• Annual Conference on Human and Language Technology
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• 2011.10a
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• pp.41-46
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• 2011

스마트폰의 대중화와 함께 그에 내장된 GPS를 활용하여 컨텐츠를 제공하는 서비스들이 점차 늘어나고 있다. 그러나 이런 컨텐츠를 단지 위도, 경도 좌표 정보만을 기초로 구성하게 되면 실제 그 위치가 가지는 의미적 특성을 제대로 반영하지 못하게 된다. 사용자의 위치를 기반으로 그에 맞는 서비스를 제공하기 위해서는 장소의 토픽을 고려해야한다. 본 논문은 장소에 내재된 토픽을 바탕으로 한 기사 추천 방법을 제안한다. 장소와 관련된 문서로부터 장소의 토픽을 표현하고 그 토픽을 기사 추천에 이용한다. 제안한 방법이 실제로 장소에 내재된 토픽을 잘 반영함을 보이고 또한 이를 바탕으로 장소와 관련된 적합한 기사를 추천하는 것을 보여준다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1637-1646
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• 2009

Recently many researchers have been studying various methods for data integration. Among the integration methods that the researchers have studied, there are a method using metadata repository, and Topic Map which identifies the relationships between the data. This study suggests Meta-Topic Map to create Topic Map about search keyword by applying metadata and Topic Map, and the XMDR as a way to connect Meta-Topic Map with metadata in the legacy system. Considering the semantic relationship of user's keyword in the legacy system, the Meta-Topic Map provides the Topic Map format and generates the Topic Map about user's keyword. The XMDR performs structural integration through solving the problem of heterogeneity among metadata in the legacy system. The suggested svides isproves the interoperability among existing Relational Database constructed in the legacy system and the search efficiency and is efficient in expanding the system.

• Journal of the Korean Regional Science Association
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• v.39 no.1
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• pp.3-20
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• 2023

This study aims to categorize the 14 major gentrified commercial areas of Seoul and analyze their characteristics based on their sense of place. To achieve this, we conducted hierarchical cluster analysis using text data collected from Naver Blog. We divided the districts into two dimensions: "experience" and "feature" and analyzed their characteristics using LDA (Latent Dirichlet Allocation) of the text data and statistical data collected from Seoul Open Data Square. As a result, we classified the commercial districts of Seoul into 5 categories: 'theater district,' 'traditional cultural district,' 'female-beauty district,' 'exclusive restaurant and medical district,' and 'trend-leading district.' The findings of this study are expected to provide valuable insights for policy-makers to develop more efficient and suitable commercial policies.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.11b
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• pp.1018-1022
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• 2003

인터넷의 급격한 발달로 인해 현재의 인터넷은 시멘틱웹 기반의 인터넷 환경으로 가고 있다. 인터넷 3D 가상환경 표준인 VRML 도 이러한 추세에 맞추어 X3D로 변모하고 있다. 이러한 환경의 변화에 따른 네비게이션 에이드도 필요해 졌다 본 논문에서는 토픽맵이 가지고 있는 구조적 특성을 이용하여 사용자가 인간사고와 유사한 과정을 통한 네비게이션 정보 습득과 풍부하고 연관된 지식을 습득할 수 있으며 투어코스를 결정하는데 도움을 주는 시스템을 제안한다 본 시스템은 가상환경구조를 기억하거나 시스템 조작을 위한 일상적인 문제점에서 벗어나 본래의 네비게이션 목적에 집중할 수 있도록 만들었다. 가상환경에서의 네비게이션을 통해 현실세계에 존재하는 장소를 사전방문 하거나 효과적인 투어계획을 만드는데 도움을 줄 수 있을 것으로 기대된다.

• Journal of KIISE:Software and Applications
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• v.41 no.8
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• pp.557-568
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• 2014

Recently, software projects have been increasing and getting complex. Due to the large number of submitted bug reports, developers' workload increases. Generally in bug triage process, the triagers assign the bug report to fixer (developer) in order to resolve the bug. However, bug reports have been reassigned to other developers because fixers are not suitable. This is why the triagers did not correctly check and understand the bug report and decide the appropriate developers to fix the bug. This results in increase of developers' time and efforts in software maintenance. To resolve these problems, in this paper, we propose a novel method for developer recommendation based on topic model and social network. First, we build a basis of topic(s) from bug reports. Next, when a new bug report (test data set) comes, we select the most similar topic(s) and extract the participated developers from the topic(s). Finally, by applying social network, we analyze the developers' behavior (comment and commit activity) and recommend the appropriate developers. In this paper we compare our work with related studies through performance experiments on open source projects. The results show that our approach is more effective than other studies in bug triage.

