• Land and Housing Review
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• v.15 no.3
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• pp.113-127
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• 2024

This study explores shifts in real estate investment sentiment using media reports from 2012 to 2022, segmenting the market dynamics into three distinct cycles based on housing and land transaction indices. Leveraging 54 BigKinds media sources, we investigates 3,387 headlines and 8,544 body texts using LDA topic modeling. The results show that the first cycle (2012-2015 ) centered on apartment pre-sales, where policy changes influenced sentiment but did not consistently affect investment decisions. The second cycle (2016-2018) was characterized by interest rate hikes and rising property prices in Seoul, resulting in significant fluctuations in transaction volumes. The third cycle (2019-2022) encompassed the effects of COVID-19, market instability, and policy failures, leading to distorted and weakened investment sentiment. Each cycle demonstrated that policies, interest rates, and economic events significantly shaped investor sentiment, as reflected in media reports.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• Journal of Science Education
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• v.45 no.3
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• pp.292-303
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• 2021

This study identifies the terms frequently used together with energy in online science news articles and topics of the news reports to find out how the term energy is used in everyday life and to draw implications for science curriculum and instruction about energy. A total of 2,171 online news articles in science category published by 11 major newspaper companies in Korea for one year from March 1, 2018 were selected by using energy as a search term. As a result of natural language processing, a total of 51,224 sentences consisting of 507,901 words were compiled for analysis. Using the R program, term frequency analysis, semantic network analysis, and structural topic modeling were performed. The results show that the terms with exceptionally high frequencies were technology, research, and development, which reflected the characteristics of news articles that report new findings. On the other hand, terms used more than once per two articles were industry-related terms (industry, product, system, production, market) and terms that were sufficiently expected as energy-related terms such as 'electricity' and 'environment.' Meanwhile, 'sun', 'heat', 'temperature', and 'power generation', which are frequently used in energy-related science classes, also appeared as terms belonging to the highest frequency. From a network analysis, two clusters were found including terms related to industry and technology and terms related to basic science and research. From the analysis of terms paired with energy, it was also found that terms related to the use of energy such as 'energy efficiency,' 'energy saving,' and 'energy consumption' were the most frequently used. Out of 16 topics found, four contexts of energy were drawn including 'high-tech industry,' 'industry,' 'basic science,' and 'environment and health.' The results suggest that the introduction of the concept of energy degradation as a starting point for energy classes can be effective. It also shows the need to introduce high-tech industries or the context of environment and health into energy learning.

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.331-350
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• 2022

This study aims to understand topics of incivility related to COVID-19 from analyzing Twitter posts including COVID-19-related hate speech. To achieve the goal, a total of 63,802 tweets that were created between December 1st, 2019, and August 31st, 2021, covering three targets of hate speech including region and public facilities, groups of people, and religion were analyzed. Frequency analysis, dynamic topic modeling, and keyword co-occurrence network analysis were used to explore topics and keywords. 1) Results of frequency analysis revealed that hate against regions and public facilities showed a relatively increasing trend while hate against specific groups of people and religion showed a relatively decreasing trend. 2) Results of dynamic topic modeling analysis showed keywords of each of the three targets of hate speech. Keywords of the region and public facilities included "Daegu, Gyeongbuk local hate", "interregional hate", and "public facility hate"; groups of people included "China hate", "virus spreaders", and "outdoor activity sanctions"; and religion included "Shincheonji", "Christianity", "religious infection", "refusal of quarantine", and "places visited by confirmed cases". 3) Similarly, results of keyword co-occurrence network analysis revealed keywords of three targets: region and public facilities (Corona, Daegu, confirmed cases, Shincheonji, Gyeongbuk, region); specific groups of people (Coronavirus, Wuhan pneumonia, Wuhan, China, Chinese, People, Entry, Banned); and religion (Corona, Church, Daegu, confirmed cases, infection). This study attempted to grasp the public's anti-citizenship public opinion related to COVID-19 by identifying domestic COVID-19 hate targets and keywords using social media. In particular, it is meaningful to grasp public opinion on incivility topics and hate emotions expressed on social media using data mining techniques for hate-related to COVID-19, which has not been attempted in previous studies. In addition, the results of this study suggest practical implications in that they can be based on basic data for contributing to the establishment of systems and policies for cultural communication measures in preparation for the post-COVID-19 era.

