• Journal of Intelligence and Information Systems
• /
• v.23 no.3
• /
• pp.29-44
• /
• 2017

In Internet commerce, consumers are heavily influenced by product reviews written by other users who have already purchased the product. However, as the product reviews accumulate, it takes a lot of time and effort for consumers to individually check the massive number of product reviews. Moreover, product reviews that are written carelessly actually inconvenience consumers. Thus many online vendors provide mechanisms to identify reviews that customers perceive as most helpful (Cao et al. 2011; Mudambi and Schuff 2010). For example, some online retailers, such as Amazon.com and TripAdvisor, allow users to rate the helpfulness of each review, and use this feedback information to rank and re-order them. However, many reviews have only a few feedbacks or no feedback at all, thus making it hard to identify their helpfulness. Also, it takes time to accumulate feedbacks, thus the newly authored reviews do not have enough ones. For example, only 20% of the reviews in Amazon Review Dataset (Mcauley and Leskovec, 2013) have more than 5 reviews (Yan et al, 2014). The purpose of this study is to analyze the factors affecting the usefulness of online product reviews and to derive a forecasting model that selectively provides product reviews that can be helpful to consumers. In order to do this, we extracted the various linguistic, psychological, and perceptual elements included in product reviews by using text-mining techniques and identifying the determinants among these elements that affect the usability of product reviews. In particular, considering that the characteristics of the product reviews and determinants of usability for apparel products (which are experiential products) and electronic products (which are search goods) can differ, the characteristics of the product reviews were compared within each product group and the determinants were established for each. This study used 7,498 apparel product reviews and 106,962 electronic product reviews from Amazon.com. In order to understand a review text, we first extract linguistic and psychological characteristics from review texts such as a word count, the level of emotional tone and analytical thinking embedded in review text using widely adopted text analysis software LIWC (Linguistic Inquiry and Word Count). After then, we explore the descriptive statistics of review text for each category and statistically compare their differences using t-test. Lastly, we regression analysis using the data mining software RapidMiner to find out determinant factors. As a result of comparing and analyzing product review characteristics of electronic products and apparel products, it was found that reviewers used more words as well as longer sentences when writing product reviews for electronic products. As for the content characteristics of the product reviews, it was found that these reviews included many analytic words, carried more clout, and related to the cognitive processes (CogProc) more so than the apparel product reviews, in addition to including many words expressing negative emotions (NegEmo). On the other hand, the apparel product reviews included more personal, authentic, positive emotions (PosEmo) and perceptual processes (Percept) compared to the electronic product reviews. Next, we analyzed the determinants toward the usefulness of the product reviews between the two product groups. As a result, it was found that product reviews with high product ratings from reviewers in both product groups that were perceived as being useful contained a larger number of total words, many expressions involving perceptual processes, and fewer negative emotions. In addition, apparel product reviews with a large number of comparative expressions, a low expertise index, and concise content with fewer words in each sentence were perceived to be useful. In the case of electronic product reviews, those that were analytical with a high expertise index, along with containing many authentic expressions, cognitive processes, and positive emotions (PosEmo) were perceived to be useful. These findings are expected to help consumers effectively identify useful product reviews in the future.

• Journal of Intelligence and Information Systems
• /
• v.25 no.4
• /
• pp.123-139
• /
• 2019