• Journal of the Korean Society for Library and Information Science
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• v.49 no.4
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• pp.179-201
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• 2015

The subject heading is an elaborate access tool for subject browsing and searching in information retrieval environment. The purpose of this study is to suggest the applicable facets to subject headings in Korea. First, the concepts of subject and the definitions of facets were investigated in the literature review. Second, six cases including OCLC's FAST, PRECIS, "Thesaurus construction and use", CC $7^{th}$ edition, BC $2^{nd}$ Edition, and UDC $3^{rd}$ Edition were analyzed to focus on configuration of facets as case studies. Based on the results, twenty-two facets were proposed including Topical, Event, Geography, Chronology, Personal and Corporate Name, Title, Form, Genre, Language, and Person facets as 11 top facets. Also, Topical-Thing/Entity and Topical-Action/Status, Part, Kind, Property, Whole, Material, Patient, Product, By-Product and Agent facets as sub-facets of Topical facet.

• Journal of the Korean association of regional geographers
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• v.23 no.2
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• pp.376-387
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• 2017

This paper attempts to analyze the geographical characters of Twitter data and presents analysis potentials for social network analysis in geography. First, this paper suggests a methodology for a topic modeling-based approach in order to identify the geographical characteristics of tweets, including an analysis flow of Twitter data sets, tweet data collection and conversion, textural pre-processing and structural analysis, topic discovery, and interpretation of tweets' topics. GPS coordinates referencing tweets(geotweets) were extracted among sampled Twitter data sets because it contains the tweet place where it was created. This paper identifies a correlated relationship between some specific topics and local places in Jeju. This correlation is closely associated with some place names and local sites in Jeju Island. We assume it is the intention of tweeters to record their tweet places and to share and retweet with other tweeters in some cases. A surface density map shows the hotspots of tweets, detecting around some specific places and sites such as Jeju airport, sightseeing sites, and local places in Jeju Island. The hotspots show similar patterns of the floating population of Jeju, especially the thirty-year age group. In addition, a topic modeling algorithm is applied for the geographical topic discovery and comparison of the spatial patterns of tweets. Finally, this empirical analysis presents that Twitter data, as social network data, provide geographical significance, with topic modeling approach being useful in analyzing the textural features reflecting the geographical characteristics in large data sets of tweets.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.57-79
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• 2022

COVID-19, which started in December 2019, has had a great impact on our lives in general, including politics, economy, society, and culture, and activities in sports and arts have also been significantly reduced. In the case of sports, sports for all fields in which ordinary citizens participate were particularly affected, and cases of infection in places closely related to people's lives, such as gyms, table tennis, and badminton clubs, also amplified the social fear of the spread of COVID-19. Therefore, in this study, we analyzed news articles related to sports for all at the time when COVID-19 was first spread, and investigated what issues were emerging and being discussed in the sports for all field under the COVID-19 situation. Specifically, we collected news articles dealt with sports for all issues under the COVID-19 situation from Korea's leading portal news sites and identified key sports for all issues by performing topic modeling on these articles. Through the analysis, we found meaningful issues such as COVID-19 outbreak in sports facilities and support for sports activities. In addition, through wordcloud analysis of these major issues, we visually understood the issues and identified the changes in these issues over time.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.4
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• pp.515-536
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• 2022

The purpose of this study is to understand the content characteristics in YouTube videos of the National Archives of Korea. For this purpose, keywords, video data, and viewer responses were collected for 324 videos posted by the National Archives of Korea for five years since April, 2017. Social network analysis, topic modeling, and content analysis were performed. Based on this, the main keywords leading the YouTube videos of the National Archives of Korea, 7 major topics and 20 sub-topics were identified. The characteristics of the YouTube videos and keywords network were studies. In addition, video characteristics were analyzed as external characteristics, video editing and delivery methods, and content characters. The study found that the YouTube channel of the National Archives of Korea has been posting the videos related to various topics such as places, history, and events as well as the basic functions of the archives to induce viewers' interest in the archives. The study also identified the areas that needed to be improved such as low response from viewers, lack of content that could interest viewers, and lack of channel operation to interact or communicate with viewers. Finally, the study was concluded with a proposal to spread the videos of the National Archives of Korea to more users.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.4
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• pp.5-22
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• 2021

This study aimed to understand how people perceive the transition from offline to online classes at universities during the COVID-19 pandemic. To achieve the goal, we collected tweets related to online classes on Twitter and performed sentiment and time series topic analysis. We have the following findings. First, through the sentiment analysis, we found that there were more negative than positive opinions overall, but negative opinions had gradually decreased over time. Through exploring the monthly distribution of sentiment scores of tweets, we found that sentiment scores during the semesters were more widespread than the ones during the vacations. Therefore, more diverse emotions and opinions were showed during the semesters. Second, through time series topic analysis, we identified five main topics of positive tweets that include class environment and equipment, positive emotions, places of taking online classes, language class, and tests and assignments. The four main topics of negative tweets include time (class & break time), tests and assignments, negative emotions, and class environment and equipment. In addition, we examined the trends of public opinions on online classes by investigating the changes in topic composition over time through checking the proportions of representative keywords in each topic. Different from the existing studies of understanding public opinions on online classes, this study attempted to understand the overall opinions from tweet data using sentiment and time series topic analysis. The results of the study can be used to improve the quality of online classes in universities and help universities and instructors to design and offer better online classes.