• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.640-645
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• 2014

Many people use social network services as to communicate, to share an information and to build social relationships between others on the Internet. Twitter is such a representative service, where millions of tweets are posted a day and a huge amount of data collection has been being accumulated. Social mining that extracts the meaningful information from the massive data has been intensively studied. Typically, Twitter easily can deliver and retweet the contents using the following-follower relationships. Topic modeling in tweet data is a good tool for issue tracking in social media. To overcome the restrictions of short contents in tweets, we introduce a notion of reply graph which is constructed as a graph structure of which nodes correspond to users and of which edges correspond to existence of reply and retweet messages between the users. The LDA topic model, which is a typical method of topic modeling, is ineffective for short textual data. This paper introduces a topic modeling method that uses reply graph to reduce the number of short documents and to improve the quality of mining results. The proposed model uses the LDA model as the topic modeling framework for tweet issue tracking. Some experimental results of the proposed method are presented for a collection of Twitter data of 7 days.

• Journal of Korean Elementary Science Education
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• v.41 no.2
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• pp.356-372
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• 2022

This study examined the topics that have appeared in the "Journal of Korean Elementary Science Education" over the past 50 years to identify the changes that have occurred in the Korean Society of Elementary Science Education. Latent Dirichlet allocation topic modeling was applied to 1,065 English abstracts from the first issue (1983) to 2021, from which 14 main topics were extracted. The meaning of each topic was then analyzed from its keywords and documents. Subsequently, to elucidate the topic trends, the topics' increase or decrease every three years was statistically examined through linear regression analysis. Based on the results, implications for developing and supporting elementary science education research in the future were discussed.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.31-48
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• 2024

The domestic automotive industry must consider a strategic shift from traditional automotive component manufacturing to align with future trends such as connectivity, autonomous driving, sharing, and electrification. This research conducted topic modeling on R&D projects in the future automotive sector funded by the Ministry of Trade, Industry, and Energy from 2013 to 2021. We found that topics such as sensors, communication, driver assistance technology, and battery and power technology remained consistently prominent throughout the entire period. Conversely, topics like high-strength lightweight chassis were observed only in the first period, while topics like AI, big data, and hydrogen fuel cells gained increasing importance in the second and third periods. Furthermore, this research analyzed the areas of concentrated investment for each period based on topic-specific government investment amounts and investment growth rates.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.95-106
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• 2023

Korean healing research has developed over the past 20 years along with the growing social interest in healing. The field of healing research is diverse and includes legislated natural-based healing. In this study, abstracts of 2,202 academic journals, master's, and doctoral dissertations published in KCI and RISS were collected and analyzed. As for the research method, LDA topic modeling used to classify research topics, and time-series publication trends were examined. As a result of the study, it identified that the topic of Korean healing research was connected with 5 types and 4 mediators. The five were "Healing Tourism," "Mind and Art Healing," "Forest Therapy," "Healing Space," and "Youth Restoration and Healing," and the four mediators were "Forest," "Nature," "Culture", and "Education". In addition, only legalized healing studies extracted from Korean healing research and the topics were analyzed. As a result, legalized healing research classified into four. The four types were "Healing Spatial Environment Plan", "Healing Therapy Experiment", "Agricultural Education Experiential Healing", and "Healing Tourism Factor". Forest Therapy, which has the largest amount of research in legalized healing, Agro Healing and Garden Healing which operate similar programs through plants, and Marine Healing using marine resources also analyzed. As a result, topics that show the unique characteristics of individual healing studies and topics that are considered universal in all healing studies derived. This study is significant in that it identified the overall trend of research on Korean healing facilities and programs by utilizing LDA topic modeling.

• Journal of the Korean Society for information Management
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• v.32 no.1
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• pp.153-169
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• 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 the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.88-95
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• 2021

This study aims to provide implications for establishing support policies for students by empirically analyzing data on university students dropouts. To this end, data of students enrolled in D University after 2017 were sampled and collected. The collected data was analyzed using topic modeling(LDA: Latent Dirichlet Allocation) technique, which is a probabilistic model based on text mining. As a result of the study, it was found that topics that were characteristic of dropout students were found, and the classification performance between groups through topics was also excellent. Based on these results, a specific educational support system was proposed to prevent dropout of university students. This study is meaningful in that it shows the use of text mining techniques in the education field and suggests an education policy based on data analysis.

• The Journal of the Korea Contents Association
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• v.19 no.6
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• pp.58-71
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• 2019

The purpose of this study is to explore the multi-dimensionality and the differences of the career success that is revealed by the employee's perception. In order to fulfill the research purpose, LDA topic modeling has applied to extract latent topics of career success from 126 Korean employees' open-end survey questionnaires. The extracted latent topics are social recognition, continuing service within an organization, expertise, financial rewards, and pursuing personal meaning. The occurrence probability of each topic was different by individual characteristics such as gender, education, position. Study findings showed there is multi-dimensionality in career success, and there are differences of topic occurrence probability by demographic characteristics. Additionally, this study showed how to apply the recently developed machine learning approach in order to reduce the researcher's bias by adapting the LDA topic modeling to the qualitative open-ended survey data.

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

• Journal of Digital Convergence
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• v.18 no.10
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• pp.239-252
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• 2020

By using text mining, we explore the factors responsible for an increase in SUV preference. We collected 32,679 posts related to SUVs from "Bobaedream," the largest online automobile community in South Korea, and applied the LDA topic model. While previous studies have explained the SUV boom as an individual's risk aversion strategy from crime, the result shows that the topic of 'Safety' appears to be an important factor in the SUV discourse in the context of a car accident and high-speed driving situation. To conclude, the consumption of SUVs in Korean society serves as a mean to prevent anxiety and danger to individuals when driving. We insist that decreasing social trust, caused by an increase in inequality, underlies the perception of risk on the road.

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