The job classification system of major job sites differs from site to site and is different from the job classification system of the 'SQF(Sectoral Qualifications Framework)' proposed by the SW field. Therefore, a new job classification system is needed for SW companies, SW job seekers, and job sites to understand. The purpose of this study is to establish a standard job classification system that reflects market demand by analyzing SQF based on job offer information of major job sites and the NCS(National Competency Standards). For this purpose, the association analysis between occupations of major job sites is conducted and the association rule between SQF and occupation is conducted to derive the association rule between occupations. Using this association rule, we proposed an intelligent job classification system based on data mapping the job classification system of major job sites and SQF and job classification system. First, major job sites are selected to obtain information on the job classification system of the SW market. Then We identify ways to collect job information from each site and collect data through open API. Focusing on the relationship between the data, filtering only the job information posted on each job site at the same time, other job information is deleted. Next, we will map the job classification system between job sites using the association rules derived from the association analysis. We will complete the mapping between these market segments, discuss with the experts, further map the SQF, and finally propose a new job classification system. As a result, more than 30,000 job listings were collected in XML format using open API in 'WORKNET,' 'JOBKOREA,' and 'saramin', which are the main job sites in Korea. After filtering out about 900 job postings simultaneously posted on multiple job sites, 800 association rules were derived by applying the Apriori algorithm, which is a frequent pattern mining. Based on 800 related rules, the job classification system of WORKNET, JOBKOREA, and saramin and the SQF job classification system were mapped and classified into 1st and 4th stages. In the new job taxonomy, the first primary class, IT consulting, computer system, network, and security related job system, consisted of three secondary classifications, five tertiary classifications, and five fourth classifications. The second primary classification, the database and the job system related to system operation, consisted of three secondary classifications, three tertiary classifications, and four fourth classifications. The third primary category, Web Planning, Web Programming, Web Design, and Game, was composed of four secondary classifications, nine tertiary classifications, and two fourth classifications. The last primary classification, job systems related to ICT management, computer and communication engineering technology, consisted of three secondary classifications and six tertiary classifications. In particular, the new job classification system has a relatively flexible stage of classification, unlike other existing classification systems. WORKNET divides jobs into third categories, JOBKOREA divides jobs into second categories, and the subdivided jobs into keywords. saramin divided the job into the second classification, and the subdivided the job into keyword form. The newly proposed standard job classification system accepts some keyword-based jobs, and treats some product names as jobs. In the classification system, not only are jobs suspended in the second classification, but there are also jobs that are subdivided into the fourth classification. This reflected the idea that not all jobs could be broken down into the same steps. We also proposed a combination of rules and experts' opinions from market data collected and conducted associative analysis. Therefore, the newly proposed job classification system can be regarded as a data-based intelligent job classification system that reflects the market demand, unlike the existing job classification system. This study is meaningful in that it suggests a new job classification system that reflects market demand by attempting mapping between occupations based on data through the association analysis between occupations rather than intuition of some experts. However, this study has a limitation in that it cannot fully reflect the market demand that changes over time because the data collection point is temporary. As market demands change over time, including seasonal factors and major corporate public recruitment timings, continuous data monitoring and repeated experiments are needed to achieve more accurate matching. The results of this study can be used to suggest the direction of improvement of SQF in the SW industry in the future, and it is expected to be transferred to other industries with the experience of success in the SW industry.

• Journal of the Korean Institute of Traditional Landscape Architecture
• /
• v.38 no.3
• /
• pp.25-34
• /
• 2020

This study examined the definition and major values, the designated procedures and types, and the designation trend in Korean scenic sites and national parks in the United States. Based on this, the analysis of the characteristics of the designation of the two natural heritages. The results are as follows; First, Scenic Sites has characteristics of complex heritage that includes academic, historical, and humanities values on the basis of landscape. As a natural heritage based on public nature, the U.S. National Park aims to contribute to the people's natural heritage and satisfy both ecological and historical values through the protection of the landscape. Second, the designation of a scenic sites are decided through deliberation by the Cultural Heritage Committee after the request of the owner, manager, or local government or by the authority of the head of the Cultural Heritage Administration. The designated survey is divided into basic resource surveys and resource surveys by type. Since the initial designation of the Sogeumgang Mountain in Cheonghakdong, Myeongju in 1970, the number of designated scenic sites was low until the 2000s, but the number of designated scenic sites has increased rapidly since 2006 due to the policy to promote the scenic site, and the proportion of natural and historical and cultural scenic sites has been balanced. The designation of the U.S. national park is decided by the Congress or the president, and the National Park Service makes a series of decisions on whether to conduct a special resource study of provisional resources through a preliminary inspection survey, whether to satisfy the criteria for designation of national parks based on the results of special resource research, and to prioritize them. The U.S. National Parks have been expanded not only by Congress but also by the president's empowerment to designate them as national monuments. With the integrated operation of the National Park Service, the number of designated cases increased as the national park included the heritage sites under the control of various ministries. In addition, a number of historical areas were designated by the enactment of the Historical Site Act, and recreational areas were designated to provide leisure space and classified and managed in a total of 18 units. Third, the comparison of the designation characteristics of the two heritage properties confirmed that the designation of natural heritage with complex value, the classification of types according to complementary designation system and resource characteristics, the establishment of the competent ministry and the balancing of the heritage according to the designation policy. The two heritages had the characteristics of complex natural heritages that met ecological, historical and academic values at the same time based on landscape and public nature. In addition, both countries have identified a system for deliberating the designation of heritage through a basic resource survey and an in-depth designation survey, and classified each type according to the characteristics of the resource. In addition, the policies for promoting scenic sites in Korea and the integrated operation of the National Park Service in the U.S. influenced the designated aspects of the two heritage sites, balancing natural heritage with historical and cultural heritage. Fourth, the resource types and conservation management methods of Scenic site and National Park were largely related. The natural areas of the U.S. National Park include types of natural monuments in Korea as major resources, and have characteristics similar to natural scenic sites. In addition, historical resources were similar to the criteria for designation of historical and cultural scenic sites in terms of landscape, and the aspects of war and celebrity-related relics were related to the types of historic sites. In terms of conservation management, the natural area of the U.S. national park has a way of keeping the original ecosystem intact, but the Korean natural heritage protection system is likely to be useful for focusing on the resource of viscosity. Meanwhile, historical resources include historical sites and historical and cultural scenic sites in the traditional era, but historical relics in the U.S. National Parks have set a time limit to modern times for war history and celebrity-related relics, and the active provision of entertainment programs based on existing resources was derived as a difference.

