• Journal of Digital Convergence
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• v.17 no.3
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• pp.107-117
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• 2019

In the era of SNS, many people relies on it to express their emotions about various kinds of products and services. Therefore, for the companies eagerly seeking to investigate how their products and services are perceived in the market, emotion mining tasks using dataset from SNSs become important much more than ever. Basically, emotion mining is a branch of sentiment analysis which is based on BOW (bag-of-words) and TF-IDF. However, there are few studies on the emotion mining which adopt feature selection (FS) methods to look for optimal set of features ensuring better results. In this sense, this study aims to propose FS methods to conduct emotion mining tasks more effectively with better outcomes. This study uses Twitter and SemEval2007 dataset for the sake of emotion mining experiments. We applied three FS methods such as CFS (Correlation based FS), IG (Information Gain), and ReliefF. Emotion mining results were obtained from applying the selected features to nine classifiers. When applying DT (decision tree) to Tweet dataset, accuracy increases with CFS, IG, and ReliefF methods. When applying LR (logistic regression) to SemEval2007 dataset, accuracy increases with ReliefF method.

• Journal of Korean Society for Geospatial Information Science
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• v.8 no.1 s.15
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• pp.51-63
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• 2000

Dynamic traffic assignment(DTA) has been a topic of substantial research during the past decade. While DTA is gradually maturing, many aspects of DTA still need improvement, especially regarding its formulation and solution algerian Recently, with its promise for In(Intelligent Transportation System) and GIS(Geographic Information System) applications, DTA have received increasing attention. This potential also implies higher requirement for DTA modeling, especially regarding its solution efficiency for real-time implementation. But DTA have many mathematical difficulties in searching process due to the complexity of spatial and temporal variables. Although many solution algorithms have been studied, conventional methods cannot iud the solution in case that objective function or constraints is not convex. In this paper, the genetic algorithm to find the solution of DTA is applied and the Merchant-Nemhauser model is used as DTA model because it has a nonconvex constraint set. To handle the nonconvex constraint set the GENOCOP III system which is a kind of the genetic algorithm is used in this study. Results for the sample network have been compared with the results of conventional method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.16 no.2
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• pp.251-258
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• 1998

The purpose of this study is to develop the District-level Planning Support System (DPSS) by using GIS. The district-level planning which is related for district-level control of city, needs the various parcel-level information which is composing the urban physical environment. The information has to be stored and analyzed for recognizing the study area, then the district-level planning will be efficiently managed. The use of GIS in the process of district-level planning is restricted for the creation of thematic map. GIS is not used for the analysis of spatial patterns and planning process. This study evaluates the characteristics of current district-level planning and the basic components of urban physical environment. And the database model is built. The topology among components is defined by using the spatial relationship. Then the spatial query machine for district-level planing is developed by using ArcView 3.1, Avenue and Dialog Extension. This spatial query machine is applied for case study. This study shows 1) the possibility of the district-level planning support system for analyzing spatial relationship, 2) the needs of the up-to-date topographic map showing current building's footlines and the complete integration with cadastral maps, it will reduce the uncertainty in the spatial decision making process, 3) the methodology for the construction of spatial decision making rules, 4) the further study for the using of raster, network, image and three dimension data.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.107-129
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• 2022

