• Journal of the Korean Regional Science Association
• /
• v.34 no.4
• /
• pp.19-34
• /
• 2018

The purpose of this study is to analyze the seasonal issues using the local newspaper articles with the keyword beach from 2004 to 2017. Topic modeling and Time series regression analysis based on open source programs were performed for analysis. Topic modeling results showed 35 topics in spring, 47 topics in summer, 36 topics in autumn and 35 topics in winter. The common themes were 'beaches', 'festivals and events', 'accident and environmental issues', 'tourism', 'development and sale', 'administration and policy' and 'weather'. Time series regression analysis showed in the spring, 5 Hot-Topics and 2 Cold-Topic were found out of the 35 topics. In the summer, 6 Hot-Topics and 3 Cold-Topic were found out of the 47 topics. In the autumn, 4 Hot-Topics and 3 Cold-Topic were found out of the 36 topics. In the winter, 3 Hot-Topics and 3 Cold-Topic were found out of the 35 topics. And for each season, topics that do not fall into the Hot-Topic and Cold-Topic are classified as Neutral-Topic. In this study if seasonal uses are different such as beaches are deemed that seasonal topic modeling for analysis of regional issues will yield more useful results and enable detailed diagnosis.

• Journal of Korean Library and Information Science Society
• /
• v.47 no.4
• /
• pp.1-18
• /
• 2016

The purpose of this study is to explore application of topic modeling for topic analysis of book report. Topic modeling can be understood as one method of topic analysis. This analysis was conducted with texts in 23 book reports using LDA function of the "topicmodels" package provided by R. According to the result of topic modeling, 16 topics were extracted. The topic network was constructed by the relation between the topics and keywords, and the book report network was constructed by the relation between book report cases and topics. Next, Centrality analysis was conducted targeting the topic network and book report network. The result of this study is following these. First, 16 topics are shown as network which has one component. In other words, 16 topics are interrelated. Second, book report was divided into 2 groups, book reports with high centrality and book reports with low centrality. The former group has similarities with others, the latter group has differences with others in aspect of the topics of book reports. The result of topic modeling is useful to identify book reports' topics combining with network analysis.

• Journal of the Microelectronics and Packaging Society
• /
• v.27 no.2
• /
• pp.53-58
• /
• 2020

Analysis of electric semiconductor packaging technology for electric vehicles was performed. Topic modeling using LDA technique was performed by collecting valid patents by deriving valid patents. It was classified into 20 topics, and the definition of technology was defined through extracted words for each topic. In order to analyze the trend of each topic, the trend of power semiconductor packaging technology was analyzed by deriving hot and cold topics by topic through regression analysis on frequency by year. The package structure technology according to the withstand voltage, the input/output-related control technology and the heat dissipation technology were derived as the hot topic technology, and the inductance reduction technology was derived as the cold topic technology.

• Journal of the Korean Society for information Management
• /
• v.32 no.3
• /
• pp.397-412
• /
• 2015

The main goal of this study is to investigate how to route a question to some relevant users who have interest in the topic of the question based on users' topic interest. In order to assess users' topic interest, archived question-answer pairs in the community were used to identify latent topics in the chosen categories using LDA. Then, these topic models were used to identify users' topic interest. Furthermore, the topics of newly submitted questions were analyzed using the topic models in order to recommend relevant answerers to the question. This study introduces the process of topic modeling to investigate relevant users based on their topic interest.

• Annual Conference on Human and Language Technology
• /
• 2022.10a
• /
• pp.164-169
• /
• 2022

토픽 모델링은 비즈니스 분석이나 기술 동향 파악 등 다방면에서 많이 사용되고 있는 기술이다. 하지만 대표적인 방법인 LDA와 같은 비지도학습의 경우, 그 알고리즘 구조상 문서의 수가 많을 때 토픽 모델링이 가능하다. 본 논문에서는 문서의 수가 적은 경우도, 키워드 및 키프레이즈를 이용한 군집화를 통해 토픽 모델링을 하고 감성분석을 통해 토픽에 대한 분석도 제시하였다. 이에 필요한 데이터 제작 및 키워드 추출, 키워드 기반 감성분석, 키워드 임베딩 및 군집화를 구현하였고, 결과를 정성적으로 보았을 때 유의미한 분석이 되는 것을 확인하였다.