• Journal of Distribution Science
• /
• v.9 no.3
• /
• pp.37-46
• /
• 2011

Since the Korean retail industry was made accessible to the big conglomerates and foreign retail companies, local traditional markets have faced serious problems. To sustain the local traditional markets' survival, the Korean government established various remedial policies for addressing, and many scholars published articles to suggest how to find solutions to, the problem. Unfortunately, the results have not been satisfactory. The purpose of this study is to find another way to help the Korean traditional retail market, from the view point of the Green Growth Policy, an initiative designed to address environmentally balanced economic growth in Korea. In order to survive and to maintain sustainable growth, it is incumbent upon retailers in the traditional market to understand the concept of the Green Growth Policy. A survey was conducted as a means of testing the degree of awareness of the Green Growth Policy, as well as determining the relationship between the degree of awareness and the degree of organizational commitment by the retailers in the local traditional markets. Interestingly, we were able to detect some of the features (e.g., they were distinguished by the elderly and the young, as well as low level of education and high level of education) in the traditional market retailers' demographic characteristics. We utilized the analysis of variance (ANOVA) statistical method to simultaneously compare the differences in retailers' demographic characteristics; the results were as follows: Overall, the results showed that the awareness of the Green Growth Policy, the degree of trust in the government's policy, levels of self-efficacy, and levels of organizational commitment were higher with the older traditional market retailers than the younger traditional market retailers. Specifically, the degree of trust in government policies (F=9.964,p < .05), levels of self-efficacy (F=5.532,p < .05), and levels of organizational commitment (F=5.697,p < .05) were statistically significant. Moreover, in the portion of the study that addressed the difference between education levels, all the variables were averaged in the higher education category of the traditional market retailers. Specifically, awareness levels of the Green Growth Policy (F=8.564,p < .005) and levels of self-efficacy (F=6.754,p < .005) were statistically significant. These results revealed that the traditional market retailers' demographic characteristics should be considered important factors in order to realize their policy. The results of the study showed the following: 1) The degree of awareness of the government's Green Growth Policy was statistically significant as it related to traditional market retailers' organizational commitment. 2) The degree of trust of the government's policy was significantly moderated between the awareness of the government's Green Growth Policy and the traditional market retailers' organizational commitment. This result demonstrates that the traditional market retailers' awareness of the government's Green Growth Policy will show more organizational commitment with higher levels of trust of the government's policy. 3) It also revealed that traditional market retailers' self-efficacy was fully mediated between the awareness of the Green Growth Policy of the government and traditional market retailers' organizational commitment. The results suggest that the government should show an interest in showing traditional market retailers how to enhance their traditional markets. Implications and future research directions are also discussed.