• The Journal of the Korea Contents Association
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• v.19 no.11
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• pp.153-167
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• 2019

This study aims to identify the media agenda and to compare each media agenda by media and by time period, analyzing the news about 2019 Gangwon-do's wild fire reported by 37 Korean news media. Using the topic modeling algorithm and semantic network analysis, this study inspected the configuration of the network media agenda and examined the intermedia agenda setting effect by using QAP correlation analysis. Results showed that the sensational media agenda with the attributes such as victim aid and political conflict and the similarity of each media agenda for this disaster reporting.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.275-277
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• 2019

The purpose of this study is to classify the topics related to the suicide papers published so far and to identify the proporations of the main topics and the trends of the topics over the past 20 years. For this purpose, a text mining technique used in big data analysis was used as a data base of the Korean Journal of Citation Index (KCI), where information sharing about the papers is most active. This study, which grasps the trends of suicide related research according to the changes of the times, will become a basic data for establishing a strategy to adapt the academic direction related to suicide in the future.

• Journal of the Korean Society for information Management
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• v.22 no.3 s.57
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• pp.289-306
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• 2005

As FRBR defines structural framework based on ER modeling for bibliographic data elements, an effective tool is required to implement FRBR model. In this paper, we present the implementation of FRBR model based on Topic Maps. To show the effectiveness of Topic Maps as the implantation language of FRBR, we implement FRBR model of MyongSungHwangHu by means of Topic Maps. We can ascertain that topic-association of Topic Maps conceptually harmonize with entity-relation of FRBR, which means that Topic Maps is suitable for the implementation of FRBR model.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.267-270
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• 2018

구제역으로 인하여 국내 축산업계 및 관련 산업분야는 매년 막대한 피해를 입고 있다. 구제역과 관련한 다양한 학술적 연구들이 현재 진행되고는 있으나, 구제역의 발병에 따른 사회적 파급효과에 관한 공학적 분석 연구는 매우 제한적이다. 본 연구에서는 구제역에 관한 일반 시민들의 감성적 반응을 텍스트 마이닝 방법론을 사용하여 분석하는 체계적인 방법론을 제안한다. 제안하는 시스템은 먼저, 트위터에 게시된 트윗 중 구제역과 관련된 데이터를 수집한 후, 감성사전을 기반으로 극성탐지 과정을 거친다. 둘째, 토픽 모델링의 대표적인 기법 중 하나인 LDA를 활용하여 트윗으로 부터 키워드들을 추출하고, 추출된 키워드들로부터 극성별 동시출현 키워드 네트워크를 구성한다. 셋째, 키워드 네트워크을 통해 각 구간별 구제역의 사회적 파급효과를 분석한다. 사례 분석으로써, 2010년 7월부터 2011년 12월까지 국내에서 발생한 구제역에 관한 일반 시민들의 감성적 변화를 분석하였다.

• Journal of KIISE:Software and Applications
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• v.33 no.9
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• pp.787-801
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• 2006

Ontology merging describes the process of integrating two ontologies into a new ontology. How this is done best is a subject of ongoing research in the Semantic Web, Data Integration, Knowledge Management System, and other ontology-related application systems. Earlier research on ontology merging, however, has studied for developing effective ontology matching approaches but missed analyzing and solving methods of problems of merging two ontologies given correspondences between them. In this paper, we propose a specific ontology merging process and a generic operator, T-MERGE, for integrating two source ontologies into a new ontology. Also, we define a taxonomy of merging conflicts which is derived from differing representations between input ontologies and a method for detecting and resolving them. Our T-MERGE operator encapsulates the process of detection and resolution of conflicts and merging two entities based on given correspondences between them. We define a data structure, MergeLog, for logging the execution of T-MERGE operator. MergeLog is used to inform detailed results of execution of merging to users or recover errors. For our experiments, we used oriental philosophy ontologies, western philosophy ontologies, Yahoo western philosophy dictionary, and Naver philosophy dictionary as input ontologies. Our experiments show that the automatic merging module compared with manual merging by a expert has advantages in terms of time and effort.

• Journal of Korean Library and Information Science Society
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• v.51 no.4
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• pp.101-119
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• 2020

The study aims to understand major topics and concerns of gender identity-related questions expressed by the users of the NAVER social Q&A site. To achieve this goal, we analyzed 2,120 questions created from 2010 to 2018 using natural language- and information retrieval-based methods. Results indicated that the major topics discussed by the users include interpersonal relationships, doubts about gender identity, sexual orientation, feelings and relationships, and concerns about gender identity. In addition, users mainly expressed concerns regarding general issues of gender identity; sexual orientation; negative cognition about gender identity; confession, coming-out, homosexuality; future, heterosexual relationships, military enlistment; and causes of gender identity confusion. The present study effectively derives information needs from real-world concerns about sexual identity by employing topic modeling techniques, and by comparing the advantages of exact match and tf-idf-based information retrieval methods extends methodology of Library and Information Science. Further, it has contributed to the academic maturity of the study of information behavior by observing the information needs or information-seeking behaviors of online community users with specific interests.