• Explosives and Blasting
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• v.40 no.1
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• pp.1-16
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• 2022

In order to develop effective and safe blasting techniques or to introduce foreign advanced blasting techniques to domestic industry, the analysis of research trend in blasting field in the world is essential. In generally, such a research trend analysis was carried out for limited number of published papers. In this study, a bibliometric analysis was performed using VOSviewer for the overall papers published in international journals to figure out the variation of research trend in blasting area. From the keyword analysis, it was found that the number of published papers and the number of overall keywords was limited in the 2000s. Since 2010, the number of published papers was increased rapidly and the keywords were diversified with the introduction of artificial intelligence(AI). The keyword analysis for 2017~2021 showed that various hybrid AI techniques were actively applied in the evaluation of blasting effect.

• Journal of the Korean Society for information Management
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• v.27 no.3
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• pp.83-102
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• 2010

This study aims to understand and compare the characteristics of two major approaches to solving global environmental problems - an academic approach including scholarly activities of environmental sciences and a practical approach of environmental movements led by NGOs - by employing informetric analysis methods. Knowledge structure of environmental sciences is depicted through co-citation networks of subject categories assigned to the cited journals in the discipline of environmental sciences for the 10-year period from 2000 to 2009. Furthermore, major interests of environmental NGOs are identified on the basis of external link data collected from web sites of the NGOs. Co-word analyses are also performed using the texts of journal papers in environmental sciences as well as news articles provided by NGO sites. Through the analyses, dominant subject areas of environmental sciences and environmental movements are identified demonstrating similarities and differences between the two approaches.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.4
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• pp.285-307
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• 2019

In 2011, the Korean Medical Association revised the name of schizophrenia to remove the social stigma for the sick. Although it has been about nine years since the revision of the terminology, no studies have quantitatively analyzed how much social awareness has changed. Thus, this study investigates the changes in social awareness of schizophrenia caused by the revision of the disease name by analyzing Naver news articles related to the disease. For text analysis, LDA topic modeling, TF-IDF, word co-occurrence, and sentiment analysis techniques were used. The results showed that social awareness of the disease was more negative after the revision of the terminology. In addition, social awareness of the former term among two terms used after the revision was more negative. In other words, the revision of the disease did not resolve the stigma.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.32 no.3
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• pp.247-264
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• 2021

This study aims to create a deep learning-based classification model to classify suicide tendency by suicide corpus constructed for the present study. Also, to analyze suicide factors, the study classified suicide tendency corpus into detailed topics by using topic modeling, an analysis technique that automatically extracts topics. For this purpose, 2,011 documents of the suicide-related corpus collected from social media naver knowledge iN were directly annotated into suicide-tendency documents or non-suicide-tendency documents based on suicide prevention education manual issued by the Central Suicide Prevention Center, and we also conducted the deep learning model(LSTM, BERT, ELECTRA) performance evaluation based on the classification model, using annotated corpus data. In addition, one of the topic modeling techniques, LDA identified suicide factors by classifying thematic literature, and co-word analysis and visualization were conducted to analyze the factors in-depth.

• Korean Journal of Ornithology
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• v.25 no.2
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• pp.126-132
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• 2018

For balanced development of ornithological research in Korea, it is important to review what birds and what research topics have been studied so far. We quantitatively investigated the trends of domestic ornithological research using text-mining and co-word analysis. As a result of studying 372 articles published in the Korean Journal of Ornithology, which is the most representative ornithological journals, words related to research topics such as population and community monitoring, first record of species and breeding ecology, and heavy metal pollution in birds have been widely used in research articles. Except for subjects such as monitoring and first record of species, studies have not been conducted widely. It was also found that research were concentrated on specific birds such as Anas platyrhynchos, Calidris alpina, and Anas poecilorhyncha. The present study, which analyzed the research topics and avian taxa that were relatively active until now and those which were insufficient, suggests what we should do in the future for the balanced development of ornithological research in Korea.