• Journal of Intelligence and Information Systems
• /
• v.25 no.2
• /
• pp.77-97
• /
• 2019

The large amount of data that emerges from the initial connection environment of the Fourth Industrial Revolution is a major factor that distinguishes the Fourth Industrial Revolution from the existing production environment. This environment has two-sided features that allow it to produce data while using it. And the data produced so produces another value. Due to the massive scale of data, future information systems need to process more data in terms of quantities than existing information systems. In addition, in terms of quality, only a large amount of data, Ability is required. In a small-scale information system, it is possible for a person to accurately understand the system and obtain the necessary information, but in a variety of complex systems where it is difficult to understand the system accurately, it becomes increasingly difficult to acquire the desired information. In other words, more accurate processing of large amounts of data has become a basic condition for future information systems. This problem related to the efficient performance of the information system can be solved by building a semantic web which enables various information processing by expressing the collected data as an ontology that can be understood by not only people but also computers. For example, as in most other organizations, IT has been introduced in the military, and most of the work has been done through information systems. Currently, most of the work is done through information systems. As existing systems contain increasingly large amounts of data, efforts are needed to make the system easier to use through its data utilization. An ontology-based system has a large data semantic network through connection with other systems, and has a wide range of databases that can be utilized, and has the advantage of searching more precisely and quickly through relationships between predefined concepts. In this paper, we propose a defense ontology as a method for effective data management and decision support. In order to judge the applicability and effectiveness of the actual system, we reconstructed the existing air force munitions situation management system as an ontology based system. It is a system constructed to strengthen management and control of logistics situation of commanders and practitioners by providing real - time information on maintenance and distribution situation as it becomes difficult to use complicated logistics information system with large amount of data. Although it is a method to take pre-specified necessary information from the existing logistics system and display it as a web page, it is also difficult to confirm this system except for a few specified items in advance, and it is also time-consuming to extend the additional function if necessary And it is a system composed of category type without search function. Therefore, it has a disadvantage that it can be easily utilized only when the system is well known as in the existing system. The ontology-based logistics situation management system is designed to provide the intuitive visualization of the complex information of the existing logistics information system through the ontology. In order to construct the logistics situation management system through the ontology, And the useful functions such as performance - based logistics support contract management and component dictionary are further identified and included in the ontology. In order to confirm whether the constructed ontology can be used for decision support, it is necessary to implement a meaningful analysis function such as calculation of the utilization rate of the aircraft, inquiry about performance-based military contract. Especially, in contrast to building ontology database in ontology study in the past, in this study, time series data which change value according to time such as the state of aircraft by date are constructed by ontology, and through the constructed ontology, It is confirmed that it is possible to calculate the utilization rate based on various criteria as well as the computable utilization rate. In addition, the data related to performance-based logistics contracts introduced as a new maintenance method of aircraft and other munitions can be inquired into various contents, and it is easy to calculate performance indexes used in performance-based logistics contract through reasoning and functions. Of course, we propose a new performance index that complements the limitations of the currently applied performance indicators, and calculate it through the ontology, confirming the possibility of using the constructed ontology. Finally, it is possible to calculate the failure rate or reliability of each component, including MTBF data of the selected fault-tolerant item based on the actual part consumption performance. The reliability of the mission and the reliability of the system are calculated. In order to confirm the usability of the constructed ontology-based logistics situation management system, the proposed system through the Technology Acceptance Model (TAM), which is a representative model for measuring the acceptability of the technology, is more useful and convenient than the existing system.

• Journal of Intelligence and Information Systems
• /
• v.25 no.2
• /
• pp.167-194
• /
• 2019