It is important to preempt new technology because the technology competition is getting much tougher. Stakeholders conduct exploration activities continuously for new technology preoccupancy at the right time. Gartner's Hype Cycle has significant implications for stakeholders. The Hype Cycle is a expectation graph for new technologies which is combining the technology life cycle (S-curve) with the Hype Level. Stakeholders such as R&D investor, CTO(Chef of Technology Officer) and technical personnel are very interested in Gartner's Hype Cycle for new technologies. Because high expectation for new technologies can bring opportunities to maintain investment by securing the legitimacy of R&D investment. However, contrary to the high interest of the industry, the preceding researches faced with limitations aspect of empirical method and source data(news, academic papers, search traffic, patent etc.). In this study, we focused on two research questions. The first research question was 'Is there a difference in the characteristics of the network structure at each stage of the hype cycle?'. To confirm the first research question, the structural characteristics of each stage were confirmed through the component cohesion size. The second research question is 'Is there a pattern of diffusion at each stage of the hype cycle?'. This research question was to be solved through centralization index and network density. The centralization index is a concept of variance, and a higher centralization index means that a small number of nodes are centered in the network. Concentration of a small number of nodes means a star network structure. In the network structure, the star network structure is a centralized structure and shows better diffusion performance than a decentralized network (circle structure). Because the nodes which are the center of information transfer can judge useful information and deliver it to other nodes the fastest. So we confirmed the out-degree centralization index and in-degree centralization index for each stage. For this purpose, we confirmed the structural features of the community and the expectation diffusion patterns using Social Network Serice(SNS) data in 'Gartner Hype Cycle for Artificial Intelligence, 2021'. Twitter data for 30 technologies (excluding four technologies) listed in 'Gartner Hype Cycle for Artificial Intelligence, 2021' were analyzed. Analysis was performed using R program (4.1.1 ver) and Cyram Netminer. From October 31, 2021 to November 9, 2021, 6,766 tweets were searched through the Twitter API, and converting the relationship user's tweet(Source) and user's retweets (Target). As a result, 4,124 edgelists were analyzed. As a reult of the study, we confirmed the structural features and diffusion patterns through analyze the component cohesion size and degree centralization and density. Through this study, we confirmed that the groups of each stage increased number of components as time passed and the density decreased. Also 'Innovation Trigger' which is a group interested in new technologies as a early adopter in the innovation diffusion theory had high out-degree centralization index and the others had higher in-degree centralization index than out-degree. It can be inferred that 'Innovation Trigger' group has the biggest influence, and the diffusion will gradually slow down from the subsequent groups. In this study, network analysis was conducted using social network service data unlike methods of the precedent researches. This is significant in that it provided an idea to expand the method of analysis when analyzing Gartner's hype cycle in the future. In addition, the fact that the innovation diffusion theory was applied to the Gartner's hype cycle's stage in artificial intelligence can be evaluated positively because the Gartner hype cycle has been repeatedly discussed as a theoretical weakness. Also it is expected that this study will provide a new perspective on decision-making on technology investment to stakeholdes.

• Journal of Environmental Impact Assessment
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• v.27 no.4
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• pp.367-377
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• 2018

A cultural ecosystem service(CES), which is non-material benefit that human gains from ecosystem, has been recently further recognized as gross national income increases. Previous researches proposed to quantify the value of CES, which still remains as a challenging issue today due to its social and cultural subjectivity. This study proposes new way of assessing CES which is called Cultural Service Opportunity Spectrum(CSOS). CSOS is accessibility based CES assessment methodology for regional scale and it is designed to be applicable for any regions in Korea for supporting decision making process. CSOS employed public spatial data which are road network and population density map. In addition, the results of 'Rapid Assessment of Natural Assets' implemented by National Institute of Ecology, Korea were used as a complementary data. CSOS was applied to Namyangju-si and the methodology resulted in revealing specific areas with great accessibility to 'Natural Assets' in the region. Based on the results, the advantages and limitations of the methodology were discussed with regard to weighting three main factors and in contrast to Scenic Quality model and Recreation model of InVEST which have been commonly used for assessing CES today due to its convenience today.

• Journal of Distribution Science
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• v.7 no.4
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• pp.47-56
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• 2009

The purpose of this study is to present an alternative improving the efficient and reasonable of the physical distribution system management is influenced by many factors. Therefore, the study depends on the documentary method and survey method to achieve the purpose of this study. The major components of a physical distribution system are refers to as elements, include warehouse·storage system, transportation system, inventory system, physical distribution information system. The factors used in this study are ① factor of product(quality·A/S·added value of product·adaption of product·technical competitive power to other enterprises), ② factor of market(market channel·kinds of customer·physical distribution share), ③ factor of warehouse·storage(warehouse design·size·direction·storage ability·warehouse quality), ④ factor of transportation(promptness·reliability·responsibility·kinds of transportation·cooperation united transportation system·national transportation network), ⑤ factor of packaging (packaging design·material·educating program·pollution degree measure program), ⑥ factor of inventory(ordinary inventory criterion·consistence for inventories record), ⑦ factor of unloaded(unloaded machine·having machine ratio), ⑧ factor of information system (physical distribution quantity analysis·usable computer part), ⑨ factor of physical distribution cost(sales ratio to product) ⑩ factor of physical distribution system(physical distribution center etc). The implication of this study can be summarized as follows: ① In firms that have not adopted a systems integrative approach, physical distribution is a fragmented and often uncoordinated set of activities spread throughout various functions with function having its own set of priorities and measurements. ② The physical distribution is recognized as more an important strategic factor than a simple cost reduction factor, ③ It can be used a strategic competition tool to enterprise.