• Journal of Industrial Convergence
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• v.20 no.1
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• pp.43-51
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• 2022

The purpose of this study is to provide basic information necessary for the establishment of mobile service marketing strategies, educational service development, and engineering education for Generation Z by analyzing the utilitization of various applications by Gen Z. To this end, 177 essays on mobile service usage experience were collected, major topics were analyzed using topic modeling, and these were visualized through word cloud analysis. As a result of the study, the main topics were related to 'transportation' such as movement and public transportation, 'personal management' such as schedule management, financial management, food management, 'transaction' such as checkout, meeting, purchase, 'leisure' such as eating out, travel, study, culture. Additionally, words such as time, thought, people, life, bus, information, confirmation, payment, KakaoTalk, and so on were found to have a high of frequency of use. Also, there was found to be a difference between topics by college. This study is meaningful in that it collected essays, which are unstructured data, and analyzed them through topic modeling.

• The Journal of Society for e-Business Studies
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• v.21 no.1
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• pp.119-129
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• 2016

With the development of web, amount of information are generated in social web. Then many researchers are focused on the extracting and analyzing social issues from various social data. The proposed approach performed gathering the science data and analyzing with LDA algorithm. It generated the clusters which represent the social topics related to 'health'. As a result, we could deduce the relationship between science data and social issues.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.763-778
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• 2022

In this study we aim to identify the core technologies that play central roles along with the peripheral technologies that contribute to the technology convergence in digital healthcare. A total of 376 korean-patents related to healthcare were gathered from 2011 to 2020, and a topic modeling technique and a network analysis were conducted on the collected data. Six major topics were derived through the topic modeling procedure which are "data collection", "signal measurement", "health management", "data transmission", "diagnostic treatment", and "measurement device". Each of the six topics were analyzed to depict relations among technologies, specify the convergence characteristics, and identify the core-technology through centrality analysis. The study illustrates the present status of digital healthcare technology development and the technological convergence in South Korea and is anticipated to help establish policies to foster healthcare industry.

• Journal of Convergence for Information Technology
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• v.10 no.2
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• pp.39-48
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• 2020

Many studies have been conducted to analyze factors that affect customer satisfaction, and service quality improvement in the parcel delivery industry. Most of these studies have a limited number of respondents using methods such as surveys and interviews. Therefore, this study aims to supplement the shortcomings of previous studies, by searching and analyzing the common major topics related to the complaints pointed out by consumers and suppliers in the parcel delivery service with cases of consumer counseling, and articles that reflect the complaints of workers in the industry. In addition, by analyzing the trend of these topics, we attempted to discover new topics and suggest implications. In conclusion, topics such as delay/lost/wrong deliveries as well as the fierce competition in the parcel delivery industry, turned out to be central aspects. As a result of the topic trend analysis, talks with international couriers have recently increased, and many conflicts related to apartment parcel delivery have been dealt with. The topics presented in this study are mainly focused on the contents of previous studies, but we expect that new and valuable topics can be derived by adding other data and analysis methods, such as internal counseling and academic literature.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.727-729
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• 2022

본 연구는 2021 게임업계 연쇄 파동을 통해 게임사 이슈 대응에 대한 사용자들의 인식이 바뀐 것에 주목하여 메이플스토리 확률 조작 사건에서 나타난 사용자들의 반응을 토픽모델링으로 분석하였다. 이를 위해 사건의 발단이 된 메이플스토리 테스트 월드 업데이트 내용이 업로드된 2021년 2월 18일 17시를 기점으로 국내 온라인 게임 커뮤니티 중 하나인 인벤의 자유게시판에서 총 10만 개의 게시물을 수집하고 토픽모델링 분석을 실시하였다. 이후 도출된 주제별 주요 단어를 10개씩 확인하여 주제를 정의했다. 각 토픽을 비교하며 관련성을 확인했고 이를 통해 사용자들의 반응을 분석한 결과 확률 조작으로 인한 보상으로 환불을 원하고 있다는 것과 아이템의 확률을 조작했다는 것에 대한 사용자들의 분노, 디렉터 본인의 직접적인 사과문과 사용자와의 소통 요구, 또 다른 게임으로의 이탈을 확인할 수 있었다.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.15 no.2
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• pp.71-96
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• 2004

The purpose of this study is to describe major concepts related to W3C OWL and ISO Topic Maps and to provide the result of comparison and analysis regarding semantic expression power between two ontology languages. This paper is comprised of the following parts: 1) describing URI and namespace concepts that are fundamental building block of effective ontology construction; 2) offering detailed explanation of major Topic Map concepts such as topics, associations, and occurrences; 3) providing how to accomplish the second purpose of cataloging(grouping related items when displaying the search result) using Topic Map; and 4) finally explaining the difference between two ontology languages in terms of semantic expression power.

• The Journal of the Korea Contents Association
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• v.22 no.6
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• pp.1-14
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• 2022

This study aims to identify topics in newspaper articles related to fine particle matter and to investigate the characteristics and time series trend of each topic. Related national newspaper articles during 1990 and 2021 were collected from Bigkinds. A total of 18 topics have been discovered using LDA, and 11 clusters deduced from clustering. Hot topics include related products/residence, overseas cause(China), power plant as a domestic cause, nationwide emergency reduction measures, international cooperation, political issues, current situation & countermeasure in other countries, and consumption patterns. Cold topics include the concentration standard and indoor air quality improvement. These findings would be useful in inferring the political direction and strategies. In particular, the consumer protection policy should be expanded as the related market is growing. It will also be necessary to pursue policies that will promote public safety and health, and that will enhance public consensus and international cooperation.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.151-159
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• 2015

This article analyzes English abstracts of the articles published in Journal of the Korean Data & Information Science Society using text mining techniques. At first, term-document matrices are formed by various methods and then visualized by social network analysis. LDA (latent Dirichlet allocation) and CTM (correlated topic model) are also employed in order to extract topics from the abstracts. Performances of the topic models are compared via entropy for several numbers of topics and weighting methods to form term-document matrices.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.349-350
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• 2023

우리나라는 지속적인 저출산과 고령화로 인해 지방 소멸 위험지역이 점차 늘어나고 있다. 본 연구는 지방 소멸과 관련된 다양한 요인을 '인구 소멸'이라는 키워드를 포함하는 신문 기사에 대한 토픽모델링을 통해 발견하고, 추출된 토픽과 관련된 공공 데이터를 수집하여 비슷한 특징을 가지는 지역을 묶는 군집분석을 수행한다. 그리고 지방소멸위험지수로 분류된 소멸 위험지역과 군집분석 결과를 비교한다.