This research starts from the four basic concepts of incentive incompatibility, limited information, myopia and decision variable which are confronted when making decisions in keyword bidding. In order to make these concept concrete, four framework approaches are designed as follows; Strategic approach for the incentive incompatibility, Statistical approach for the limited information, Alternative optimization for myopia, and New model approach for decision variable. The purpose of this research is to propose the statistical optimization model in constructing the portfolio of Sponsored Search Advertising (SSA) in the Sponsor's perspective through empirical tests which can be used in portfolio decision making. Previous research up to date formulates the CTR estimation model using CPC, Rank, Impression, CVR, etc., individually or collectively as the independent variables. However, many of the variables are not controllable in keyword bidding. Only CPC and Rank can be used as decision variables in the bidding system. Classical SSA model is designed on the basic assumption that the CPC is the decision variable and CTR is the response variable. However, this classical model has so many huddles in the estimation of CTR. The main problem is the uncertainty between CPC and Rank. In keyword bid, CPC is continuously fluctuating even at the same Rank. This uncertainty usually raises questions about the credibility of CTR, along with the practical management problems. Sponsors make decisions in keyword bids under the limited information, and the strategic portfolio approach based on statistical models is necessary. In order to solve the problem in Classical SSA model, the New SSA model frame is designed on the basic assumption that Rank is the decision variable. Rank is proposed as the best decision variable in predicting the CTR in many papers. Further, most of the search engine platforms provide the options and algorithms to make it possible to bid with Rank. Sponsors can participate in the keyword bidding with Rank. Therefore, this paper tries to test the validity of this new SSA model and the applicability to construct the optimal portfolio in keyword bidding. Research process is as follows; In order to perform the optimization analysis in constructing the keyword portfolio under the New SSA model, this study proposes the criteria for categorizing the keywords, selects the representing keywords for each category, shows the non-linearity relationship, screens the scenarios for CTR and CPC estimation, selects the best fit model through Goodness-of-Fit (GOF) test, formulates the optimization models, confirms the Spillover effects, and suggests the modified optimization model reflecting Spillover and some strategic recommendations. Tests of Optimization models using these CTR/CPC estimation models are empirically performed with the objective functions of (1) maximizing CTR (CTR optimization model) and of (2) maximizing expected profit reflecting CVR (namely, CVR optimization model). Both of the CTR and CVR optimization test result show that the suggested SSA model confirms the significant improvements and this model is valid in constructing the keyword portfolio using the CTR/CPC estimation models suggested in this study. However, one critical problem is found in the CVR optimization model. Important keywords are excluded from the keyword portfolio due to the myopia of the immediate low profit at present. In order to solve this problem, Markov Chain analysis is carried out and the concept of Core Transit Keyword (CTK) and Expected Opportunity Profit (EOP) are introduced. The Revised CVR Optimization model is proposed and is tested and shows validity in constructing the portfolio. Strategic guidelines and insights are as follows; Brand keywords are usually dominant in almost every aspects of CTR, CVR, the expected profit, etc. Now, it is found that the Generic keywords are the CTK and have the spillover potentials which might increase consumers awareness and lead them to Brand keyword. That's why the Generic keyword should be focused in the keyword bidding. The contribution of the thesis is to propose the novel SSA model based on Rank as decision variable, to propose to manage the keyword portfolio by categories according to the characteristics of keywords, to propose the statistical modelling and managing based on the Rank in constructing the keyword portfolio, and to perform empirical tests and propose a new strategic guidelines to focus on the CTK and to propose the modified CVR optimization objective function reflecting the spillover effect in stead of the previous expected profit models.

• Korean Journal of Heritage: History & Science
• /
• v.44 no.1
• /
• pp.196-221
• /
• 2011

