• 산업경영시스템학회지
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• 제44권4호
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• pp.113-127
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• 2021

Topic modeling has been receiving much attention in academic disciplines in recent years. Topic modeling is one of the applications in machine learning and natural language processing. It is a statistical modeling procedure to discover topics in the collection of documents. Recently, there have been many attempts to find out topics in diverse fields of academic research. Although the first Department of Industrial Engineering (I.E.) was established in Hanyang university in 1958, Korean Institute of Industrial Engineers (KIIE) which is truly the most academic society was first founded to contribute to research for I.E. and promote industrial techniques in 1974. Korean Society of Industrial and Systems Engineering (KSIE) was established four years later. However, the research topics for KSIE journal have not been deeply examined up until now. Using topic modeling algorithms, we cautiously aim to detect the research topics of KSIE journal for the first half of the society history, from 1978 to 1999. We made use of titles and abstracts in research papers to find out topics in KSIE journal by conducting four algorithms, LSA, HDP, LDA, and LDA Mallet. Topic analysis results obtained by the algorithms were compared. We tried to show the whole procedure of topic analysis in detail for further practical use in future. We employed visualization techniques by using analysis result obtained from LDA. As a result of thorough analysis of topic modeling, eight major research topics were discovered including Production/Logistics/Inventory, Reliability, Quality, Probability/Statistics, Management Engineering/Industry, Engineering Economy, Human Factor/Safety/Computer/Information Technology, and Heuristics/Optimization.

• Journal of Korea Trade
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• 제25권2호
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• pp.22-45
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• 2021

Purpose - This study aims to identify new attributes by analyzing reviews conducted by global automaker customers and to examine the influence of these attributes on satisfaction ratings in the U.S. automobile sales market. The present study used J.D. Power for customer responses, which is the largest online review site in the USA. Design/methodology - Automobile customer reviews are valid data available to analyze the brand personality of the automaker. This study collected 2,998 survey responses from automobile companies in the U.S. automobile sales market. Keyword analysis, topic modeling, and the multiple regression analysis were used to analyze the data. Findings - Using topic modeling, the author analyzed 2,998 responses of the U.S. automobile brands. As a result, Topic 1 (Competence), Topic 5 (Sincerity), and Topic 6 (Prestige) attributes had positive effects, and Topic 2 (Sophistication) had a negative effect on overall customer responses. Topic 4 (Conspicuousness) did not have any statistical effect on this research. Topic 1, Topic 5, and Topic 6 factors also show the importance of buying factors. This present study has contributed to identifying a new attribute, personality. These findings will help global automakers better understand the impacts of Topic 1, Topic 5, and Topic 6 on purchasing a car. Originality/value - Contrary to a traditional approach to brand analysis using questionnaire survey methods, this study analyzed customer reviews using text mining. This study is timely research since a big data analysis is employed in order to identify direct responses to customers in the future.

• 복식문화연구
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• 제31권1호
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• pp.53-69
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• 2023

This study aims to analyze research trends regarding outdoor wear. For this purpose, the data-collection period was limited to January 2002-October 2022, and the collection consisted of titles of papers, academic names, abstracts, and publication years from the Research Information Sharing Service (RISS). Frequency analysis was conducted on 227 papers in total to check academic journals and annual trends, and LDA topic-modeling analysis was conducted using 20,964 tokens. Data pre-processing was performed prior to topic-modeling analysis; after that, topic-modeling analysis, core topic derivation, and visualization were performed using a Python algorithm. A total of eight topics were obtained from the comprehensive analysis: experiential marketing and lifestyle, property and evaluation of outdoor wear, design and patterns of outdoor wear, outdoor-wear purchase behavior, color, designs and materials of outdoor wear, promotional strategies for outdoor wear, purchase intention and satisfaction depending on the brand image of outdoor wear, differences in outdoor wear preferences by consumer group. The results of topic-modeling analysis revealed that the topic, which includes a study on the design and material of outdoor wear and the pattern of jackets related to the overall shape, was the highest at 30.9% of the total topics. The next highest topic was also the design and color of outdoor wear, indicating that design-related research was the main research topic in outdoor wear research. It is hoped that analyzing outdoor wear research will help comprehend the research conducted thus far and reveal future directions.

• Journal of Information Science Theory and Practice
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• 제9권1호
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• pp.35-53
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• 2021

The study reported in this paper aimed to evaluate the topics and opinions of COVID-19 discussion found on Twitter. It performed topic modeling and sentiment analysis of tweets posted during the COVID-19 outbreak, and compared these results over space and time. In addition, by covering a more recent and a longer period of the pandemic timeline, several patterns not previously reported in the literature were revealed. Author-pooled Latent Dirichlet Allocation (LDA) was used to generate twenty topics that discuss different aspects related to the pandemic. Time-series analysis of the distribution of tweets over topics was performed to explore how the discussion on each topic changed over time, and the potential reasons behind the change. In addition, spatial analysis of topics was performed by comparing the percentage of tweets in each topic among top tweeting countries. Afterward, sentiment analysis of tweets was performed at both temporal and spatial levels. Our intention was to analyze how the sentiment differs between countries and in response to certain events. The performance of the topic model was assessed by being compared with other alternative topic modeling techniques. The topic coherence was measured for the different techniques while changing the number of topics. Results showed that the pooling by author before performing LDA significantly improved the produced topic models.