• Journal of the Korean Society for information Management
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• v.30 no.1
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• pp.285-302
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• 2013

This study identified topic shifts and patterns over time by analyzing an enormous amount of Twitter data whose characteristics are high accessibility and briefness. First, we extracted keywords for a certain product and used them for representing the topic network allows for intuitive understanding of keywords associated with topics by nodes and edges by co-word analysis. We conducted temporal analysis of term co-occurrence as well as topic modeling to examine the results of network analysis. In addition, the results of comparing topic shifts on Twitter with the corresponding retrieval results from newspapers confirm that Twitter makes immediate responses to news media and spreads the negative issues out quickly. Our findings may suggest that companies utilize the proposed technique to identify public's negative opinions as quickly as possible and to apply for the timely decision making and effective responses to their customers.

• Journal of the Korean Society for Library and Information Science
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• v.51 no.4
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• pp.121-138
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• 2017

As information users seek multimedia with a wide variety of information needs, information environments for multimedia have been developed drastically. More specifically, as seeking multimedia with emotional access points has been popular, the needs for indexing in terms of abstract concepts including emotions have grown. This study aims to analyze the index terms extracted from Getty Image Bank. Five basic emotion terms, which are sadness, love, horror, happiness, anger, were used when collected the indexing terms. A total 22,675 index terms were used for this study. The data are three sets; entire emotion, positive emotion, and negative emotion. For these three data sets, co-word occurrence matrices were created and visualized in weighted network with PNNC clusters. The entire emotion network demonstrates three clusters and 20 sub-clusters. On the other hand, positive emotion network and negative emotion network show 10 clusters, respectively. The results point out three elements for next generation of multimedia retrieval: (1) the analysis on index terms for emotions shown in people on image, (2) the relationship between connotative term and denotative term and possibility for inferring connotative terms from denotative terms using the relationship, and (3) the significance of thesaurus on connotative term in order to expand related terms or synonyms for better access points.

• Journal of the Korean Society for information Management
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• v.38 no.2
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• pp.153-172
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• 2021

This study is to create a deep learning based classification model to examine the characteristics of panic disorder and to classify the panic disorder tendency literature by the panic disorder corpus constructed for the present study. For this purpose, 5,884 documents of the panic disorder corpus collected from social media were directly annotated based on the mental disease diagnosis manual and were classified into panic disorder-prone and non-panic-disorder documents. Then, TF-IDF scores were calculated and word co-occurrence analysis was performed to analyze the lexical characteristics of the corpus. In addition, the co-occurrence between the symptom frequency measurement and the annotated symptom was calculated to analyze the characteristics of panic disorder symptoms and the relationship between symptoms. We also conducted the performance evaluation for a deep learning based classification model. Three pre-trained models, BERT multi-lingual, KoBERT, and KcBERT, were adopted for classification model, and KcBERT showed the best performance among them. This study demonstrated that it can help early diagnosis and treatment of people suffering from related symptoms by examining the characteristics of panic disorder and expand the field of mental illness research to social media.

• Journal of Information Management
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• v.41 no.2
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• pp.1-30
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• 2010

This study analyzed research output in dominant research areas of 19 national research institutions. Policy knowledge produced by the institutions during the past 5 years mainly concerned 10 policies dealing with economy and society issues. Similarities between the research subjects of the institutions were displayed by MDS mapping. The study also identified issue attention cycles of the 5 chosen policies and examined the correlation between the issue attention cycles and the yields of policy knowledge. The knowledge structure of each policy was mapped using co-word analysis and Ward's clustering. It was also found that the institutions performing research on similar subjects demonstrated citation preferences for each other.

• Journal of the Korean Society for information Management
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• v.36 no.4
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• pp.207-226
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• 2019

Depression is a serious psychological disease that is expected to afflict an increasing number of people. And studies on depression have been conducted in the context of social media because social media is a platform through which users often frankly express their emotions and often reveal their mental states. In this study, large amounts of Korean text were collected and analyzed to determine whether such data could be used to detect depression in users. This study analyzed data collected from Twitter users who had and did not have depressive tendencies between January 2016 and February 2019. The data for each user was separately analyzed before and after the appearance of depressive tendencies to see how their expression changed. In this study the data were analyzed through co-occurrence word analysis, topic modeling, and sentiment analysis. This study's automated data collection method enabled analyses of data collected over a relatively long period of time. Also it compared the textual characteristics of users with depressive tendencies to those without depressive tendencies.