The Royal Private Shrines or the Samyo(私廟), were dedicated to members of Choseon's royal family who could not be enshrined at the (official) Royal Ancestral Shrine, the Jongmyo(宗廟). The Samyo were constructed at the national level and were systematically managed as such. Because these private Shrines were dedicated to those who couldn't belong to the Jongmyo but were still very important, such as the ruling king's biological father or mother. The details of all royal constructions were included in the State Event Manuals, and with them, the two-dimensional layouts of the Samyo also. From the remaining "Hyunsa-gung Private Tomb Construction Layout Record(顯思宮別廟營建都監儀軌)" of 1824, which is the construction record of Gyeongwoo-gung Shrine(景祐宮) dedicated to Subin, the mother of King Sunjo(純祖), it became possible to investigate the so far unknown "The Painting of Gyeongwoo-gung Shrine", in terms of the year produced, materials used and other situational contexts. The investigation revealed that the "The Painting of Gyeongwoo-gung Shrine" is actually the "Hyunsa-gung Private Tomb Layout" produced by the Royal Construction Bureau. The bureau painted this to build Hyunsa-gung Private Shrine in a separately prepared site outside the court in 1824, according to the royal verdict to close down and move the temporary shrine inside the courtyard dedicated to Subin who had passed away in 1822. As the Construction Bureau must have also produced the Gyeongwoo-gung Shrine Layout, the painter(s) of this layout should exist among the official artists listed in the State Event Manual, but sadly, as their paintings have not survived to this day, we cannot compare their painting styles. The biggest stylistic character of the Painting of Gyeongwoo-gung Shrine is its perfect diagonal composition method and detailed and neat portrayalof the many palace buildings, just as seen in Donggwoldo(東闕圖, Painting of a panoramic view for Changdeokgung and Changgyeonggung Palaces). A well-perceiving architectural painting employs a specific point of view chosen to fit the purpose of the painting, or it can opt to the multi-viewpoint. Korean traditional architectural paintings in early ages utilized the diagonal composition method, the bird-eye viewpoint, or the multi-viewpoint. By the 18th century, detailed but also artistic architectural paintings utilizing the diagonal method are observed. In the early 19th century, the peak of such techniques is exhibited in Donggwoldo(Painting of a panoramic view for Changdeokgung and Changgyeonggung Palaces). From the perfect diagonal composition method employed and the details of the palace buildings numbering almost two hundreds, we can determine that the Painting of Gyeongwoo-gung Shrine also belongs to the same category of the highly technical architectural paintings as Donggwoldo(Painting of a panoramic view for Changdeokgung and Changgyeonggung Palaces). We can also confirm this hypothesis by comparing the painting techniques employed in these two paintings in detailthe way trees and houses are depicted, and the way ground texture is expressed, etc. The unique characteristic of the Painting of Gyeongwoo-gung Shrine is, however, that the area surrounding the central shrine building(正堂), the most important area of the shrine, is drawn using not the diagonal method but the bird-eye viewpoint with the buildings lying flat on both the left and right sides, just as seen in the "Buildings Below the Central Shrine(正堂以下諸處)" in the State Event Manual's Painting Method section. The same viewpoint method is discovered in some other concurrent paintings of common residential buildings, so it is not certain that this particular viewpoint had been a distinctive feature for shrine paintings in general. On the other hand, when the diagonalmethod pointing to the left direction is chosen, the top-left and bottom-right sections of the painting become inevitably empty. This has been the case for the Painting of Gyeongwoo-gung Shrine, but in contrast, Donggwoldo shows perfect screen composition with these empty margins filled up with different types of trees and other objects. Such difference is consistent with the different situational contexts of these two paintings: the Painting of Gyeongwoo-gung Shrine is a simple single-sheet painting, while Donggwoldo is a perfected work of painting book given an official title. Therefore, if Donggwoldo was produced to fulfill the role of depiction and documentation as well as the aesthetic purpose, contrastingly, the Painting of Gyeongwoo-gung Shrine only served the purpose of copying the circumstances of the architecture and projecting them onto the painting.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.179-196
• /
• 2019

Recently, the "Smart Consumer" has been emerging. He or she is increasingly inclined to search for and purchase products by taking into account personal judgment or expert reviews rather than by relying on information delivered through manufacturers' advertising. This is especially true when purchasing cosmetics. Because cosmetics act directly on the skin, consumers respond seriously to dangerous chemical elements they contain or to skin problems they may cause. Above all, cosmetics should fit well with the purchaser's skin type. In addition, changes in global cosmetics consumer trends make it necessary to study this field. The desire to find one's own individualized cosmetics is being revealed to consumers around the world and is known as "Finding the Holy Grail." Many consumers show a deep interest in customized cosmetics with the cultural boom known as "K-Beauty" (an aspect of "Han-Ryu"), the growth of personal grooming, and the emergence of "self-culture" that includes "self-beauty" and "self-interior." These trends have led to the explosive popularity of cosmetics made in Korea in the Chinese and Southeast Asian markets. In order to meet the customized cosmetics needs of consumers, cosmetics manufacturers and related companies are responding by concentrating on delivering premium services through the convergence of ICT(Information, Communication and Technology). Despite the evolution of companies' responses regarding market trends toward customized cosmetics, there is no "Intelligent Data Platform" that deals holistically with consumers' skin condition experience and thus attaches emotions to products and services. To find the Holy Grail of customized cosmetics, it is important to acquire and analyze consumer data on what they want in order to address their experiences and emotions. The emotions consumers are addressing when purchasing cosmetics varies by their age, sex, skin type, and specific skin issues and influences what price is considered reasonable. Therefore, it is necessary to classify emotions regarding cosmetics by individual consumer. Because of its importance, consumer emotion analysis has been used for both services and products. Given the trends identified above, we judge that consumer emotion analysis can be used in our study. Therefore, we collected and indexed data on consumers' emotions regarding their cosmetics experiences focusing on consumers' language. We crawled the cosmetics emotion data from SNS (blog and Twitter) according to sales ranking ($1^{st}$ to $99^{th}$), focusing on the ample/serum category. A total of 357 emotional adjectives were collected, and we combined and abstracted similar or duplicate emotional adjectives. We conducted a "Consumer Sentiment Journey" workshop to build a "Consumer Sentiment Dictionary," and this resulted in a total of 76 emotional adjectives regarding cosmetics consumer experience. Using these 76 emotional adjectives, we performed clustering with the Self-Organizing Map (SOM) method. As a result of the analysis, we derived eight final clusters of cosmetics consumer sentiments. Using the vector values of each node for each cluster, the characteristics of each cluster were derived based on the top ten most frequently appearing consumer sentiments. Different characteristics were found in consumer sentiments in each cluster. We also developed a cosmetics experience pattern map. The study results confirmed that recommendation and classification systems that consider consumer emotions and sentiments are needed because each consumer differs in what he or she pursues and prefers. Furthermore, this study reaffirms that the application of emotion and sentiment analysis can be extended to various fields other than cosmetics, and it implies that consumer insights can be derived using these methods. They can be used not only to build a specialized sentiment dictionary using scientific processes and "Design Thinking Methodology," but we also expect that these methods can help us to understand consumers' psychological reactions and cognitive behaviors. If this study is further developed, we believe that it will be able to provide solutions based on consumer experience, and therefore that it can be developed as an aspect of marketing intelligence.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.46 no.6
• /
• pp.49-59
• /
• 2018