• 한국정보시스템학회지:정보시스템연구
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• 제28권4호
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• pp.271-291
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• 2019

Purpose The purpose of this study is to propose a methodology to derive positive keywords and negative keywords through deep learning to classify reviews into positive reviews and negative ones, and then refine the results of topic modeling using these keywords. Design/methodology/approach In this study, we extracted topic keywords by performing LDA-based topic modeling. At the same time, we performed attention-based deep learning to identify positive and negative keywords. Finally, we refined the topic keywords using these keywords as filters. Findings We collected and analyzed about 6,000 English reviews of Gyeongbokgung, a representative tourist attraction in Korea, from Tripadvisor, a representative travel site. Experimental results show that the proposed methodology properly identifies positive and negative keywords describing major topics.

• 한국치위생학회지
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• 제22권6호
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• pp.495-502
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• 2022

Objectives: The purpose of this study was to analyze research trends in dental hygiene using topic modeling and semantic network analysis. Methods: A total of 261 published studies were collected 686 key words from the Research Information Sharing Service (RISS) by 2019-2021. Topic modeling and semantic network analysis were performed using Textom. Results: The most frequently and frequency-inverse document frequently key words were 'dental hygienist', 'oral health', 'elderly', 'periodontal disease', 'dental hygiene'. N-gram of key words show that 'dental hygienist-emotional labor', 'dental hygienist-elderly', 'dental hygienist-job performance', 'oral health-quality of life', 'oral health-periodontal disease' etc. were frequently. Key words with high degree centrality were 'dental hygienist (0.317)', 'oral health (0.239)', 'elderly (0.127)', 'job satisfaction (0.057)', 'dental care (0.049)'. Extracted topics were 5 by topic modeling. Conclusions: Results from the current study could be available to know research trends in dental hygiene and it is necessary to improve more detailed and qualitative analysis in follow-up study.

• 한국의류학회지
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• 제46권6호
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• pp.1004-1022
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• 2022

This study seeks to investigate K-fashion's external image by examining the trends in global media reporting. It applies Dynamic Topic Modeling (DTM), which captures the evolution of topics in a sequentially organized corpus of documents, and consists of text preprocessing, the determination of the number of topics, and a timeseries analysis of the probability distribution of words within topics. The data set comprised 551 online media articles on 'Korean fashion' or 'K-fashion' published on Google News between 2010 and 2021. The analysis identifies seven topics: 'brand look and style,' 'lifestyle,' 'traditional style,' 'Seoul Fashion Week (SFW) event,' 'model size,' 'K-pop,' and 'fashion market,' as well as annual topic proportion trends. It also explores annual word changes within the topic and indicates increasing and decreasing word patterns. In most topics, the probability distribution of the word 'brand' is confirmed to be on the increase, while 'digital,' 'platform,' and 'virtual' have been newly created in the 'SFW event' topic. Moreover, this study confirms the transition of each K-fashion topic over the past 12 years, along with various factors related to Hallyu content, traditional culture, government support, and digital technology innovation.

• Journal of Information Science Theory and Practice
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• 제9권2호
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• pp.47-65
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• 2021

This study aims to identify specific topics, trends, and structural characteristics of scholarly communication research, based on 1,435 articles published from 1970 to 2018 in the Scopus database through Latent Dirichlet Allocation topic modeling, serial analysis, and network analysis. Topic modeling, time series analysis, and network analysis were used to analyze specific topics, trends, and structures, respectively. The results were summarized into three sets as follows. First, the specific topics of scholarly communication research were nineteen in number, including research resource management and research data, and their research proportion is even. Second, as a result of the time series analysis, there are three upward trending topics: Topic 6: Open Access Publishing, Topic 7: Green Open Access, Topic 19: Informal Communication, and two downward trending topics: Topic 11: Researcher Network and Topic 12: Electronic Journal. Third, the network analysis results indicated that high mean profile association topics were related to the institution, and topics with high triangle betweenness centrality, such as Topic 14: Research Resource Management, shared the citation context. Also, through cluster analysis using parallel nearest neighbor clustering, six clusters connected with different concepts were identified.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제8권1호
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• pp.13-18
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• 2019

본 연구는 커뮤니케이션이 활발한 SNS 속에서 사회적 이슈가 어떤 주제별로 나뉘어져 있고, 어떤 키워드들이 유기적으로 연결되었는지 그 연결 관계를 알아보고자 하였다. '미투'라는 새로운 단어가 생겨남과 동시에 큰 운동으로 번지고 있는 '미투운동'을 사회적 이슈로 간주하였고, 여러 SNS 중 특히 실시간 소통이 가장 활발한 트위터를 중심으로 분석을 실시하였다. 우선 키워드를 '미투'로 하여 관련된 키워드를 각 날짜별로 추출하였고, 주요 키워드를 파악한 후 토픽 모델링을 수행하였다. 이를 통해 사회적 이슈를 둘러싼 키워드들이 시간의 흐름에 따라 어떻게 변화하였는지 파악하고, 각 토픽 내의 키워드를 종합하여 토픽별 사회적 이슈의 다양한 관점을 해석하였다.

• 실내환경 및 냄새 학회지
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• 제17권4호
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• pp.322-329
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• 2018

The objective of this study is to identify the research trend in the field of indoor environment in Korea. We collected 419 papers published in the Journal of the Korean Society for indoor environment between 2004 and 2018, and attempted to produce datasets using a topic modeling technique, Latent Dirichlet Allocation(LDA). The result of topic modeling showed that 8 topics ("VOCs investigation", "Subway environment", "Building thermal environment", "School health", "Building particulate matter", "Asbestos risk", "Radon risk", "Air cleaner and treatment") could be extracted using Gibbs sampling method. In terms of topic trends, investigation of volatile organic compounds, subway environment, school health, and building particulate matter showed a decreasing tendency, while the building thermal environment, asbestos risk, radon risk, air cleaners, and air treatment showed an increasing tendency. The results of this topic modeling could help us to understand current trends related indoor environment, and provide valuable information in developing future research and policy frameworks.