• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.40 no.4
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• pp.58-66
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• 2022

Biophilic design is an approach that attempts to connect people with nature to promote health, recovery and restoration processes. The purpose of this study was to systematically analyze restorative factors and characteristics of Soswaewon by applying a biophilic design analysis framework. To this end, a biophilic design classification system was established and content analysis and frequency analysis were conducted for 『Soswaewon 48 Young』. the ratio of plant words including bamboo, pine, and plum was the highest, and words related to dynamic water flow or interaction with water as well as various water types such as streams, waterfalls, and ponds were also high. appeared in rank. In addition, multisensory factors, seasonal changes, microclimatic factors, emotional elements that allow people to indirectly experience nature were expressed in various ways. The space layout and circulation provide opportunities to appreciate and experience the rich sensory resources of Soswaewon. In conclusion, this study confirmed the healing and restorative value of Soswaewon from the perspective of biophilic design, and it needs further research on the restorative factors of traditional spaces.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.47-51
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• 2021

In this study, we try to find out and analyze the results of reduced self-esteem and loss using text mining. Physical health is important, of course, but these days, mental health is considered more important. In order for the mind to be healthy, it is important to have self-esteem and self-confidence first. Self-esteem decreases, and if lost, it directly leads to depression. If depression is severe, the worst will lead to self-harm and suicide. However, more and more people are committing suicide these days because both ordinary people and entertainers cannot overcome depression. For this reason, the seriousness of depression and loss of self-esteem are also considered important and become an issue. Therefore, we want to collect data for a certain period of time through Naver, Instagram, and Twitter searches and extract the words of the data to anticipate and analyze the cause of loss of self-esteem, how serious the recent depression is, and what the consequences of loss of self-esteem are.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.41-48
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• 2023

In this paper, we propose an analysis of recent trends in the NFT (Non-Fungible Token) industry using topic modeling techniques, focusing on their universal application across various industrial fields. For this study, patent data was utilized to understand industry trends. We collected data on 371 domestic and 454 international NFT-related patents registered in the patent information search service KIPRIS from 2017, when the first NFT standard was introduced, to October 2023. In the preprocessing stage, stopwords and lemmas were removed, and only noun words were extracted. For the analysis, the top 50 words by frequency were listed, and their corresponding TF-IDF values were examined to derive key keywords of the industry trends. Next, Using the LDA algorithm, we identified four major latent topics within the patent data, both domestically and internationally. We analyzed these topics and presented our findings on NFT industry trends, underpinned by real-world industry cases. While previous review presented trends from an academic perspective using paper data, this study is significant as it provides practical trend information based on data rooted in field practice. It is expected to be a useful reference for professionals in the NFT industry for understanding market conditions and generating new items.

• Korean Association of Arts Management
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• no.50
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• pp.5-31
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• 2019

The main purpose of this study was to use Deep Learning based Topic Modeling and Semantic Network Analysis to examine research trend of arts management-related papers in korea. For this purpose, research subjects such as 'The Journal of Cultural Policy', 'The Journal of Cultural Economics', 'The Journal of Culture Industry', 'The Journal of Arts Management', and 'The Journal of Human Content', which are the registered journal of the National Research Foundation of Korea directly or indirectly related to arts management field. From 1988 to 2017, a total of 2,110 domestic journals' signature, abstract, and keyword were analyzed. We tried Big Data analysis such as Topic Modeling and Semantic Network Analysis to examine changes in trends in arts management. The analysis program used open software R and standard statistical software SPSS. Based on the results of the analysis, the implications and limitations of the study and suggestions for future research were discussed. And the potential for development of convergent research such as Arts & Artificial Intelligence and Arts & Big Data.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.1
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• pp.113-126
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• 2017

This study aimed to quantitatively analyze the trends in environmental research that utilize environmental geospatial information through text mining, one of the big data analysis technologies. The analysis was conducted on a total of 869 papers published in the Republic of Korea, which were collected from the National Digital Science Library (NDSL). On the basis of the classification scheme, the keywords extracted from the papers were recategorized into 10 environmental fields including "general environment", "climate", "air quality", and 20 environmental geospatial information fields including "satellite image", "numerical map", and "disaster". With the recategorized keywords, their frequency levels and time series changes in the collected papers were analyzed, as well as the association rules between keywords. First, the results of frequency analysis showed that "general environment"(40.85%) and "satellite image"(24.87%) had the highest frequency levels among environmental fields and environmental geospatial information fields, respectively. Second, the results of the time series analysis on environmental fields showed that the share of "climate" between 1996 and 2000 was high, but since 2001, that of "general environment" has increased. In terms of environmental geospatial information fields, the demand for "satellite image" was highest throughout the period analyzed, and its utilization share has also gradually increased. Third, a total of 80 correlation rules were generated for environmental fields and environmental geospatial information fields. Among environmental fields, "general environment" generated the highest number of correlation rules (17) with environmental geospatial information fields such as "satellite image" and "digital map".