• Journal of the Korean Society for information Management
• /
• v.32 no.1
• /
• pp.153-169
• /
• 2015

The combined approach of using ego-centric network analysis and dynamic citation network analysis for refining the result of LDA-based topic modeling was suggested and examined in this study. Tow datasets were constructed by collecting Web of Science bibliographic records of White LED and topic modeling was performed by setting a different number of topics on each dataset. The multi-assigned top keywords of each topic were re-assigned to one specific topic by applying an ego-centric network analysis algorithm. It was found that the topical cohesion of the result of topic modeling with the number of topic corresponding to the lowest value of perplexity to the dataset extracted by SPLC network analysis was the strongest with the best values of internal clustering evaluation indices. Furthermore, it demonstrates the possibility of developing the suggested approach as a method of multi-faceted research trend detection.

• Journal of Digital Contents Society
• /
• v.19 no.5
• /
• pp.981-993
• /
• 2018

Smart healthcare is convergence of ICT and healthcare services, and interdisciplinary research has been actively conducted in various fields. The objective of this study is to investigate trends of smart healthcare research using topic modeling and ego network analysis. Text analysis, frequency analysis, topic modeling, word cloud, and ego network analysis were conducted for the abstracts of 2,690 articles in Scopus from 2001 to April 2018. Topic Modeling analysis resulted in eight topics, Topics included "AI in healthcare", "Smart hospital", "Healthcare platform", "Blockchain in healthcare", "Smart health data", "Mobile healthcare", " Wellness care", "Cognitive healthcare". In order to examine the topic modeling results core deeply, we analyzed word cloud and ego network analysis for eight topics. This study aims to identify trends in smart healthcare research and suggest implications for establishing future research direction.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2021.07a
• /
• pp.329-331
• /
• 2021

본 연구에서는 코로나19 관련 연구논문의 연구주제를 탐색하고 동향을 검토하고 있다. 또한 감성분석을 통해 부정적인 어조가 강한 경고가 되는 주제들을 알아본다. 잠재 디리슐레 할당(LDA)를 이용하여 총 8개의 토픽을 발견하 였고, 이를 구조적 토픽 모델링(STM)과 비교하여 비교적 안정적인 결과임을 확인하였다. 또한 k-means 군집 알고리즘을 통해 각 토픽별로 세부 연구주제를 발견하였고 주성분 분석을 이용하여 이를 시각적으로 표현하였다. 감성분석을 통해 각 토픽별 긍정적, 부정적인 단어들을 살펴보고 감성점수를 계산하여 연구논문의 주된 어조를 파악하였는데, 특히 생물 의학 관련, 국제적 역학관계, 심리적 영향과 관련된 연구에서 부정적인 어조가 강한 것으로 나타나 해당 부문에 대해서 주의와 관심이 요구된다. 향후 연구자들이 연구의 방향성을 탐색하고 정책결정자들이 연구지원 사업을 결정하는데 기초자료로 활용될 수 있을 것이다.

• Journal of Korean Library and Information Science Society
• /
• v.48 no.2
• /
• pp.53-72
• /
• 2017

The purpose of this study is to analyze types and features social issues about the Gyeongju earthquakes 2016, South Korea. The specific purpose is to identify types of topics related to Gyeongju Earthquakes, changes of topics over time, and the differences of topics depending on the each type of newspapers. According to the result of topic modeling, 55 topics were extracted. The result of this study is following these. First, the main topics have been changed with the course of time. In September, various topics were emerged, specifically urgent issues was found during two weeks after the first earthquake. After October, topics about social problems derived from the earthquakes received much attention at that time. Topics related to safety problems about nuclear plant have steadily found in all period. Second, topics varied depending whether the newspaper is national or regional. Also, differences of topics were found when dividing the newspapers by their characteristics considered conservative or liberal.

• Journal of the Korean Society for information Management
• /
• v.40 no.1
• /
• pp.175-198
• /
• 2023

This study aimed to investigate the difference in response to reading as shown in book reports by personality type and interest type. For this purpose, personality type analysis data, interest type analysis data, and book report data written in subject reading activities were collected from 81 third graders at D Science High School in Daejeon. Topic analysis was conducted on the collected book reports, and the probability of a topic being mentioned was statistically tested according to personality type (thinking type, feeling type) and interest type (investigative type, types other than investigative). Subsequently, the conceptual connection structure of words was measured by keyword network analysis, and the analysis results of topic modeling were complemented by the centrality index. As a result of the study, the topic regression analysis showed statistically significant differences between thinking type (T) and feeling type (F) in topic 2 (understanding and studying) and topic 3 (reading and thinking), and statistically significant differences between investigative type and non-investigative type in topic 2 (understanding and studying). The results of this study can be used as a basis for tailored book recommendations and personalized reading education.