The purpose of this study was to analyze the importance and satisfaction of resources in the mouth of Nakdong River. A Pearson's chi-square test was performed in SPSS 24.0 for statistical analysis and the result of the study was summarized by three points. First, the results of importance analysis on resources in Nakdong estuary found that the importance of ecology resources was the highest with 27.1%, followed by landscape resources (18.5%), waterside leisure resources (6.5%), complex cultural resources (5.4%), and historic and cultural resources (3.3%). The probability values (p-value) of each group had shown significant differences depending on gender, age, and the location of the survey. For instance, women respondents reported a higher preference to ecology resources and complex cultural resources such as museums than men respondents as much as two times and three times, respectively. Meanwhile, men respondents showed a higher preference to waterside leisure resources in three times as much as women respondents. As for the analysis by age, the respondents in their 20s and 30s recorded a higher value than those in other age groups, and people in their 30s reported a higher preference to waterside leisure resources than those in different age groups by three times. Lastly, no significant differences were found in the preference analysis by occupation (p>.05). With regard to the results of satisfaction analysis, the average level of satisfaction on landscape resources was 6.01, and that of ecology resources and complex cultural resource were 5.65 and 5.15, respectively. Also, significant differences were found between landscape and ecology resources in the satisfaction analysis by age, landscape resources by age, ecology resources by region, and between landscape resources and ecology resources by occupation. The p-value of complex cultural resources was p=0.012, although the satisfaction level of landscape resources and ecology resources were reported to have no significant differences by age. As for the level of satisfaction in landscape resources, respondents in their 40s and 50s showed a high level of satisfaction. However, those in their 20s showed a relatively low level of satisfaction in the same category. The survey respondents living in Busan and South Gyeongsang Province and those living outside the regions revealed no significant differences in terms of satisfaction in landscape resources and complex cultural resources. However, the two same groups were found to show significant differences in the satisfaction analysis on ecology resources. In the satisfaction analysis of landscape resources and ecology resources by occupation, significant differences were found among college students, government employees, ordinary citizens, and expert groups. However, they showed no significant differences in the level of satisfaction to complex cultural resources. Third, the results of importance-satisfaction analysis on Nakdong estuary found that the average levels of satisfaction to landscape resources for each group of respondents who considered landscape, ecology, and cultural resources as important was 6.19, 6.08, and 5.67, respectively. Their levels of satisfaction on ecology resources were 5.95, 5.57, and 5.41 for each. Its correlation to the importance was insignificant. However, it was confirmed that the correlation to the level of satisfaction on complex cultural resources had a significant difference (p=0.025). In addition, the results of the analysis on 15 detailed items that was carried out with the aim to improving values and vitalizing resources in the mouth of Nakdong River found that respondents considered that the vitalization of eco-tourism (49.5%) and restoration of reed marsh (47.5%) were important. The results of detailed analysis revealed respondents' high awareness on the need of enhancing values on ecology resources. Also, improving infrastructure nearby the mouth, creating cycling routes, walkways, waterside leisure facilities, and others were considered as the requirements for the vitalization of Nakdong estuary.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.57-73
• /
• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.