• The Journal of the Korea Contents Association
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• v.15 no.7
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• pp.578-590
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• 2015

This paper investigated how high school students perceive on nuclear power applying semantic network analysis. The total number of 250 high school students in 6 metropolitan areas responded to the survey. Results indicate that the word which most frequently appeared is 'Nuclear plant' (87 times) following by 'Japan' (71 times), 'Danger'(59 times), 'Fukushima' (59 times), 'Radioactivity' (56 times), 'Energy' (47 times), 'Youngkwang' (37 times), 'Electricity' (30times), 'Chernobyl' (29 times), 'Explosion' (25 times). For students in higher acceptance level of nuclear 'Radioactivity' (25 times) was most frequently showed up following by 'Fukushima' (23 times), 'Energy' (21 times), 'Japan' (21 times), Nuclear plant' (20 times), 'Danger' (17 times), 'Youngkwang' (16 times). For student in lower acceptance level of nuclear, the word of 'Nuclear plant' (40 times) most frequently appeared following by 'Japan' (31 times)', 'Danger' (29 times), 'Fukushima' (23 times), 'Radioactivity' (17 times), 'Energy' (16 times), 'Youngkwang' (16 times), 'Chernobyl' (15 times).

• The KIPS Transactions:PartB
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• v.15B no.5
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• pp.493-500
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• 2008

Although modern day people gather many data from the network, the users want only the information needed. Using this technology, the users can extract on the data that satisfy the query. As the previous studies use the single data in the document, frequency of the data for example, it cannot be considered as the effective data clustering method. What is needed is the effective clustering technology that can process the electronic network documents such as the e-mail or XML that contain the tags of various formats. This paper describes the study of extracting the information from the user query based on the multi-attributes. It proposes a method of extracting the data such as the sender, text type, time limit syntax in the text, and title from the e-mail and using such data for filtering. It also describes the experiment to verify that the multi-attribute based clustering method is more accurate than the existing clustering methods using only the word frequency.

• Journal of KIISE:Software and Applications
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• v.41 no.7
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• pp.492-500
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• 2014

This paper presents the performance enhancement of automatic bilingual lexicon extraction by using refinement of pivot-context vectors under the standard pivot-based approach, which is very effective method for less-resource language pairs. In this paper, we gradually improve the performance through two different refinements of pivot-context vectors: One is to filter out unhelpful elements of the pivot-context vectors and to revise the values of the vectors through bidirectional translation probabilities estimated by Anymalign and another one is to remove non-noun elements from the original vectors. In this paper, experiments have been conducted on two different language pairs that are bi-directional Korean-Spanish and Korean-French, respectively. The experimental results have demonstrated that our method for high-frequency words shows at least 48.5% at the top 1 and up to 88.5% at the top 20 and for the low-frequency words at least 43.3% at the top 1 and up to 48.9% at the top 20.

• Journal of Korean Library and Information Science Society
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• v.46 no.1
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• pp.321-344
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• 2015

This paper presents a fusion approach to sentiment detection that combines multiple sources of evidence to retrieve blogs that contain opinions on a specific topic. Our approach to finding opinionated blogs on topic consists of first applying traditional information retrieval methods to retrieve blogs on a given topic and then boosting the ranks of opinionated blogs based on the opinion scores computed by multiple sentiment detection methods. Our sentiment detection strategy, whose central idea is to rely on a variety of complementary evidences rather than trying to optimize the utilization of a single source of evidence, includes High Frequency module, which identifies opinions based on the frequency of opinion terms (i.e., terms that occur frequently in opinionated documents), Low Frequency module, which makes use of uncommon/rare terms (e.g., "sooo good") that express strong sentiments, IU Module, which leverages n-grams with IU (I and you) anchor terms (e.g., I believe, You will love), Wilson's lexicon module, which uses a collection-independent opinion lexicon constructed from Wilson's subjectivity terms, and Opinion Acronym module, which utilizes a small set of opinion acronyms (e.g., imho). The results of our study show that combining multiple sources of opinion evidence is an effective method for improving opinion detection performance.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.415-423
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• 2017

This study explored research trends by investigating articles published in the Journal of Korean Society of Fashion Design from 2001 through 2015. English key words and abstracts were analyzed using text mining and topic modeling techniques. The findings are as followings. By the text mining technique, 183 core terms, appeared more than 30 times, were derived from 7137 words used in total 338 articles' key words and abstracts. 'Fashion' and 'design' showed the highest frequency rate. After that, the well-received topic modeling technique, LDA, was applied to the collected data sets. Several distinct sub-research domains strongly tied with the previous fashion design field, except for topics such as fashion brand marketing and digital technology, were extracted. It was observed that there are the growing and declining trends in the research topics. Based on findings, implication, limitation, and future research questions were presented.