, act, relate, think and feel affected brand attitude. Thirdly, brand reputation had a moderating role in the causal relationship between aesthetics dimension and brand attitude, but space, cleanliness, convenience and pleasance dimensions did not have a moderating role of brand reputation to the relationship of brand attitude. Fourth, brand reputation had a moderating role in the causal relationship between act and brand attitude, but relate, think and feel didn't have a moderating role of brand reputation to the relationship of brand attitude. Based on these findings, marketers of Korean restaurants were recommended to strengthen environmental evidence and experiences. Also, they are advised to strengthen brand reputation with use of marketing.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.471-492
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• 2017

The demand and interest in big data analytics are increasing rapidly. The concepts around big data include not only existing structured data, but also various kinds of unstructured data such as text, images, videos, and logs. Among the various types of unstructured data, text data have gained particular attention because it is the most representative method to describe and deliver information. Text analysis is generally performed in the following order: document collection, parsing and filtering, structuring, frequency analysis, and similarity analysis. The results of the analysis can be displayed through word cloud, word network, topic modeling, document classification, and semantic analysis. Notably, there is an increasing demand to identify trending topics from the rapidly increasing text data generated through various social media. Thus, research on and applications of topic modeling have been actively carried out in various fields since topic modeling is able to extract the core topics from a huge amount of unstructured text documents and provide the document groups for each different topic. In this paper, we review the major techniques and research trends of text analysis. Further, we also introduce some cases of applications that solve the problems in various fields by using topic modeling.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.715-721
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• 2018

This study examined energy saving and elevating the worker's neuro energy (comfort, concentration, physical, and psychological stability) by compensating for the unpleasant tactile sensation to stimulate auditory and olfactory senses and reduce energy consumption. The experiment was conducted in an environment test room under the test conditions of temperature $25[^{\circ}C]$, relative humidity 50[RH%], illumination 1,000[lux] and air current speed 0.02[m/sec] by stimulating the auditory senses with a 1/f change in rhythm and the olfactory senses with an aroma scent. The experiment utilized the method of EEG, which evaluates human body's psychological status via tactile means, and the method of the vibra image, which evaluates the learning abilities, HRV and human body's psychological status via non-tactile means. The subjects were selected as eight university students (four males and four females) in their 20s, the type that have high relative ${\alpha}$(8~13[Hz]) activation in occipital lobe, which brings the highest level of mind stability and concentration, who had no difficulty in physical activities. The subjects' posture and physical activity was fixed to 1met - when the subjects are seated and relaxing in a comfortable environment - and their clothes condition was standardized as 0.7clo. As a result, the sentimental and psychological stability and concentration were the highest in the multisensory stimulation of jasmine scent and change rhythm of an a=1.106 sound source. In addition, under this condition, the relative $M{\alpha}$ and relative $M{\beta}$ increased by 70.49[%] and 89.72[%], respectively; the HRT decreased by 39.09[%]; and the fatigue and tension/anxiety decreased by 36.85[%] and 15.54[%], respectively.

• Archives of design research
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• v.14 no.3
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• pp.37-48
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• 2001

This paper is a study on how women consumers purchase are affected by models who appear in advertisements for cosmetics, focusing especially on studies concerning the impact that models have on advertisement strategies of the cosmetic industry in korea. In surveys conducted, consumers responded that cosmetic advertisement models should examplify a expertness and trustworthiness attitude more than just display their own physical attractiveness. The consumers who bought cosmetic products based on its endorsement from particular models responded that they had a positive reaction to the models physical attractiveness and likability while experiencing a negative reaction to the model's expertness and trustworthiness attitude. Women consumers are interested in cosmetic advertisement models, but do not necessarily trust them. Hence, the use of a Particular model does not directly affect the consumers Purchasing decision. Famous stars often appear in cosmetic advertisements in korea, and targeted consumers have a very positive response to their physical attractiveness, familiarity and perceived likability. However, the consumers have a completely negative response to the models in regards to their expertness, trustworthiness, and their sense of similarity with the model. The models, then, should be used in these advertisements to try and uphold the fellowing qualities. expertness in regards to having some knowledge of, experience with, and expertness in using the cosmetic produces, trustworthiness when expressing their own opinion of the product, matching image of products and targeted consumers.

• The Journal of the Korea Contents Association
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• v.21 no.9
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• pp.75-87
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• 2021

The purpose of this study is to derive the characteristics of interaction design for each stage of content composition so that it can be referred to in the planning and production of virtual reality safety education contents. It was confirmed that each of the following interaction design features was found in the three configuration steps: acquisition of situation response procedure knowledge, accident situation experiential learning, and content confirmation and evaluation. First, it was revealed that the quality of experience was controlled by increasing the fidelity of behaviors and reducing general and repetitive behaviors in order to emphasize the educational content-related experiences in the learner experience stage. Second, in order for learners to easily recognize main interaction objects in order to acquire information on safe behavior procedures in unfamiliar environments, use of spatial UI or signifiers using arrows or symbols, posts that specifically instruct actions, and multisensory signals Therefore, it was found to be important to emphasize essential actions in a way that lowers the degree of freedom of user experience, and the proportion of non-realistic interactions for cognitive interactions was found to increase. Lastly, in the confirmation and evaluation stage of the experience, it is important to use the meta UI to alleviate negative experiences such as physical damage after experiencing a safety accident situation,